Construction Research Congress 2024: Health and Safety, Workforce, and Education

The current study is a first-ever evaluation of the system dynamics (SD)-based serious game for its pedagogical impact on teaching construction project management (CPM) concepts. An online game designed by the MIT Sloan School of Management is explored to identify three learning objectives consistent with the Project Management Body of Knowledge (PMBOK). A newly designed game session allowed the participants to control a project’s hiring, management, and quality pressures and learn through the simulated impact. The game’s effectiveness on learning outcomes was captured using pre- and post-game questionnaires implemented for 23 civil engineering undergraduate and graduate students in India. A statistically significant improvement of 44% in the post-game scores was obtained for the three learning objectives. About 83% of the participants agreed with the game’s effectiveness in delivering the CPM concepts. The participants’ feedback on sessions encourages further exploring the potential of SD’s use in CPM teachings.

Construction site orientation, a.k.a. induction or “on-boarding,” is an essential process for new or inexperienced construction employees to familiarize themselves with site logistics, safety precautions, and management teams. However, traditional site orientations are often time-consuming and need human resources who conduct these in person. To address these issues, this study aims to design and develop a digital replica of a large commercial construction project in the form of a virtual construction site that can be used to provide general site orientations to all construction workers. The necessary orientation components were identified and validated by an expert panel consisting of construction professionals, and a virtual site was developed that included the identified components and key locations. This study provides a potential solution to the issues associated with traditional construction site orientations and offers an engaging and efficient way to provide important information to newcomers of construction sites.

Industrial transformation is experienced through the adoption and utilization of technologies. However, this is slow in the construction industry due to a lethargic approach toward innovation, which adversely affects project performance and productivity. The current study evaluates existing literature on the integration of emerging technology (ET) in architecture, engineering, and construction (AEC) industry and education curricula (EC). Also, applied a scientometric analysis to identify technologies integrated into AEC EC courses and institutions from 2010 to 2023. A qualitative content analysis was adopted in this study to scrutinize the selected articles. The study found BIM, VR, and AR as the top reoccurring ET incorporated in AEC EC, but lack enough information on specific course integration. The research limitation is the use of only existing published literature. The study concludes by proposing a road map to help stakeholders assess technology integration into AEC EC, which could result in producing innovative and competitive professionals and likewise enhance AEC’s project performance and productivity.

Through semi-structured interviews with 20 women leaders, vice president (VP) and above, from top architecture, engineering, and construction (AEC) firms, researchers explored career path progression. This study sought to investigate root causes for women’s AEC underrepresentation by gleaning insights from the experiences, successes, and challenges during their climb up the corporate ladder. Understanding the richness of individual journeys, researchers selected narrative inquiry to explore underlying themes and generate a career road map for early and mid-career women. NVIVO software facilitated qualitative analysis of the resultant compiled corpus of information, identifying common topics and industry best practices to promote diversity. Sharing successful initiatives with firms throughout the industry expands the long-term focus from initial recruiting through long-term career nurturing for women. Study participants noted assembling a supportive personal network and identifying early career mentors as key success factors. Firms wishing to differentiate themselves and gain the proven benefits of diversity, highlighted by better business outcomes and greater innovation, should invest in diversity programs. Successful initiatives include creating a culture that nurtures women throughout their careers and tailoring more inclusive recruiting programs.

The integration of artificial intelligence (AI) has the potential to address various challenges in the construction industry, including cost overruns, safety concerns, labor shortages, and productivity issues. However, the construction industry has been slow to adopt AI solutions due to a lack of construction professionals with appropriate AI understanding. Currently, very few construction programs incorporate AI literacy into their undergraduate curricula. This study aims to address this gap by developing a 2-h curriculum that teaches construction students AI literacy knowledge within the context of construction safety. The effectiveness of the curriculum was assessed through pre- and post-intervention questionnaires, which showed that the curriculum effectively enhanced construction students’ AI literacy level. This research provides a valuable foundation for the future integration of AI literacy components into construction curricula, which is crucial for the construction industry to catch up with other industries in adopting AI solutions.

The COVID-19 pandemic led to the adoption of social distancing and the closing of public places. One of the public places affected was tertiary education settings where universities had to close. Without a physical school environment, some tertiary institutions resulted in e-learning. Therefore, this study examined e-learning adoption in construction education in a developing country during the COVID-19 pandemic. The study used a cross-sectional survey research design through an online questionnaire form. The study revealed the leading e-learning platforms used during this period. In conclusion, the study revealed the significant challenges and benefits of e-learning adoption in construction education during the COVID-19 pandemic. Beyond the COVID-19 pandemic, the study recommended more investment in e-learning infrastructure for both students and tutors.

The construction industry does not have a great reputation for its workplace compared to other industries. A tolerant workplace has positive impacts on employee productivity. While existing research has analyzed the cultural and social experiences of construction workers across gender lines, no research has analyzed such experiences based on age or role. The purpose of this paper is to compare the construction workforce’s attitudes to social and cultural issues across different age groups, and whether they have a supervisory role. In this study, we surveyed 2,740 construction workers across the US using an online questionnaire. The survey participants were asked to rate statements adopted from the General Social Survey on how they feel on a Likert scale ranging from “strongly disagree” to “strongly agree” regarding five specific issues. Key findings show that younger workers and non-supervisors have a statistically significant worse experience compared to their colleagues.

This study examines the perceived expertise of diverse construction professionals across gender and race in the construction industry. While expertise assessment is essential, contextual factors such as race and gender can influence the perceived expertise of individuals. An online study was conducted, employing an immersive storytelling tool in a between-subject experimental design with four different conditions. Construction-related discipline students were randomly assigned to experience an immersive story narrated by a female African American, female White American, male African American, or male White American construction expert. Results reveal no statistical differences, yet female professionals received lower expertise ratings based on gender and race. Furthermore, students provided negative feedback predominantly for female professionals. These findings underscore the importance of raising awareness among students regarding potential biases in evaluating diverse construction professionals.

With the increasing deployment of robots in the construction industry, it is crucial for future professionals to not only be familiar with these robots but also understand their applications and associated safety challenges. However, the logistics and financial issues in bringing these robots to classes pose a challenge in including an effective robot-related curriculum in construction education. This study developed a virtual site visit that integrates virtual reality (VR) technology and immersive storytelling to expose students to a robot-dominant construction site. The virtual site visit enabled users to navigate a robot-dominant construction site and learn about four-legged robots, their applications, safety challenges, and countermeasures for working safely with robots. The results of the study demonstrate that virtual site visit offers unique opportunities for enhancing construction students’ conceptual understanding of robots in diverse and unfamiliar scenarios without exposing them to undue risks or incurring substantial costs.

The engineering, architecture, and construction professions have lost talents to other industries because millennials and Generation Z tend to misapprehend the workplace expectations of inclusion and work-life balance (WLB). Construction careers are highly stressful and demanding, with pressures including meeting tight deadlines, aligning multiple stakeholders, and managing disputes. There is a disconnect between the perception/experience provided to students during their studies, which results in challenges, particularly the coordination between personal and professional life, commonly known as WLB. The goals of this study are to investigate (1) the organizations’ efforts, challenges, and concerns to recruit and retain new workforce through interviewing industry professionals, and (2) the role that WLB and other factors, including wage, job location, and benefits, play when considering a job offer in the construction industry through surveying civil engineering and construction management students. The study results indicated that WLB is a priority for the future workforce and that inadequate WLB could result in project delays, higher project costs, and a devastating effect on the employees’ mental and physical health. The findings of this study serve the construction industry and construction stakeholders by promoting awareness of the importance of WLB to recruit and retain new workforce.

Remote virtual training for tasks that involve intensive human motor participation is gaining popularity in the emerging education 4.0 era. However, the user experience and the underlying cognitive characteristics while facing the state-of-the-art training platform are less understood. This paper implements a robot-assisted virtual reality training system for welding training. The virtual reality system creates an immersive environment, and the robotic device provides the necessary physical interaction. A total of 28 participants who had no prior welding experience were recruited to learn welding skills. The participants were trained under the conventional training condition, visual guidance condition, and haptic guidance condition. Participants’ pupillary response and subjective feedback were evaluated to investigate the user experience and the cognitive characteristics’ differences while being trained under different conditions. The results showed that participants felt easier when learning with conventional learning method, while pupillary response showed learning with visual and haptic feedback reduced the cognitive load. By showing the complexity of evaluating user experience, this study encourages training designers to evaluate the use of new technology with more dimensions.

The development of teamwork skills in project management is becoming increasingly important as the future of work requires increasingly complex team organization and coordination. Professionals of highly technical backgrounds face challenges in working in collaborative environments involving communication, management, conflict resolution, and challenging behaviors. To help improve construction engineering students’ collaborative skill development in and via project team assignments, this paper presents an iterative development process of dynamic project team interventions. An intervention module was designed after a comprehensive literature review, which was later tested in a pilot study and revised as the final version. The module focuses on planning, management, coordination, conflict resolution, and communication aspects of project teamwork, and includes a dynamic charter, a training module, and reminders driven by teams’ use of the project charter. Insights into training and intervention modules for the future of work in higher education are discussed, and directions for future research are presented.

The construction industry has a severe workforce shortage compounded with historical diversity, equity, and inclusion challenges. The Associated General Contractors (AGC) of Washington developed a culture to commit, attract, retain, and empower (CARE) construction workers in a diverse, equitable, and inclusive environment. Shortly, the AGC of America adopted the culture of CARE and began actively working to ensure construction workers feel valued, respected, and heard. At the same time, psychological safety emerged in conversations that construction workers should feel safe and be able to speak up with ideas, questions, concerns, or mistakes. As a result, many construction companies took a pledge and sought training for construction workers. This paper aims to present a training development for diversity, equity, inclusion (DEI), and psychological safety in construction. The authors collected data from multiple construction companies that were leaders in developing resources for DEI training. The authors analyzed scenarios collected from real-world construction projects, synthesized them in case studies, and prepared manuals for trainees and trainers. The developed manuals are publicly available for corporate trainers and university educators to train construction workers and students. The training positively impacts the DEI-built environment and workforce development.

