Journal of Management in Engineering

Numerous research proposed alternative selection methods to compensate for the drawbacks of the lowest qualified bidder (LQB) approach, including the below-average-bidder (BAB) method. While previous work has been carried out to simulate contractors’ behavior when altering the bid selection method, the literature lacks a tool that investigates how long it will take for both public and private owners to adopt the newly proposed alternative bid selection approach. To this end, this paper presents an agent-based model (ABM) that simulates the different bidding processes and tracks the impact on the owners’ utilities of switching from LQB to BAB. The ABM simulates the contractors’ learning behaviors to optimize their bidding strategies and markups throughout transitioning from LQB to BAB. Meanwhile, the ABM accounts for the owners’ utilities from using both selection methods and their preferences in utilizing one over the other using social learning. The findings of this research are validated by experts’ insights through a questionnaire survey. The model is tested against the ratio of public to private projects to provide insights into the owners’ decision-making processes and the potential market dynamics due to the proposed changes. One of the main findings of this research is that the ratio of public to private projects has a significant impact on the adoption of an alternative bid selection method. This paper contributes to the body of knowledge by proposing an approach to quantify market perturbations, the expected equilibrium, and how long the market might need to restabilize using BAB as the main selection method. Ultimately, the proposed approach would aid policymakers in understanding the impact of alternating the bid selection method, and adequately plan for the transition. In addition, the utilized methodology can be used in other managerial domains that require simulating different what-if scenarios.

Conventional scan to building information modeling (BIM) automation mainly deals with geometry. However, one of its limitations is the time it takes and the costs in generating material. Therefore, this study proposes an automated scan-to-BIM method considering both the geometry and material of building objects. It recognizes the geometry from a point cloud and the material from panorama images through deep learning–based semantic segmentation. The two extracted pieces of data are merged, and the BIM objects with material are automatically generated by using Dynamo. Here, the object–space relationships were applied to increase the accuracy of the material data to be included in the BIM object. As the result, the accuracy was improved by 48.66% compared with before the application. The proposed method can contribute to the improvement of the as-built BIM model usability because it can automatically generate a BIM model by reflecting the material, as well as the geometry of the existing building.

Learning from previous projects in light of benchmarking criteria is a desirable and popular approach to reliable project development and planning in the preconstruction phase. Previous similar projects serve as a practical and proven source of knowledge that can be applicable to future projects. In the early preconstruction phase, the common practice of similar project determination involves leveraging simple and limited project characteristics, resulting in determination accuracy degradation. In order to deliver a project context-based similarity evaluation, this study develops and proposes a natural language processing (NLP)–driven method that can recommend similar previous projects by systematically measuring the similarity between project scope statements. NLP techniques enable systematic measurement of project scope similarity, which addresses the reliance on (1) individual experience and expertise for comprehending contents; and (2) time and efforts for reviewing all the unstructured descriptive narratives of project scopes. The proposed method extracts key work activities from project scope statements, evaluates the level of homogeneity (LOH) between extracted activities, and quantifies the project similarity based on the homogeneity evaluation results. The proposed method utilizes bidirectional encoder representations from transformers (BERT) models that can embed unstructured texts into computer-readable numeric formats by considering the context of texts. The output of the proposed model is a graphical map depicting similarities that can help project engineers quickly and intuitively recognize the similarity evaluation results. The validity test shows that the proposed method demonstrates better performance in determining the most highly similar past projects with an ongoing project. The proposed method is appropriate for enhancing an effective information acquisition process from previous projects, resulting in an improved and more efficient project planning process during the preconstruction phase.