Traditional lectures have difficulties instilling pragmatic skills in construction engineering students due to the inability to illuminate the complexities within the human-robot collaborative construction environment. While on-site can acclimatize construction students to reality and construct knowledge that can solve safety challenges, it is challenging to organize on-site training trips owing to the dangerous nature of construction workplaces. This research aimed to explore virtual reality (VR) as a tool to enhance students’ perception and knowledge of construction robotic safety. For this purpose, the study developed a virtual training platform for providing construction engineering students with safety knowledge on interacting with simulated robots within the virtual environment of construction sites. A self-assessment approach was leveraged among 20 recruited students to demonstrate the efficacy of students’ engagement and learning outcomes from the proposed learning approach over the traditional learning approach. Results indicated a statistical difference in students’ learning outcomes and engagement levels between the developed approach and the traditional approach. Findings demonstrated the implications of VR as an experiential tool to enhance the students’ learning of robotic safety in construction.

The body of literature exploring how diversity, equity, and inclusion (DEI) percolates in technical environments is growing. These studies adopt various goals and methodologies. However, limited systematic analyses exist regarding the status, development, and trends of DEI research in the architecture-engineering-construction (AEC) industry. This paper presents a systematic literature review of recent DEI research trends in AEC by analyzing relevant publications. Both descriptive and content analyses were performed, considering the annual number of papers and the range of topics addressed. This review offers an enhanced understanding of the current state of DEI research, highlighting achievements, research gaps, and future research directions. This review is valuable for researchers aiming to address limitations and expand the scope of DEI research and for industry professionals seeking to implement DEI best practices.

Construction inspection ensures the delivery of quality transportation infrastructure projects by demonstrating that the work put in place meets or exceeds the contract requirements. Performing quality construction work helps state departments of transportation (DOTs) realize the engineered lifespan of a transportation asset, which provides the best value for DOTs and the traveling public. However, difficulties in finding, hiring, recruiting, and developing well-trained and experienced construction inspectors are a growing risk to the quality of transportation construction projects. With new inspectors joining the construction workforce, proper training is needed to impart the technical competencies (knowledge, skills, and abilities) to perform inspection responsibilities accurately and efficiently. For training to be successful, it needs to provide learning in specific technical competencies. However, limited research has been conducted to determine the technical competencies an inspector needs to perform their job duties successfully. Therefore, this study investigates the technical knowledge, skills, and abilities needed to perform inspection responsibilities. Two surveys and two focus groups were conducted to collect information on inspection responsibilities and technical competencies. The results reveal the specific technical competencies that the transportation construction inspection workforce requires, and this information can be used to develop more effective training for inspectors.

The Construction Innovation Centre (CIC) was established at the University of Alberta with the aim of providing breakthrough research, education, and training that would directly benefit the construction industry, leading to sustainable and economic development of our built environment—all while providing a competitive advantage for the Canadian construction sector. Bringing together over 30 inter-disciplinary faculty members and more than 50 industrial partners, professional associations, and government bodies, the CIC is accelerating and supporting innovation, productivity, and competitiveness in the construction industry through high-impact research. This paper shares the steps taken to establish the CIC including the development of its research road map, governing structure, services provided, and metrics to evaluate performance. By exploring the process of establishing the CIC as a collaborative construction research center, this paper provides insights into the opportunities, challenges, and directions for creating successful academia-industry collaborations. Such collaborations are crucial for developing impactful solutions that can address the challenges encountered by the construction industry through cultivating a collaborative environment for effective innovation and knowledge mobilization, successful dissemination of research findings, and productive training of highly qualified personnel.

The COVID-19 pandemic is one of the biggest challenges the world has faced in recent decades. The pandemic has disrupted human life in significant ways. Similar to many other industries, the construction industry has faced numerous challenges due to the pandemic. Some of the challenges include project shutdowns, supply chain disruptions, health and safety challenges, project delays, and economic hardships. This study is focused on unveiling workforce-related challenges the industry experienced during the early stages of the COVID-19 pandemic. The investigation leveraged YouTube as the source of information given the presence of rich and relevant content. As a first step, YouTube’s application programming interface (API) was used to extract relevant videos using appropriate keywords. The analysis of the videos revealed a number of workforce-related challenges that the pandemic posed. These included job losses, lower wages, financial stress, and fringe benefit losses. Additionally, the videos highlighted the challenges of migrant workers from different regions, including the US, Canada, Europe, Singapore, the Middle East, and others. The videos also unveiled the struggle of minority workers such as Hispanic workers in the US. The findings of this study can be utilized by industry stakeholders and governments to overcome workforce-related challenges during similar emergencies in the future.

To promote social mobility and job equity in low-income communities, this research demonstrates a successful pedagogy that educates, trains, and certifies lower-income and underserved individuals, including unemployed, veterans, women, individuals aging out of foster care, and the homeless to join the construction industry, thus filling the critical shortage of skilled craft workers in construction. The goals of this research are to (1) identify gaps in construction trade knowledge of low-income individuals, (2) evaluate if individuals found better job opportunities after completing a construction trades program (CTP), and (3) identify pedagogy including course delivery and instructional technologies to effectively educate and train particularly the low-income workforce on basic construction knowledge and offer them the needed skills to find better job opportunities in this well-paid industry. To achieve these goals, this research surveyed 65 students, alumni, and faculty of a CTP that serves low-income and underserved communities. The results of this study showed that students of a CTP obtained better job opportunities and highlighted the importance of training low-income individuals in construction trades for social mobility. This study provides strategies for low-income communities to join the construction workforce, thus leading to a diverse workforce and filling in the construction workforce gap.

Educating construction management (CM) students to embrace infrastructure sustainability and utilize alternative project delivery methods (PDM) can facilitate addressing issues including project funding, stakeholder conflicts, as well as social, economic, and environmental implications. Therefore, this study introduces the CM students to how the Envision sustainability rating system can support the implementation of alternative PDMs in infrastructure projects through leveraging integrated design. Furthermore, the study reflects the participants’ experience and perspective on using alternative PDMs to improve the sustainability performance of infrastructure projects. This study piloted a workshop along with pre- and post-survey in a sustainable construction class under the CM program. The findings indicated that the workshop improved the students’ competencies in delivering high-performance sustainable infrastructure (SI) projects. Moreover, the students shared positive feedback for incorporating alternative PDMs in SI projects. This study would be critical for implementing sustainable developments and enhancing the project management skills of the future construction workforce.

Rural water utilities often struggle to retain a reliable, skilled workforce. In the Yukon-Kuskokwim (YK) Delta of Alaska, this challenge is exacerbated by communities’ remoteness, small populations, and limited economies. Workforce attrition leaves utilities understaffed and unable to provide sufficient water services to their community. In this study, we evaluate interviews with seven YK Delta water sector professionals, including environmental health officers, water plant operators, and water haulers. Through a hybrid qualitative content analysis and cognitive mapping, we seek to better understand the relationships between workforce attrition and interdependent system factors. For instance, we identify that overworking can lead to attrition, which can lead to service disruptions. Preliminary results indicate that a major concern for water sector professionals is the excessive hours of work, which leads to worker burnout, attrition, and absenteeism. We further identify that increased wages for water sector workers can improve retention and recruitment, likely leading to long-term improvements in water provision.

The importance of international collaborations and experiences is increasingly evident in the professional development of engineering graduates, preparing them for the global and interconnected nature of their disciplines. This study examines the role of international research experiences in enhancing the professional and intercultural development of engineering graduate students, focusing on participants in the International Research Experiences in Civil, Construction, and Environmental Engineering (IRECCEE) program. Using a mixed-methods approach, data collected from surveys and interviews are analyzed to assess the impact of the program on students’ communication, networking, collaboration, independent research abilities, and intercultural competence. Preliminary findings indicate significant improvements in these competencies, highlighting the IRECCEE program as an effective platform for preparing students for successful academic and professional careers in a global context. The study further demonstrates the positive influence of international exposure on students’ capacity to navigate diverse cultural and research environments. These insights underscore the importance of international research experiences in graduate education and provide guidance to improve the design and evaluation of such programs.

The construction sector acknowledges the need of teamwork for project success, which necessitates good collaboration among individuals working towards common goals. Social cognitive theory (CST) also concedes the importance of understanding the impact of individual personalities on a group’s behavior as it affects team dynamics and project performance. However, there is shortage of research on overall team personality in the construction sector. This study fills this gap by developing and evaluating an overall construction project team personality from the weighted averages of individual team member personality traits. The weights were developed using a machine learning model, trained, and tested on data from 38 project teams spanning the past five years. These teams included important stakeholders such as owners, architects, engineers, contractors, and subcontractors. The findings highlight overall team personality provides a comprehensive summary of team characteristics, while presenting the group’s motivation, interaction, and performance, ultimately leading to successful project outcomes.

Humanitarian engineering (HE) programs are growing exponentially to meet the rising demand of engineering and construction students wanting to address equitable infrastructure service provision. This field is attracting students traditionally underrepresented in engineering and construction, including those of diverse backgrounds, identities, and passions. However, there is a dearth of research tracking students’ evolving career aspirations through the influences of graduate school. This study conducts longitudinal interviews with seven students enrolled in seven graduate HE programs over two years. Through these interviews, students’ career aspirations were collected and qualitatively coded, including desires around impact, daily tasks, work environment, location, and compensation. The study identified three expectation patterns of shifts in career interests over a graduate education. This ongoing analysis will address the shortage of research on the career pathways of socially minded engineering and construction students. Results will be valuable for programs to support and meet the changing needs and aspirations in these fields.

This study explores whether priming universalism values in construction project owners for whom these values are central can increase the perceived importance of the inclusion of underrepresented firms when selecting a general contractor. This hypothesis was preliminarily tested in a sample of civil engineering and architecture students (n = 42), which was divided into two groups. Those in the experimental condition received the universalism prime whereas those in the control condition did not. Then, both groups completed a general contractor selection task, basing their choices on typical decision factors, including the utilization of small and women- and minority-owned (SWaM) firms in the project proposals. While results did not provide support for the initial hypothesis, subsequent analyses showed that the prime significantly affected how conformity, tradition, and self-direction values related to the importance that participants assigned to the inclusion of SWaM firms in pre-qualification judgements. These findings suggest a strong influence coming from social norms in the workplace that future research should address.

The construction industry has been male-dominated for a long time and has struggled with diversity and inclusion in the workforce, especially for construction equipment operators with only 2.7% of women. Meanwhile, the construction industry is also currently facing the emergence of new technologies such as construction robots. This trend may offer new opportunities for increasing diversity and inclusion in the workforce, but it also poses the risk of exacerbating existing inequities without a well-designed transformation process of hiring and performance evaluation. This study aims to examine the current state of hiring and performance evaluation practices for equipment operators and identify the barriers and challenges to creating a more diverse and inclusive workforce, especially in the context of the incorporation of construction robots. Specifically, through content analysis of job posters and a series of interviews with industry professionals, this study identifies the reasons that prevent women from entering this occupation, the existing barriers, and the potential impacts of emerging technology adoptions on workforce diversity. The findings of this study provide valuable insights for the construction industry to build a more diverse and inclusive workforce in the technology transformation process.

Mathematics is fundamental to engineering and deserves careful attention from academics and researchers to provide strategies for improving the knowledge and skills of future practitioners. Although strategies to improve mathematics education have been widely published, there are still some problems in Latin American contexts. A systematic review of 54 articles on pedagogical experiences, published between 2000 and 2020, compared European, North American, and Latin American studies. The results show that European and North American studies are based on explicit theoretical approaches, while many Latin American studies do not explicitly have a theoretical framework. European studies are based on cognitivism and North American studies on cognitive constructivism, but Latin American studies do not refer to any approaches in particular. Trends in the pedagogical knowledge about mathematics education in engineering differ between countries. Therefore, engineering education research in Latin American contexts must provide more insights to improve mathematics education in engineering based on explicit theoretical frames.

The architectural, engineering, and construction industry is undergoing an active transition towards sustainability practices. It is important to feature sustainability in current decision-making curriculum. This paper introduces a qualitative case study on course redesign, which takes a pedagogical planning process known as “students-as-partners (SaP)” through action research. The redesign has two overarching goals: (1) introducing knowledge and skills required for aligning decision-making with Sustainable Development Goals, within a module of a civil engineering decision-making course, and (2) developing working partnerships that bring together varied perspectives on how to best integrate the contents. A team containing an instructor and two students worked on the project, supported by a curriculum developer and three faculty peer mentors. A new module, including active learning activities (e.g., experimental learning), formative and summative learning assessments, and a teaching evaluation, was created. A semi-structured interview was performed to measure the impacts of the redesign project. The results show that the purposes of SaP are met. The students reported increased engagement and motivation for learning, and instructor is enabled to develop laser-focused contents. Takeaways were summarized as enablers for common SaP challenges. Limitations were identified, such as the selection of student participants and challenges on logistic communications.

While incorporating novel technologies aims to facilitate the inclusion of the future construction industry, the empirical investigation of diverse workers’ performance is necessary to better understand the strategies to broaden their participation. The literature has rarely focused on inclusivity in terms of involving workers with various disabilities in the construction sector. Thus, this study aims to pioneer the discussion on inclusivity in construction by focusing on workers with attention-deficit/hyperactivity disorder (ADHD). A future bricklaying worker-AI collaborative task was developed to examine workers’ attentional allocation, situational awareness (SA), and productivity in future works. The results indicated that ADHD workers exhibited a lower SA of dynamic objects by focusing on the primary task with no increase in productivity, leading to lower overall performance. This study provided insights into (1) understanding ADHD workers’ performance on human-AI teaming of future jobsites, and (2) proposing strategies to facilitate inclusivity in the future construction industry.

Equitable access to communities of practice for future workforce development has been challenging for construction education instructors. This has been identified as one of the triggers of deficiencies and disparities in the skills and competence of new construction engineering graduates and consequently, dissatisfaction of employers. To address this challenge, a web-based collaborative platform was designed and developed to integrate both communities. This study presents a usability evaluation of the web-based platform from instructors’ perspective using quantitative and qualitative analyses. The results from semi-structured interview, ratings of system usability scale, and trust scale were used to infer users’ acceptance of the platform. The results reveal high acceptance of the platform by end users as a tool to connect with practitioners for workforce development collaborations. The results also show required improvements to enhance users’ experience. The study provides a guide for the usability evaluation of similar matching platforms.

As the number of distance students increases, they are facing challenges such as isolation, limited access to resources, and the need for hands-on experiences. Metaverse-based education can be a solution to broaden accessibility to educational content. Therefore, this study provides a review of metaverse technologies and their potential to broaden accessibility in construction and civil engineering education. We explore the opportunities of utilizing advanced technologies, such as IoT, AI, and blockchain, as supporting tools for an educational Metaverse to enhance effectiveness and efficiency in the learning environment. The potential of Metaverse-based education is also discussed to provide a more inclusive and accessible educational experience, leading to better learning outcomes and improved safety practices in the construction industry. This study concludes by highlighting the need for further research to explore the full potential of these technologies in the Metaverse context and to develop effective educational strategies and frameworks that can leverage their capabilities.

The construction industry demands a new generation of professionals with the expertise to solve complex problems and manage new technological conditions. An approach that satisfies this need comprises integrating Industry 4.0 knowledge, building information modeling (BIM), and sustainability principles in construction education due to their collective potential to address the evolving demands and challenges of the construction industry. Each component brings benefits contributing to more efficient, innovative, and environmentally responsible construction practices and improving interdisciplinary project planning and control in collaborative environments. As of 2021, undergraduate construction management courses at Universidad Industrial de Santander (Bucaramanga, Colombia) include capstone design projects implementing BIM and sustainability. The capstone design projects require students to collaborate in person and remotely and develop autonomous learning skills to adapt to current technological tools. Through those projects, the authors identified the challenges and possibilities of proposing an academic training strategy to meet the needs of the construction industry. This study discusses the challenges, advantages, and disadvantages of including BIM and sustainability requirements in construction education and academic formation. Furthermore, other academic programs in architecture, engineering, and construction (AEC) disciplines seeking to implement improvement actions in their teaching-learning scenarios can reference these findings.

Innovation and technologies play a vital role in meeting the evolving demands of the construction industry. However, the construction industry still needs to increase technology adoption. One of the main barriers that have been discussed is the industry’s readiness to adopt new technologies. To prepare the next generation of professionals, construction education needs to lead the trend by understanding current construction industry challenges and teaching how to develop technological development solutions to improve the constructability of projects. This study applies Kolb’s experiential learning model to engage construction management students in hands-on experience and reflections related to technology adoption theories with industry professionals. In addition, this study aims to engage students to gain insight into their skills, interests, and values in technology adoption. The results contribute to identifying the areas that need realignments to the construction management curriculum and provide a pedagogical framework for construction technology adoption for academia and practice.

Almost four decades ago, federal and local governments began incorporating Small and Disadvantaged Business Enterprise (DBE) programs into their construction projects. However, the private sector in the US construction industry does not have similar requirements, and the benefits and challenges of these programs are not well understood. This exploratory study aims to identify the challenges of supplier diversity and inclusion programs in the US construction industry. Based on current literature and Twitter analysis, the study found that most DBE construction-related companies lack financial power, resources, and experience. Some may find it difficult to enter already-established relationships between prime contractors and existing DBEs. In addition, contract requirements and project delivery types can be restrictive for them, and the scope of work can be either felt extensive or limited based on their resources. This study also provides future research directions for successfully implementing supplier diversity and inclusion programs in the US.

North Carolina A&T State University (NCAT) is the largest historically black university (HBCU) in the United States. CM 224 Surveying Lab is a one-credit laboratory course for sophomores in architectural engineering, civil engineering, construction management, and geomatics. Laboratory sessions of distance measurement, differential and profile leveling, and traverse and topographic surveying using surveying instruments of auto level and total station. The authors were awarded a Scholarship of Teaching and Learning project to use the university-provided iPad and free apps of surveying instrument simulators to enhance students’ pre-class preparation and learning. This paper presents the pre-lab and post-lab survey findings from the pilot study of Spring 2023 with 19 students enrolled in CM224. The survey results showed that the developed course materials have the potential to help the underrepresented minority students to understand the surveying instruments of auto level and total station, and the field procedures of differential leveling, traverse surveying, and topographic surveying.

Artificial intelligence (AI) and simulated virtual models (SVMs) have influenced construction practices by making activities safer, error-free, efficient, cost-effective, high quality, connected, and transparent. With significant advancements, it is anticipated that new courses will be needed to familiarize students with applicable AI and SVMs. A course to introduce AI and SVMs was offered at a state university, but the students requested a more visual and hands-on approach to establish connections between real-world engineering challenges and AI. A ride-on excavator toy was converted into a robot replicating real-world excavator operations of pick-up dirt and dumping at a specific location. In addition, a 3D simulation SVMs was developed for enhancing teaching learning. The use of the robot as a hands-on tool created significant interest in civil engineering and computer engineering students. The learning experience was captured through a structured questionnaire survey, and the analysis shows higher levels of student interest in learning AI applications for construction.

About 90% of demolition waste is recyclable and reusable based on its inert nature; however, recycling and reuse of those materials are limited. One major barrier is the lack of demolition engineers’ knowledge about sustainable demolition practices. Material separation for recovery at demolition sites is much easier and more effective but more time-consuming than separation for recovery at material recovery facilities. As such, careful demolition planning by construction/demolition engineers is critical; however, most of the US construction engineering curricula do not cover demolition operations, thus lacking learning opportunities about sustainable demolition practices. In an effort to develop the next generation of demolition engineers with the sustainability mindset, we designed small-scale demolition simulation activities using 1:14 scale demolition vehicles where students will be exposed to sustainability challenges (e.g., on-site separation vs. off-site separation) and learned their critical role as construction/demolition engineers. To mimic real-world demolition sites, different structural waste was used, and students would be tasked with operating excavators and trucks in separating and moving different types of materials for recovery within set time constraints. Through the hands-on experience, students could find potential challenges with sustainable operations and develop critical thinking skills to devise solutions and strategies to tackle them.