Delivery of infrastructure in Australia is increasingly challenged by shortages of professional civil engineers. It is therefore imperative to investigate their career optimism. Through the lens of the theory of work adjustment, we expand upon previous research on this topic. We identify the significance of occupational and organizational values and needs, adjustment, and commitment, emphasizing both individual and organizational factors. From a nationwide survey of Australian professional civil engineers, our logistic regression models identify the mechanisms that predict a civil engineer’s increasing or decreasing career optimism, which are distinguished through the constructs of Person, Environment, and the Adjustment between them. Our findings show that a civil engineer’s career optimism depends upon their years of professional experience and gender. Optimistic male civil engineers report affective organizational commitment, but career optimism declines with years of experience. Optimistic nonmale civil engineers report continuing organizational commitment and increasing optimism with experience. Predictors of an early-career civil engineer’s increasing career optimism include the occupational status value of responsibility and the ability to adjust within a work environment. Therefore, organizations must continue to build upon current practices in supporting a civil engineer’s occupational needs, provide flexibility, and understand their organizational commitment. Practical management implications that build career optimism of early-career civil engineers include provision of roles offering autonomy, responsibility, and flexibility, support returning from career breaks, and providing change management training.

The inevitable incidents (including accidents and near misses) that occur on construction sites provide opportunities to extract experience, optimize positive outcomes, and decrease the reoccurrence of unwanted events. Learning from incidents (LFI) is defined as an organizational capability to extract experience from incidents and convert them into preventive safety measures. Safety information flow (SIF) and resilient safety culture (RSC) have been found to be closely related to critical aspects of organizational safety management, such as reasoning incident causes and organizational learning. However, the interaction of LFI, SIF, and RSC and how it affects safety performance remain unclear. This study designed a questionnaire to investigate their interrelations. The questionnaire was distributed to construction companies from the Chinese mainland and Hong Kong, and the collected valid responses were analyzed using partial least-squares structural equation modeling (PLS-SEM). The results showed that LFI directly affects the reactive measures of safety performance, i.e., the occurrence frequency of accidents, near misses, and similar events on construction worksites. SIF positively impacts LFI, and RSC plays a mediating role in their relationship. The findings deepen the theoretical interpretation of SIF, RSC, and LFI. SIF facilitates LFI by reasoning the causes of incidents, and RSC provides a supportive environment for upgrading LFI. The findings also guide practitioners to improve LFI and construction safety performance by taking measures to ensure nondefective safety information and smooth information flow within the resilient cultural context.

Managing the fatigue of construction workers is crucial to productivity, quality of work, and accident risk reduction. However, the current practice for assessing fatigue is limited when applied to construction sites. This study proposed a framework to objectively and subjectively evaluate construction workers’ fatigue in real-time using an ecological momentary assessment (EMA) application and smartwatch data. Fatigue data were collected from 100 construction workers over three days. The results revealed that objective fatigue factors (heart rate and physical activity) were easily affected by the characteristics of the construction field (i.e., starting early, changing and demanding schedules, and overworking hours), whereas subjective fatigue steadily increased with working time. Most workers were aware of physical fatigue at the end of work for the day, when the EMA scores were the highest in a day. However, objective and subjective fatigue did not completely concur throughout the work period. Our findings are expected to improve the management of construction site health and safety with priority given to construction workers. The proposed framework, which utilizes EMA and wearable devices as a fatigue assessment method, reflects the comprehensive aspect of work-related fatigue.

The major objective of this research is to investigate what skill sets are desirable to utilize a multiskilled labor force in the construction industry using web scraping and text analytics. The construction industry has been suffering from skilled labor shortages for decades. To resolve the shortage and pertinent problems, multiskilled labor utilization has been presented in construction. Although extensive research has been conducted over multiskilling, it is still uncertain what skill sets are desirable to foster and utilize multiskilled labor force in construction projects. It is also difficult for laborers to know which skill sets will be competitive in the job market. To fill this gap, this research investigated skill sets for multiskilled labor development in the construction industry using web scraping techniques. The results were derived from large data collected over a year. We analyzed texts of construction job advertisements and provided the trends in what skill sets are beneficial for laborers to get employed using text mining techniques with categories from US Bureau of Labor Statistics. The results provided construction workers with the information of what kind of skill sets are required to meet industrial needs. This study provided two types of dual skill sets matrices from text analytics, analyzing only the texts that are essential to job advertisements and analyzing all the texts in job advertisements. The research also provided an example for multiskilling strategies with the matrices and an additional matrix from professional surveys. This study will contribute to the body of knowledge by identifying high-demand dual skill sets and presenting the multiskilling through the data-driven analysis of real construction job market.