The construction industry is considered one of the world’s most demanding, labor-intensive work sectors. Mental workload states of construction workers are known to affect their performance and safety. Previous studies examined construction workers’ perceptions and impacts of mental workload on safety and hazard identification. However, there is a need to investigate whether specific task characteristics impact mental workload, and the corresponding impact of the level of mental workload on worker safety when performing tasks. This study is being conducted to evaluate the impacts of task characteristics on mental workload and safety. The study includes an experiment using self-evaluation, galvanic skin response sensors, and observations in a controlled environment to determine participant mental workload and safety impacts. The targeted subject population consists of adults who can perform the study trials. The study findings reveal that task performance differs when subjects are in a hazard-free room compared to when they are located on an elevated work platform, and performance is impacted by increased task complexity. The results can be used to assess the mental workload of workers to improve safety. The study contributes an understanding of how task characteristics impact mental workload experienced by workers and their safety while performing the work.

Construction workers face many occupational hazards, and personal protective equipment (PPE) is essential in protecting them. Unfortunately, numerous studies have shown that a significant percentage of injured workers did not use or adequately use the required PPE during incidents. The aim of the study is to examine the human and workplace factors contributing to PPE non-compliance to manage these factors more efficiently. A survey was conducted between February and April 2022 to collect opinions from construction professionals. A total of 184 valid responses were obtained. The study found that workers’ attitudes and capabilities were among the most frequently cited human factors, while a lack of leadership and safety training were among the most frequently cited workplace factors. This study emphasizes the importance of addressing these factors to improve PPE utilization and reduce fatal and non-fatal injuries among construction workers, making it an important aspect of construction safety management. Finally, the findings can be utilized to develop an investigation learning program that identifies the underlying reasons behind PPE noncompliance.

A well-designed evacuation plan is essential for protecting human lives during emergencies. Traditionally, architects have relied on building codes to guide the design of the means of egress, including the layout of exits and maximum travel distance, to ensure the safety of building occupants. However, advancements in technologies such as building information modeling (BIM), computer simulation and modeling, and the Internet of Things (IoT) have enabled more effective and improved design, planning, and guidance for emergency evacuations. It is also important for constructors to consider the safety of workers during the construction phase in emergency situations. This paper aims to examine the current state-of-the-art research on design and planning for evacuation during the design, construction, and operation phases. By adopting a systematic approach, the paper summarizes the various approaches and technologies involved in building and construction safety design for emergency evacuations, identifies gaps, and highlights areas for future research.

Work zones are vital for improving infrastructural utilities yet pose significant dangers for workers and drivers who are often unfamiliar with these areas. Prior research examined various aspects of work zone accident causation; however, the interrelationships and critical combinations among the contributing factors have not been fully analyzed. This study aims to identify the underlying factors contributing to work zone accidents and evaluate the different combinations among them. To this purpose, accident factors were derived from a total of 133 research articles addressing work zone safety. Social network analysis (SNA) and clustering were used to comprehensively model the identified factors. Additionally, association rule analysis (ARA) was implemented to determine the critical associations among the identified factors. Results revealed that work zone design parameters are the most analyzed in the literature, while temporal considerations, regulatory factors, and driver characteristics have been relatively understudied. Moreover, the analysis shows that several critical interdependent relationships among these factors, such as the combined influence of inclement weather and driver behavior on navigating work zones, have been comparatively understudied in the literature. Ultimately, this study contributes to the body of knowledge by providing a comprehensive understanding of work zone accident causation.

The construction industry has long struggled with safety performance, with many concerns surrounding the industry’s ability to maintain high levels of safety. One effective approach to improve safety performance is through the use of construction safety culture and climate frameworks. However, the construction industry has faced several challenges in effectively utilizing these frameworks, including confusion due to the availability of multiple frameworks and the interchangeable use of definitions. To address these challenges, a distinct and practical framework for construction safety culture and climate has recently been proposed. The present study aimed to evaluate the perceived usability of this framework. To achieve this goal, an online survey was designed and administered, resulting in 77 valid responses. The analysis of the survey responses revealed that the participants generally perceived the framework’s concepts positively. However, some aspects of the framework require improvement to enhance its usability further. The study’s findings highlight the importance of adopting effective safety culture and climate frameworks in the construction industry and the need for ongoing evaluation and improvement to ensure their relevance and efficacy. As such, this research can contribute to improving the overall safety performance in the construction industry.

In most construction sites in Nigeria, compliance with wearing personal protective equipment (PPE) is poor. Some construction workers have alluded to the degree of discomfort experienced while wearing PPE on construction sites. Therefore, this study aimed to develop a web-based wear-fit system to involve construction workers in the process of PPE selection. The study used a cross-sectional survey research design, which utilized an electronic questionnaire instrument in this survey. A total of 80 construction workers participated in this study. They were selected using a quota-purposive sampling technique. The data obtained were analyzed using frequencies, percentages, mean score tests, and analysis of variance (ANOVA) tests. The study revealed that construction workers experienced the most comfort in wearing reflective jackets, lab coats, and coverall or aprons. In contrast, the slightest comfort was experienced using hand gloves, nose masks, and respirators. The study developed a web-based wear-fit system for construction workers’ PPE to aid comfort and compliance in wearing PPE on construction sites. The study’s contribution is mainly clamoring for the involvement of workers in the PPE selection process using electronic platforms such as the web-based wear-fit system.

Construction machine operators are at risk of drowsy driving, which can lead to accidents and injuries on jobsites. Jobsite injuries are a major concern in the construction industry, with operators of heavy machinery being particularly vulnerable. According to the US Bureau of Labor Statistics, in 2019, the fatal injury rate for workers in the construction industry was higher than the national average for all workers, and many of these fatal injuries were caused by heavy equipment. To address this issue, a method using computer vision and edge devices has been proposed to detect drowsy construction machine drivers using convolutional neural networks (CNNs). A dataset of images showing signs of drowsiness was constructed and used to train the CNN. After training, the neural network can score the testing images and predict if the subject in the image is drowsy or non-drowsy. This technology has the potential to greatly improve safety in the construction industry by alerting drowsy operators before an accident occurs, thus reducing the risk of accidents and injuries on jobsites. The implementation of this technology can help in reducing jobsite injuries and improve the overall safety of construction sites.

Roadway work zones often present hazardous conditions for drivers, pedestrians, and construction workers. Understanding the factors contributing to work zone crashes and their severity can assist the departments of transportation (DOTs) in safety management and planning. Accordingly, this study investigated the effect of freeway work zones on the severity of crashes by employing machine learning algorithms. The model was developed using crash data and construction zone information from Utah between 2017 and 2021. This study compares the performance of probabilistic and non-probabilistic machine learning models in predicting work zone crash severity using the KABCO severity scale. All three models achieved promising accuracy levels, with Extreme Gradient Boosting (XGB) achieving the highest accuracy at 86%, followed by Gaussian Naïve Bayes (GNB) with an accuracy of 76%, and complement Naïve Bayes (CNB) with an accuracy of 74%. The findings of this study offer valuable insights for the safety management plans of DOTs.

Fall accidents remain the leading cause of fatalities in the United States. Training programs are often used to mitigate fall accidents, but they lack effectiveness in terms of engagement experienced by trainees. This pilot study developed an immersive storytelling safety training approach to increase trainee engagement during safety training focusing on residential industry fall hazards. A between-subject experimental design was used to comparatively evaluate trainee engagement across two conditions: (1) immersive storytelling, and (2) immersive non-storytelling. Trainee behavioral, cognitive, and emotional engagement was measured using eye-tracking data and surveys. Twenty students from construction-related programs participated in this pilot study. While the results suggest some portions of the training that can benefit from the use of the storytelling technique, no significant statistical differences were observed across behavioral, cognitive, or emotional engagement. This study highlights how immersive storytelling safety training can be used as an alternative method for fall hazard safety training.

The existing literature emphasizes developing safety recommendations specific to small, medium, and large construction projects’ unique needs. Research has also shown that enhanced complexities can negatively affect the safety performance of construction projects. However, whether projects of different sizes perceive complexities differently has not been examined empirically in the existing literature. To address the gap, the current study examines how differently sized construction projects perceive the technical, organizational, and environmental complexities while managing safety. An existing survey was adapted, and 180 responses were obtained from practitioners across small, medium, and large construction projects. The findings reveal that most complexities are perceived as critical throughout the industry, and the perception of technical, organizational, and environmental complexities differs within projects of different sizes. The study’s findings significantly advance understanding of the safety-critical complexities confronting projects of varying sizes and aid in developing safety improvement recommendations tailored to their specific needs.

In the construction industry, clear communication is vital for optimal teamwork and performance. Yet, diverse languages, accents, and continuous worksite noises often impede speech clarity, leading to misunderstandings and consequential errors. Addressing this challenge, the presented research introduces a speech emotion recognition algorithm tailored for construction. Unlike prior algorithms emphasizing language content, this approach centers on speech features, bridging a significant gap in construction-specific applications. The algorithm aims to identify four key emotions: anger, happiness, sadness, and neutrality, using six distinct acoustic features: pitch, intensity, frequency formants, jitter, shimmer, and zero crossing rate. Leveraging these features collectively enhances system accuracy. Designed in MATLAB, the decision-tree-based method calculates confidentiality intervals for each feature and is specifically developed for speaker-dependent emotion recognition. Remarkably, the algorithm secured an 86% accuracy rate in detecting anger, outperforming existing models. By recognizing and addressing the emotional states of workers, this method holds the promise to greatly enhance safety on construction sites, averting potential hazards.

Construction workplaces often face unforeseen struck-by equipment hazards, leading to severe injuries and fatalities for workers. Detecting and localizing equipment sounds using multi-channel audio data has drawn interest in research. However, collecting such data for developing sound detection and localization machine learning models is challenging. Physical recordings on site required for deep learning are often infeasible due to the lack of proper sound attribute labels from heterogeneous construction sounds. This paper introduces a novel method for synthesizing overlapping and non-overlapping sound datasets in a three-dimensional space, utilizing Pyroomacoustics. The approach uses single sound data with attributes like start time, end time, azimuth, and elevation as microphone input to generate multi-channel audio output. The study successfully simulates 5,025 distinct scenario audios for both datasets, utilizing seven single-sound audiotapes. The generated large dataset can train neural network models capable of localizing equipment collision hazards in construction sites.

Construction activities depend highly on many conditions, such as human factors, weather, and materials. These conditions are dynamic and may involve many unexpected changes that impact current or subsequent activities. Unexpected changes that occur at the last minute when there is little time to respond to the change and pressure to complete the work may lead to safety issues. However, there is no clear definition for a last-minute change and little information on how last-minute changes impact safety performance. This research presents a definition of last-minute change and utilizes an incident review rubric based on experts’ recommendations and literature reviews to assess the relationship between last-minute changes and safety. The researchers analyzed 179 fatality incidents reported by the National Institute for Occupational Safety and Health (NIOSH) and associated states as part of the Fatality Assessment and Control Evaluation (FACE) program. The analysis verified that many fatality incidents (29%) are related to last-minute changes. Moreover, the incidents can be categorized according to energy type and change characteristics to help understand the impact of last-minute changes on construction safety. The research results contribute knowledge to help construction teams understand the safety impacts of last-minute changes and prevent incidents caused by last-minute changes.

Extreme weather events related to climate change present injury risks to construction workers. The increased exposure to these extreme conditions leads to heightened physical and mental health challenges among these workers. This study aims to investigate the impact of extreme weather events on construction worker safety, utilizing historical accident data from Occupational Safety and Health Administration (OSHA) and extreme weather event data from National Weather Service (NWS) from 2015 to 2021. By employing a matched-pair analysis, the research explores the intricate relationship between extreme weather and construction worker injuries. The results show that extreme heat, flood, hail, and wildfire increase the risk of construction accidents. Under extreme weather scenarios, fall accidents and heat-related illnesses are more likely. Findings will help decision-makers understand the challenges faced by construction workers under extreme weather. Identifying the most dangerous weather event for construction workers can aid in task allocation and safety planning.

Comparing to other industries, the construction sector has the most annual worker fatalities. One of the most dangerous jobs includes high-rise construction workers. There are many novel technological solutions to prevent and detect fall hazards; however, there is a lack of verifying which kinds of these technologies solutions has been used in the construction site. For this analysis, the first step consists of using the most recent literature review to assess a list of technology devices for fall protection. The second step consists of the development and distribution of a questionnaire that aims to assess the frequency of safety device usage and evaluate their effectiveness and usability through rating scales. The main findings indicate that the selected technological devices to mitigate fall hazards have not gained widespread adoption among general contractors. One of the primary factors contributing to this is the limited awareness and understanding of these devices.

Carpal tunnel syndrome (CTS) is a repetitive-motion injury that occurs when the median nerve is regularly compressed or squeezed. CTS causes pain, numbness, tingling, and weakness in the hand and wrist, which can affect workers’ ability to safely perform their job tasks. The National Institute for Occupational Safety and Health (NIOSH) recognizes that construction workers are at high risk for developing CTS due to the repetitive motions, awkward working posture, and forceful exertions required in many tasks. Specifically, overhead working postures and sustained postures with the wrist bent are the major causal factors of CTS among construction workers. However, to date, there has been little discussion about assessing the risk of CTS among construction workers. To this end, this study explores an approach to detect activities exposing workers to the risk of developing CTS by assessing workers’ hand movements. The inertial measurement unit sensors were attached to a participant’s wrist. The convolutional neural network-based approach was adopted to classify workers’ postures and activities. The result validates the feasibility of assessing the development of construction workers’ CTS and provides a foundation for the implementation of ergonomic interventions to reduce the risk of CTS.

Falls remain the leading cause of occupational fatalities and accidents in the United States construction industry. Currently, the incorporation of building information modelling (BIM) into construction practices has proven to be a valuable tool for eliminating general hazards on construction sites. This study investigated how the application of BIM technology has successfully contributed towards the mitigation of fall hazards at the construction and renovation phases of a construction project. Furthermore, the study explored how personalized monitoring has improved workplace safety while performing a task on the construction site. The study was conducted through a systematic review of past studies on the different general applications of BIM technology to construction safety engineering and design. The findings of this study highlighted the significant contribution of BIM application towards fall prevention measures during the construction and renovation phases. This study showed the tremendous potentials of BIM and developed an approach to integrate BIM with other technologies for real-time detection and prevention of fall hazards in the construction industry.

Increasing suicides among construction workers has put the spotlight firmly on understanding how the industry is contributing to the poor mental health of its employees. Studies have identified several work-related attributes that negatively impact the mental health of workers. However, much remains unknown on which of these work-related stressors are perceived by Hispanic workers as most relevant or impactful. Using self-report survey, the authors analyzed the responses of 91 Hispanic construction workers who were asked to rank their perceived importance of stressors commonly associated with the construction industry. The results were analyzed across Hispanic workers employed to work primarily in the field vs. those employed to work in desk jobs within the construction industry. The findings presented provide foundational knowledge that would support the determination, testing, and creation of personalized interventions that organizations would need to consider improving the mental health of employees from different socio-economic backgrounds.

Most cities, counties, and states in the US adopt popular modal building codes such as the International Residential Code (IRC) and the International Building Code (IBC) for their jurisdictions with or without amendments. These codes provide a comprehensive set of requirements for residential and commercial construction. Inspectors from city, county, and state use these codes to ensure that the buildings being constructed in their jurisdictions meet the minimum standard to protect health, safety, and welfare of the occupants. If the construction is not up to the code, building inspection will fail, and the issues need to be fixed before continuing the construction. Such inspection failures can delay the project, increase the construction cost, and negatively impact the reputation of the builders. Such inspection failures can be reduced by identifying and understanding the most common building code violations from past projects. This study identifies the five to 12 most common building code violations for each of the five categories of inspections: (1) framing, (2) plumbing, (3) electrical, (4) gas, and (5) mechanical. The findings can be shared with builders and homeowners to educate them about the common code violations, which can reduce the number of building code violations in the future.

Beyond the extreme human cost, serious injuries and fatalities (SIFs) also negatively impact the business performance of construction firms. The mechanisms operate through tangible factors (e.g., downtime, reduced productivity) and intangible factors (e.g., lower employee morale, loss of company reputation). To explore the significance and interactions of the factors influenced by SIFs, 15 safety professionals representing various industry sectors spanning from construction to technology, consulting, and insurance firms were surveyed. Specifically, they rated the extent to which each of 13 business factors impacted by SIFs in turn adversely influences one another. A network analysis depicts the relationship between these factors and identifies the most influential ones. Preliminary findings suggest that both tangible and intangible factors influence business performance, with intangible factors perceived as more salient after a SIF occurs. Company reputation and managerial bandwidth are the most prominent factors, while third-party prequalification, lawsuit probability, and productivity were ranked as less relevant. In contrast to contractors, owners perceive workers’ mental health as a more important factor influencing the business bottom line when a SIF occurs. This study adds to the literature on how safety performance impacts firms’ profitability by exploring the influence of each factor and their interactions with one another.

Advancements in wearable sensor technology and AI provide an excellent opportunity to monitor the health status of construction workers on-site. However, there is still a lack of efficient means of promptly communicating this information to workers without violating their privacy. This study evaluates the feasibility of providing personal feedback to construction workers regarding their health status while carrying out routine tasks. The proposed mechanism employs machine learning models and a decision tree to provide workers with timely private feedback and corresponding recommendations or risk mitigation strategies. As such, an experiment was conducted to evaluate the performance of the proposed feedback system as well as users’ perception of its usability. The findings revealed that the proposed feedback system could provide the workers with accurate and effective feedback regarding their health status, indicating the great potential to enhance worker safety and well-being at construction jobsites.

Construction workers may be at an elevated risk of contracting infectious diseases due to unsanitary conditions and constant exposure to health hazards on jobsites. Poor sanitation is one of the major causes of the spreading of infectious diseases. Construction workers with poor or limited sanitary conditions may struggle with safe drinking water, hand washing, access to clean toilets, and properly handled and serviced food. Some examples of infectious diseases include the common cold, flu, tuberculosis, pneumonia, and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). This paper develops several training scenarios on infectious diseases to keep construction workers safe and healthy in the construction industry. The authors collected documents and scenarios on four topics: potential exposures to infectious diseases, common causes and types of infectious diseases, precaution and prevention strategies, and control and protection plans. The authors analyzed scenarios collected from real-world construction projects and synthesized them in training manuals. The developed manuals are publicly available for corporate trainers and university educators to train construction workers and students. The research outcomes provide preliminary guidance and effective practices for training workers and preventing infectious diseases on construction jobsites.

Lean construction and construction industrialization have been the focus of the construction industry in recent decades. Lean construction has been shown to reduce waste and uncertainty, and construction industrialization has a positive effect on improving efficiency and safety. However, there is limited evidence in the industry that lean construction has sufficient potential in the context of evolving processes and methodologies. The present study identifies and quantifies the value, waste, and impact factors of lean construction practices and construction industrialization methods through a literature review, survey, and interviews. Interviews were used to validate the results of the literature review and survey. This study explores the relationship between lean construction and construction industrialization, and whether they reinforce each other to provide added benefit. The results of the study were used to assess 14 lean construction practices and four construction industrialization methods, along with their value and waste. The study findings reveal that the combination of the two methods can play a role in promoting each other in some instances. The contribution of this study to knowledge is an attempt to combine the application of lean construction practices and construction industrialization methods for better performance by demonstrating six aspects associated with construction projects.

Sustainable buildings are becoming increasingly desired. One of the specific aspects for evaluating sustainability is indoor environmental quality (IEQ), which affects occupants’ behaviors and health (OBH) directly. While most of the research focuses on high occupancy-buildings, this research is aiming at investigating and categorizing the IEQ factors that influence OBH of people working inside industrial buildings. This paper presents the state-of-the-art literature on IEQ factors and proposes potential mitigating solutions to problems caused by unhealthy IEQ. Through a systematic literature review of bibliographical databases, and through quantitative analysis, this research identifies that the most influential features of IEQ on OBH. The most widely studied IEQ factors by researchers are “thermal conditions” and “indoor air quality,” which directly affects clothing insulation and physical activity level of the occupants working in the industrial buildings. Potential solutions are highlighted based on the case studies from the literature. The contribution lies on describing the relationship between IEQ and OBH, as well as technological solutions for improving indoor environment.