The adoption of human–robot collaboration (HRC) in various forms is widely expected to help improve productivity, reduce human physical workload, and alleviate the issues created by a skilled labor shortage in the construction industry. One potential deterrent to adoption of such collaborative work methods often cited by industry stakeholders is resistance by workers stemming from their fear of losing their jobs or having to learn new approaches and methods of performing construction work. Although significant prior studies have used path analyses to statistically evaluate hypotheses about technology adoption models by estimating the relationships in the models, no research has specifically investigated the intention to work in HRC through such an approach. This study addresses this knowledge gap and empirically investigates the factors affecting construction personnel’s behavioral intention to accept HRC. HRC in this study is envisioned as robotic assistants performing the physically demanding repetitive work while construction personnel focus on cognitive tasks necessary to plan and supervise the work. An HRC adoption model based on the technology acceptance model (TAM) and innovation diffusion theory (IDT) is presented. This study expands a TAM-IDT model with new constructs such as job satisfaction and openness to training to explore an improved model that increases the understanding of construction personnel’s intention to adopt HRC. Data collected from 156 construction personnel were analyzed to identify the key components based on exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The proposed model, consisting of eight external and four internal variables, was examined using structural equation modeling (SEM). Results indicated that variables such as perceived usefulness (PU) and perceived ease of use (PEU) had a positive and significant impact on behavioral intention (BI) to work in the described HRC system. Both theoretical and management implications emerging from the study are offered as insights to assist the construction industry in managing the introduction and adoption of HRC for on-site construction.

Abstract

Daycare centers are the first spaces where children develop social relationships and enhance their senses through public education. Despite its significance, marginal guidelines are provided for the design of centers in the Republic of Korea (ROK); thus, clients have different perceptions of the priority of functions. To bridge these gaps, in this study, guideline for daycare center planning were established by applying value management (VM), rarely introduced in the ROK. First, importance-performance analysis was conducted to identify the gap between the ideal and status of daycare centers based on the developed framework. The analysis results explain the attitude toward risk perception of end-users and construction practitioners. The VM workshop process was then executed thoroughly, and diverse perceptions of risk factors for each space in the daycare center were moderated to propose a sample drawing and guideline. Compared with existing drawings, the room arrangement was improved for utilization and safety, and materials were developed to increase eco-friendliness and safety. The introduction of VM to establish daycare center guidelines suggested that continuous communication to coordinate perceptions supports the integration of stakeholder opinions based on the required space with reduced risks. The unification of the overall goal with a reasonable solution could be identified through the VM process. However, the lack of top management support and inexperience in VM are hindrances to overcome in the context of the ROK. Therefore, the encouragement of clients’ participation actively and systematically in the VM process by establishing governance is necessary to increase user satisfaction and outputs.

Practical Applications

Based on the importance-performance analysis conducted in the pre-VM workshop, it was possible to identify and compare the differences in perceptions of representative stakeholders. Therefore, it could support the understanding of the interests of stakeholders participating in the project in advance through systematic steps, rather than by simply applying VM. This research provided opportunities for stakeholders who do not experienced VM workshops in the ROK. It helped to identify which part was insufficient during the VM workshop. VM can be used in a more advanced way now that the interests of different stakeholders have been taken into account. Thus, environments in which VM can be applied can be expanded, thereby reducing the cost, as the basic purpose of VM, is increasing information sharing and communication and, in turn, increasing the quality and performance of the output as well as trust among stakeholders. If the guidelines and sample drawing generated in this research are followed in a practical manner, daycare facilities with reasonable building costs and appropriate functionality can be established, as the opinions of representative stakeholders and experts are genuinely reflected. These aspects are an improvement over the existing plans; therefore, they could increase the satisfaction of end-users.