Ensuring the effectiveness of safety training programs is essential for protecting construction workers and mitigating the occurrence of accidents, injuries, and fatalities on jobsites. Despite ongoing efforts to improve the design and implementation of safety training programs, challenges persist, particularly in engaging trainees. This study aims to identify the techniques used by trainers to promote engagement within safety training programs. To this end, trainers from construction companies across the North America were surveyed. Thematic analysis of the responses from 195 participants revealed five key themes of engaging techniques: collaborative learning, experience-based learning, hands-on group activities, maintaining a comfortable and relaxed training environment, and effective communication techniques. Additionally, the analysis also indicated that trainers have a positive, albeit cautious attitude toward using new technologies to promote engagement from trainees. The findings of this study will aid in the development of more engaging and effective safety training programs.

Worker safety is a critical factor to construction success and should be properly monitored and managed at jobsites. While many vision-based worker safety inspection/monitoring systems were developed by previous studies, they commonly suffer from low mobility of stationary cameras and the lack of taking real-time actions. To address these challenges, this paper proposes an intelligent inspection and warning robotic system using an unmanned ground vehicle (UGV). This robotic system (1) automatically and movably detects construction workers and personal protective equipment (PPE) using state-of-the-art YOLOv8 architecture deep learning-based computer vision model, and (2) dynamically warns/reminds workers of wearing the undetected required PPE. The developed system includes (1) a robotic vehicle prototype to provide mobility, (2) a high-resolution camera to collect visual data, (3) a speaker for auditory warning/reminder information, and (4) a single board computer for real-time data processing. The proposed system was tested at a real construction site. Field test results showed that it can reliably detect construction workers and their PPE and then play voice messages to remind them to wear the required PPE when it is not detected. Ultimately, this paper contributes to the body of knowledge by developing an intelligent UGV-based system for improving onsite construction safety management.

The construction industry is one of the industries with the highest rates of musculoskeletal disorders (MSDs). Masons are particularly susceptible to overexertion and back injuries due to the physical demands of their jobs and, thus, are more susceptible to MSDs. While previous research established that expert masons use different strategies to perform their work while experiencing reduced joint loads and increased productivity, it was unclear which movement strategies they used. This study analyzed the movements of novice, apprentice, and expert masons to characterize the postural characteristics of expert and in-expert techniques. Novice, apprentice, and expert masons’ kinematics are analyzed for a standard concrete masonry unit wall-building activity and investigated the key markers of these techniques, such as trunk flexion and twisting. Masonry movement techniques refer to the kinematics and postures that the expert journeymen adopt while working. The results of this study describe the movement strategies experts use in comparison to inexpert groups (novices and apprentices).

Falls from height (FFH) are the leading cause of fatalities in construction. Traditional methods for detecting FFH hazards have great limitations due to workers’ desensitized risk perception or supervisors’ subjective inspection. Electroencephalogram (EEG) provides an objective metric for overcoming the limits. Early hazard detection can be facilitated by measuring individual physiological states, exhibiting atypical patterns when workers face risks. Although previous work covered hazard detection using EEG, there is a scarcity of FFH-specific emphasis. How to determine appropriate EEG features for detecting FFH hazards was still not discussed. Therefore, this paper evaluated the validity of an EEG-based approach for detecting FFH hazards by establishing five supervised machine learning models. EEG data was collected from 20 front-line construction workers. Two EEG feature selection techniques were included, and performances of five classifiers were compared. Support vector machine was found to have the best overall classification performance for FFH hazard detection when using the filter-based feature selection approach, with a high accuracy of 79.20%. Adopting the proposed approach has the potential to bring managerial benefits in proactive safety management. It sheds light on the development of an early warning system through real-time monitoring of physiological states of construction workers while working at height.

Awkward working postures are deviations of body parts from their neutral position. Construction workers who hold these postures for a long term are exposed to discomfort, reducing safety and productivity. While several studies have been conducted to assess the effect of awkward postures on human physical and musculoskeletal system like muscles and joints, little research has attempted to explore the impact of awkward posture selection on physiological system. This investigation is necessary for a broader comprehension of risky factors resulting from the awkward working postures of construction workers. Accordingly, this study aims to evaluate if and how physiological responses such as heart rate and skin temperature will be affected by awkward working postures. The study utilizes a non-invasive wearable wristband biosensor to measure and monitor participants’ physiological signals during performing construction tasks in a simulated laboratory experiment. Signals in natural and awkward postures are then analyzed, and further comparisons are discussed. The results show that postures have significant impacts on physiological patterns, specifically when sustained for a longer duration. The findings of this study are expected to be used for the recognition of awkward working postures and further for the safety management interventions of worker behavior.

Cognitive and physiological abilities are essential in construction projects, which involve a complex and dynamic array of tasks and decisions. Cognitive abilities, such as problem-solving, decision-making, and critical thinking, are used to identify potential issues and develop innovative solutions, while physiological abilities, such as physical strength, endurance, and dexterity, are necessary for performing heavy and critical physical tasks. Both abilities are essential for communicating effectively within the team, adapting to unexpected changes, adjusting the plan as necessary, and implementing required changes. While several studies have explored cognitive and physiological abilities generally, this paper aims to conceptualize the relationship between cognition and physiology in construction to bridge construction workers’ psychology and physiology through a conceptual approach. After defining cognitive and physiological abilities, links among them are drawn, and their value for different social and personal skills in construction, such as collaboration and improvisation, is explored.

Work-related musculoskeletal disorders (WMSDs) are a group of painful disorders of muscles, tendons, and nerves caused due to improper work postures prevalent in construction workers. These disorders can cause temporary and/or permanent disabilities and seriously affect workers’ livelihoods. Previous research has applied machine learning (ML) for the recognition of WMSD risk. However, previous research used inertial sensors strapped to the body to measure the angle of the body parts. These sensors are expensive and uncomfortable to wear while working. This research aims to eliminate the need for additional hardware through the use of computer vision. In this project, an ML pipeline was built to identify WMSD risk from workers’ images. A pre-trained ML framework called Mediapipe Pose was used to generate features from the images. The relative positions of these landmarks were then used as the input for an artificial neural network (ANN) to classify ergonomic and non-ergonomic postures using the supervised learning approach. After hyper-parameter tuning, 100% training and 99.96% validation accuracy were achieved. Finally, the trained model was tested on real-life videos of construction workers and found to perform satisfactorily.

Mental fatigue in construction is a growing concern as it can negatively impact worker productivity, safety, and overall job performance. Factors such as long work hours, monotonous tasks, and high stress levels can contribute to mental fatigue. This can lead to decreased decision-making capabilities, increased errors, and a higher risk of accidents on the jobsite. To combat this challenge, studies have focused on measuring mental fatigue in construction using technologies such as EEG, eye-tracking, and virtual reality, among others. This study focuses on capturing the state of the science related to the utilization of technology to measure mental fatigue of construction workers. To achieve the objectives, relevant literature was searched on one of the largest scientific databases, Web of Science. Upon extracting the relevant research articles, the articles were investigated thoroughly to identify the technology adopted to measure mental fatigue, the targeted construction activity, and future opportunities to tackle mental fatigue in construction. The findings of this study will contribute to the body of existing knowledge by paving the pathway for future research on measuring and managing mental fatigue.

The construction industry in Egypt is booming at unprecedented rates. The Egyptian government is spending a significant amount of its budget to expand the country’s infrastructure systems and build new cities and business hubs. As a result, there is pressure to finish many government-funded projects in record time, which can come at the cost of workforce safety. Additionally, the safety measures adopted by construction companies in Egypt vary significantly according to organizational cultures and workers’ behavior. This paper investigates the current safety training content in the Egyptian construction industry via content analysis. The paper focuses mainly on scaffolds and falling object hazards as it is one of Egypt’s leading causes of accidents. Safety assessment tests were collected from a large-sized construction company and compared to the safety regulations of OSHA. Using content analysis to investigate the collected safety assessment tests, it was found that most of the questions addressed the various areas covered in OSHA. Thus, it was concluded that safety training programs are aligned with the safety regulations in OSHA. Additionally, the study identifies the gaps between OSHA and Egypt’s current assessment to provide recommendations to improve the safety assessment tests to address all hazards concerning OSHA.

Construction tasks impose high ergonomic risks, making it crucial to observe ergonomic problems (e.g., actions and postures associated with ergonomic risks) and provide solutions. As manually identifying problems and solutions is time-consuming and subjective, there has been extensive development toward automation through computer vision-based or sensor-based applications. Nevertheless, most existing studies have focused on assessing ergonomic risks, leaving tasks of recognizing problems and generating solutions to ergonomists. However, ergonomists are scarce in construction. Therefore, this study aims to automatically identify ergonomic problems and solutions from images by way of image captioning. To overcome limitations of traditional image captioning models, incapable of incorporating knowledge of ergonomics, this study applied a vision-language model (VLM) with caption augmentation leveraging text-based knowledge. The authors tested five work scenarios and showed superior performance of the proposed VLM over the traditional model. This result showed the feasibility of the proposed approach in identifying ergonomic problems and solutions.

Construction workers often experience high levels of physical and mental stress due to the demanding nature of their work on construction sites. Real-time health monitoring can provide an effective means of detecting these stressors. Previous research in this field has demonstrated the potential of photoplethysmography (PPG), which represents cardiac activities, as a biomarker for assessing various stressors, including physical fatigue, mental stress, and heat stress. However, PPG acquisition during construction tasks is subject to several external noises, of which motion artifact is a major one. To address this, the study develops and examines an autoencoder network—a special type of artificial neural network—to remove PPG signals’ motion artifacts during construction tasks, thereby enhancing the accuracy of health assessments. Artifact-free PPG signals are acquired through subjects in a stationary position, which is used as the reference for training the autoencoder network. The network’s performance is examined with PPG signals acquired from the same subjects performing multiple construction tasks. The developed autoencoder network can increase the signal-to-noise ratio (SNR) by up to 33% for the corrupted signals acquired in a construction setting. This research contributes to the extensive and resilient use of PPG signals in health monitoring for construction workers.

Fall from heights accounts for 50% of the accidents on construction sites, and 20% are scaffolding-related. Therefore, ensuring the safety of scaffolding installation can significantly reduce accidents onsite. Scaffoldings are erected by assembling components such as footboards, standards, ledgers, and cross-bracings. A safety check must ensure all necessary components in a scaffolding unit are installed in the correct order. While vision-based scaffolding detection has become more accurate with the recent success of deep learning detection algorithms, the detection of individual scaffolding units and their components is still challenging due to the scaffolding component size and shape. The size and shape factors pose many difficulties for the vision algorithms to detect thin structures and components of similar looks. To address the difficulties, this study proposes a method to segment the scaffolding units and components from a point cloud using a deep learning-based 3D semantic segmentation model. After segmentation, a rule-based approach can be applied to check the missing components. The method has been implemented in a construction project, and the preliminary results confirm its applicability for drawing workers’ attention to the missing scaffolding components, thereby improving the construction site’s safety.

Exoskeletons are promising solutions to address the prevalence of back injuries in the construction industry. However, scarce evidence is available regarding the acceptance of exoskeletons among construction workers. Using the unified theory of acceptance and use of technology (UTAUT) model, this study explored construction workers’ intention to use an exoskeleton. A field study was conducted wherein pipe and concrete workers performed construction tasks with a passive back-support exoskeleton. Data on workers’ perceived usability, discomfort, exertion, safety, and social influence from using the exoskeleton were collected. Spearman’s correlation analysis indicates a strong correlation between perceived ease of use, comfort, performance, safety and social influence, and workers’ intention to use the exoskeleton. However, discomfort and exertion did not have an impact on workers’ behavioral intentions. The findings of this study contribute to the UTAUT model and provide evidence on the drivers of workers’ acceptance of passive exoskeletons in the construction industry.

Wearable robots have the potential to address the occurrence of work-related musculoskeletal disorders in the construction industry. However, sparse evidence is available on how to implement these technological interventions in construction organizations. Therefore, this study aims to develop an implementation strategy to aid the adoption of wearable robots in the construction sector. An online survey was conducted to identify the factors (i.e., facilitators and barriers) and stakeholders that could influence the adoption of wearable robots. Using the results of the survey, constructs from normalization process theory, and literature review, an implementation strategy was developed. A four-step framework was formulated, which includes organizational decision on the implementation of exoskeleton, operational strategy, creating buy-in and training, and deployment monitoring. The study proposes one of the first conceptual frameworks to guide construction companies in the implementation of wearable robots.

Workers in the construction industry remain exposed to different accidents and hazardous environments. Yet, occupational health and safety (OHS) factors are the least addressed in contractors’ pre-qualification. As such, this paper presents two decision-making tools to evaluate contractors’ performance based on OHS-related criteria. Particularly, it presents the status of OHS-related orders in Alberta, Canada, and the most prominent criteria to be integrated in pre-qualification. Based on the identified criteria, a machine learning-based clustering model and an AHP model were formulated to assist in the pre-qualification process. Data related to construction OHS performance was obtained from the Workers Compensation Board between 2016 and 2020 for more than 1,500 contractors with more than 7,000 OHS-related orders issued. Orders related to (1) falls, (2) hazard assessment, (3) safeguards, and (4) entrances, walkways, stairways, and ladders were the most common ones. Both developed approaches showed the potential in supporting the contractor’s pre-qualification process.

The COVID-19 pandemic is evolving office building design and operation. Recent studies indicate that healthy building requirements, such as increased ventilation, higher air filtration, and temperature and humidity levels, could help mitigate the risk of airborne transmissions, such as COVID-19. As the risk of airborne diseases still exists, there is minimal information on the level current office building codes prevent or mitigate such risk. In response, this study aims to perform comparison of how building codes and rating tools recommend factors to mitigate airborne diseases. This study identified eight factors applicable to healthy buildings while mitigating the risk of airborne transmission allowing an effective comparison. The dataset included building codes for 15 states and cities and four rating tools, revealing several gaps in how codes and rating tools respond to airborne diseases. The study shows that, the benchmarked building codes lacked responsiveness to the risks of airborne diseases compared to building rating tools. The study results are expected to contribute to the effort of mitigating airborne transmission in office buildings, leading to healthier building design and operations in the future.

The manual handling involved in construction work can put workers at risk for musculoskeletal disorders (MSDs) and other health issues related to hand and arm movements. While some studies have investigated the potential impact of new technologies like exoskeletons on MSDs, they have not specifically looked at their impact on the amount of movement and effort required to perform a task [known as hand activity level (HAL)]. The American Conference of Governmental Industrial Hygienists (ACGIH) has developed a framework for assessing HAL and determining a safe level for a task, known as the threshold limit value (TLV). This study aimed to evaluate the effectiveness of an exoskeleton in reducing MSD risk in construction using the HAL-TLV framework. Ten participants participated in a simulated drilling task while wearing the exoskeleton, and the results indicated that it is possible to quantify the MSD risk reduction capability of an exoskeleton. This study helps us appreciate how the risk assessment tool works in construction. Practitioners and researchers can determine the true cost-benefit of using an exoskeleton as a safety intervention using the risk outcome from the risk assessment.

Given that the construction workforce is aging, managing their safety at jobsites has become increasingly crucial for the construction industry. While the effect of aging on situational awareness has been extensively discussed in various fields, no study empirically examined the impact of aging on construction workers’ hazard identification. Thus, this study utilized eye-tracking technologies to investigate the effects of aging on workers’ hazard identification performance and attentional distribution abilities. The results indicated that aging negatively impacts workers’ ability to broadly scan the 360° surroundings, as their cognitive processing becomes slower with aging. However, older workers showed better visual search strategies by fixating on hazardous areas in the scene, likely attributed to their crystallized intelligence that is information they have learned in the past and stored. As the proportion of older workers increases, it is crucial to prioritize their safety by addressing the impact of aging on their cognitive abilities and situational awareness and developing appropriate intervention strategies.

Situation awareness is a cognitive process involving perception, comprehension, and prediction of the environment, and it is critical to accurately identify and manage hazards. Analyzing differences in physiological data can help determine an individual’s situation awareness level and pinpoint the reasons for hazard identification failures. This study used eye movement data to quantify the differences in attention and search strategies at each level of situation awareness. The results showed that attention-related eye movement data, such as fixation duration, run count, and pupil diameter, increase in number or size as the level of situation awareness increases, while saccade count decreases. Moreover, individuals with higher situation awareness tend to use more systematic search strategies, enabling them to identify hazards more accurately and quickly. These findings provide insights into the differences between the various situation awareness levels and a foundation for developing customized training programs to enhance hazard identification skills.

Construction workers are at a high risk of experiencing heat-related illnesses as they mostly work outdoors, undertaking physically demanding activities. While several previous studies elaborated on the high likelihood of experiencing heat stroke on construction jobsites, the effects of heat stress that might translate into an increased risk of injury due to exacerbating physical fatigue and reducing vigilance were not well studied. To tackle this gap, this study monitored subjects’ objective and subjective fatigue metrics under various heat (no, low, high) conditions within a simulated XR roofing task. Findings showed the negative effects of heat stress on increasing physical fatigue levels and reducing the efficiency of attentional distribution to identify hazards, increasing the likelihood of incident involvement. These findings call for designing practical interventions to minimize the risk of heat-related illnesses and fatalities.

Construction is one of the most hazardous industries, characterized by high numbers of injuries and deaths. Despite employers implementing various safety programs to mitigate risks, these programs often entail high costs and inefficiencies. This article explores the utilization of safety nudges as a cost-effective and minimally disruptive solution to enhance safety performance in the construction industry. A survey was conducted among 108 industry participants to gather data on their attitudes and experiences regarding safety nudges. The core of the questionnaire consisted of four questions aimed at assessing perceptions and recommendations regarding the use of safety nudges. The results indicate that safety nudges hold promise as an approach for improving safety in construction, and many professionals believe in utilizing them in their safety routines. Construction companies should consider integrating safety nudges into their safety management strategies. Therefore, several practical safety nudges are presented in this article. Their implementation can enhance safety performance and reduce costs associated with traditional safety training programs. This study represents the first formal examination of construction safety nudges, providing inspiration for future research on this topic. Future research should focus on exploring different types of safety nudges and their effectiveness in diverse contexts and situations.

Wearable sensors proposed to measure fatigue in construction have shortcomings such as cumbersome size, high cost, flexibility, and high noise-to-amplitude ratio. Hence, this article demonstrates the use of inexpensive paper-based wearable electronics (PBWE) for predicting fatigue in the lower back of construction workers without compromising skin breathability or the natural motions of the wearer and with a low noise-to-amplitude ratio. Five subjects were recruited to participate in a simulated manual lifting activity in the form of a repetitive lifting task while measuring their physiological conditions using paper-based wearable electronics, EMG, and off-the-shelf optical heart rate monitors. Analysis of variance (ANOVA) was employed for this study. The BORG scale was also used to assess the fatigue level of the participants subjectively. The noise-to-amplitude ratio of the paper-based wearable electronics (PBWE) was significantly lower than that of the EMG by about 4.5 times. The analysis produced p-values less than 0.05, implying a significant change in the fatigue level after carrying out the repetitive lifting task from the base value. The findings imply that paper-based wearable electronics are portable and reliable devices that can predict physical fatigue and have excellent potential to improve the safety of construction workers on a construction site.

Safety is a critical issue in construction management, and one reason for accidents is the lack of situation awareness (SA) of workers. Although the concept of situation awareness has been widely studied in the construction industry, there are some inconsistencies among the findings regarding SA levels, measuring methods, and factors. Therefore, this paper aims to synthesize situation awareness research in the construction industry based on SA levels, measurements, as well as factors influencing SA. A systematic review method included database and search strings, screening and filtering, and coding and data analysis. In total, 124 papers were identified and evaluated by the research team to determine themes and categories. For each category, Endsley’s three-level theory is used as a baseline for SA levels; measurements of SA and SA components are developed for measurement classification; and factors including individual characteristics, processing, system design, and environment are decided. The findings can assist scholars in systematically understanding the current studies of SA in construction and shed light on future research areas.

Warning signs are a critical defense against construction accidents; however, there’s a lack of study on the effectiveness of construction signs. Therefore, this research examines the effectiveness of signs on construction sites by considering variables such as noticeability and comprehension. Noticeability relates to the signs’ ability to attract attention, whereas comprehension refers to understanding them. In this study, comprehension is related to eye movement metric total fixation duration, while noticeability is related to visit count within an area of interest (AOI). The experiments utilized a stationary eye-tracking device to track nine participants’ (Purdue University graduate students) attention and record reaction time. The results were analyzed using the Mann-Whitney test and found that the presence of text (p-value = 0.0) impacts comprehension speed and the use of pictograms in the signs (p-value = 0.049) impacts the noticeability. This research helps to identify and potentially improve less effective warning signs, contributing to decreased construction-related incidents.

Automaticity is a core attribute of skill that is achieved as the attentional requirement gradually diminishes as proficiency increases with practice or repeated execution of a task. Hence, it is a phenomenon that affects workers’ performance both positively (e.g., productivity) and negatively (e.g., accident involvement). Nevertheless, despite its significance, little is known about the effects of automaticity in the construction industry. To address this knowledge gap, this study used eye-tracking technology to examine the effects of automaticity on attention to productivity and safety-related areas of interest (AOIs) during repetitive construction activities. To achieve this research objective, 28 participants were recruited for a simulated roofing experiment. Based on the participants’ attentional distributions, this study employed a support vector machine to distinguish and identify workers who are exhibiting automatic behaviors from those who are not. The findings of this study discovered that although automaticity can improve productivity, it can also cause workers in the construction industry to pay less attention to safety-related AOIs, which puts them at greater risk of safety hazards. Construction managers can use this study’s model to predict automatic behaviors among workers to evaluate productivity and identify at-risk workers.

Fatigue is a significant safety concern in the construction industry and can lead to fatal accidents if not properly addressed. This article examines the impact of education on the level of fatigue in the construction industry through a longitudinal study. The study utilized a literature review and survey data collected over a five-week period to explore the relationship between education level and occupational fatigue in the construction industry. Overall, 80 construction workers participated in this study. Workers’ fatigue level was measured using a subjective scale called OFER (Occupational Fatigue Exhaustion/Recovery) scale. The findings highlight that higher education levels are associated with lowered fatigue levels. Further analysis revealed that education has both direct and indirect impacts on fatigue levels among workers. Directly, education empowers individuals with knowledge, skills, and critical thinking abilities, enabling them to enhance their understanding of safety practices, risk assessment, and hazard mitigation. Indirectly, education provides better job opportunities with less demanding tasks, such as office-based work. The practical implications of the findings suggest the importance of personalized safety training programs tailored to individuals’ educational backgrounds and job positions in the construction industry to effectively address fatigue, enhance safety practices, and mitigate the risk of accidents.

Active back-support exoskeletons are increasingly being perceived as potential solutions to the ergonomic risks of construction work. However, users of exoskeletons are susceptible to increased cognitive load could thwart the benefits of the device. Using self-reported cognitive load and electroencephalogram (EEG) data, this study investigated the detection of the cognitive load of users of an active back-support exoskeleton. EEG data and perceived ratings of cognitive load from participants performing flooring tasks are trained with several classifiers. The performance of the best classifier, Ensemble, improved using synthetic minority oversampling technique. This study contributes to existing knowledge by providing evidence of the extent to which cognitive load can be detected from the brain activity of exoskeleton users. The study also advances knowledge of the extent to which synthetic data could enhance the detection of cognitive load. Therefore, the study opens doors for improving exoskeleton designs to better support human cognition and performance.

The construction industry is known for the prevalence of musculoskeletal disorders, particularly back disorders, which are triggered by work involving abnormal postures. Active back-support exoskeletons are increasingly being perceived as ergonomic interventions to reduce the risks associated with construction work. This study examined the factors that would influence the adoption of active back-support exoskeletons in the construction industry. Using the Delphi approach, two-round surveys were conducted with key stakeholders such as safety professionals, supervisors, site superintendents, and construction professionals. The participants had the opportunity to experience the exoskeleton and provide feedback on the factors that could influence the adoption of the technology. The findings reveal the most important facilitators and barriers that should be considered to enhance the adoption of active back-support exoskeletons in the construction industry. The results could provide a practical guide for construction organizations to understand the requirements for implementing exoskeletons in their organizations. Manufacturers could use the design-related facilitators and barriers to adapt exoskeleton designs to construction work.

Construction is a demanding and dangerous profession that requires a high level of physical and mental stamina. Despite the known health risks, many workers smoke as a means of coping with the fatigue associated with the job. This study aimed to investigate the relationship between smoking and fatigue among construction workers. To assess this relationship, 80 workers participated in the study, with 43 non-smokers and 37 smokers. The fatigue levels of the participants were assessed using the Occupational Fatigue Exhaustion Recovery (OFER) scale, a subjective measure of fatigue. Results revealed that 46% of the surveyed workers were smokers, with 73% smoking tobacco and the remaining 27% using e-cigarettes or vapes. Although the mean fatigue score for smokers was higher than that of non-smokers, the difference was not significant. However, it is important to highlight the potential risks faced by non-smokers due to second-hand smoke exposure in the construction industry. Non-smokers on construction sites are at a very high risk of being exposed to second-hand smoke, which can have detrimental effects on their health. Organizations should prioritize creating smoke-free environments, implementing smoking cessation programs, and raising awareness about the dangers of smoking to protect their employees’ health and well-being.

Existing studies conducted across industries on the adoption and acceptance of safety technologies such as wearable sensing devices (WSDs) indicate that users still have some post-adoptive concerns about the use of these devices, and they tend to manage these concerns using different mechanisms. This unease is more pronounced in the construction industry reputed for being averse to the adoption of technology or innovation. Details about workers’ post-WSD-adoption behavior and how to measure their coping mechanisms have received limited attention in construction research. This study proposes a conceptual model that can be deployed to examine the coping mechanism and infusion of WSDs for safety and health monitoring in construction. First, a review of the antecedents of the adoption and use of WSDs is conducted followed by an appraisal of existing theories and models that can be used to explain user coping behavior and adaptation. Subsequently, the feasibility and utility of the proposed conceptual model was evaluated by construction safety experts. This study provides foundational insights needed to generate new knowledge regarding the post-WSD-adoption behavior of construction workers. Such insights could influence strategies geared toward enhancing the successful use of WSDs for construction safety and health management.

Construction employees face work demands that strain their life and mental health. With the recent pandemic and increase in digitalization, different sectors are re-designing jobs to improve employee flexibility. However, the construction industry would require sector-appropriate techniques to meet project demands and improve employees’ health. This study explores bottom-up job re-design techniques (i.e., job crafting and job sculpting) to identify techniques adopted that could improve work-life balance and mental health among supervisors. Using a qualitative methodology, 16 supervisors working in Hong Kong, Australia, Nigeria, and Ghana responded to semi-structured interview questions, including changes to their duties and deeply embedded interests they would like sculpted into their roles. The interview data were subjected to inductive content analysis. The result revealed that supervisors presently craft their jobs with six themes emerging but want more opportunities as it increases their perception of job control. The themes include modifying work schedules of subordinates and training subordinates. The respondents indicated that deeply embedded life interests designed into their roles should include research and development, fitness coaching, and training. This study developed a job crafting and sculpting design checklist for supervisors in construction organizations and highlighted the need to include employees in the job design process.

Sensing technologies demonstrate promising potential in improving construction safety. The current research efforts focus on the sensing technologies development, yet these technologies are not implemented or are in the initial stages of adoption in the construction sector. This paper aims to understand the constructs that prevent decision-makers from adopting these technologies to enhance the safety of field labor. A modified unified theory of acceptance and use of technology (UTAUT) was developed to study the barriers commonly found within technology adoption in the construction industry. The model addressed the perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, data security, and facilitating conditions. A closed-ended questionnaire was used to collect the study data. The study found that an association was found between behavioral intent to implement a biometric wearable sensing device and perceived performance expectancy, openness to data utilization, and the social influence of the client.

Many fatal and non-fatal occupational incidents have been reported in the construction industry globally. While many effective techniques have been developed in recent years to reduce the number of tragic accidents on the jobsite, there are still high rates of accidents. To improve management’s decision-making processes, machine learning (ML) has drawn significant attention due to its ability to analyze large quantities of data to identify potential hazards on construction sites. Compared with traditional techniques, ML is able to handle large datasets and, through the use of different algorithms, can quickly analyze them to produce more accurate interpretations. Although machine learning has been identified as a useful statistical method for improving the decision-making process, little systematic research has been carried out on the correlation between machine learning and construction safety. To address this gap, this study was developed to explore a systematic review of the effect of machine learning on the safety of construction work sites. In examining and reviewing research two databases, it can be identified that ML techniques can be a powerful leverage for discovering useful knowledge from large datasets to perceive relationships, trends, and correlations. This study provides a contribution to the research area of ML applications in enhancing construction safety.

Many hazards fail to be recognized on construction projects because of the complex and dynamic nature of construction environments, resulting in life-threatening accidents and fatalities. Researchers and safety experts have attempted to identify potential safety hazards using emerging technologies. In particular, building information modeling (BIM) has attracted considerable attention due to its ability to increase cooperation between all stakeholders involved in a construction project in order to reduce safety issues. Given the importance of BIM in improving hazard recognition as the first step in the process of safety programs, there is a shortage of systematic reviews of this necessary safety factor. In response to this need, this paper explored the impact of BIM on hazard recognition skills. To fulfill this purpose, two databases were examined, and related articles were reviewed. The research papers selected for this study were categorized based on the specific phase of construction. This categorization aimed to determine which phases in the overall life cycle of construction have been predominantly examined, as well as identify the phases that require more effort in hazard recognition using BIM. Furthermore, the findings provide insights into the most effective methodologies for each construction phase, which is summarized for easier access by researchers. The systematic review results provided a reference for safety management in the construction industry to reduce the number of workplace accidents using an established technology.