Rapid and efficient infrastructure restoration is critical to reducing the impacts of extreme events on community lifelines. Following a large-scale extreme event, infrastructure restoration at various stages is carried out simultaneously by agencies at various government levels and jurisdictions. Since each agency has different roles, responsibilities, and boundaries within which it operates, coordination and communication among them are challenging. With the overall goal of providing a common operating picture and facilitating concerted planning and action among emergency response agencies, this research proposes a data-driven and equity-centered framework that links the various stages—damage identification, restoration scheduling, and monitoring and control—of infrastructure restoration. This study takes a particular focus on the highway restoration caused by flood inundation. In detail, the framework is composed of three parts, including (1) a systematic data-driven approach that quickly provides spatially distributed estimates of highway inundation, (2) an equity-centered restoration scheduling strategy that prioritizes restoration tasks based on community social vulnerability, and (3) a Bayesian-based approach that provides an up-to-date indication of the impacts of component level changes on the overall restoration progress. A case study on highway inundation in Harris County during Hurricane Harvey was conducted to demonstrate the feasibility and applicability of the proposed framework. In the case study, multisource data, including physical highway topology, geospatial information, field inspection results, and socioeconomic and demographic data, were used. Our framework generates outputs that can be used for rapid damage identification, automated restoration scheduling, and real-time progress updating. In practice, these outputs facilitate quick and shared situational awareness among the involved agencies, which is expected to ease communication and coordination and help overcome challenges resulting from parallel and fragmented restoration efforts. To the authors’ best knowledge, this is the first framework that aims to support the management of infrastructure restoration by synthesizing various restoration stages.

Women are significantly underrepresented in construction and engineering industry leadership roles despite having comparable qualifications, experience, and degrees. Improvements require a better understanding of the factors leading to the underrepresentation of females in the construction industry. While prior studies report that a male-dominated culture in construction negatively impacts females’ career advancement, minimal research explores the role congruity theory and the likeability and competency dilemma among construction leaders. When striving to succeed in traditionally male-dominated fields, women face a unique challenge because they need to defy gender stereotypes created by cultural norms, encapsulated by the likeability versus competency dilemma. Females who accomplish traditionally male tasks and demonstrate independence, assertiveness, self-reliance, and power are no longer seen as likeable. Through analyzing publicly available LinkedIn recommendations, this paper proposed a data-driven approach for examining the likeability and competency dilemma for female construction leaders. Results showed that female leaders in construction were seen as competent in the same way as their male counterparts, but far less likeable (a likeability score of 28% compared to 51% for males). Developing a text mining model to predict the gender of the person receiving a recommendation, this paper also highlights unconscious bias in describing and reacting to leaders’ successes. The machine learning model accurately predicted the gender of the person receiving the recommendation with more than 86% accuracy. Despite possessing the sufficient capability to handle traditionally male work successfully, women receive negative judgment from colleagues, creating challenges in their career paths. Finally, the paper contributes to social role theory and role congruity theory by highlighting the differences in gender roles and leadership roles for women in the construction industry.

Abstract

Technological recombinant innovation (TRI) has been regarded as an important part of technological innovation in the construction industry. However, the pattern of how TRI affects the performance of construction firms is still unclear. Building upon architectural innovation, this study identifies the exploratory and exploitative TRIs and explores the mediating mechanism of business models, by which TRIs influence a firm’s performance. Using text mining, panel data analysis of 107 China-listed construction firms was conducted to analyze the relationships. The results show that the positive relationship between exploratory TRI and firm performance is weakened by novelty-centered business model, while the negative effect of exploitative TRI on firm performance is strengthened through its mediation. This research enriches the literature on the relationship between technological innovation and firm performance by highlighting the important role of TRIs in predicting firm performance and differentiating mediating mechanisms for the two types of business models. In addition, the application of text mining to quantitatively evaluate the types of business model from the annual reports of listed construction firms, provides a new quantitative evaluation method for the research of business models.

Practical Applications

This study discusses the necessity of recombinant innovation as the source of technological innovation for construction firms, and provides guidance for construction firms in regards to their technological innovation approaches. Firms should focus on exploratory recombinant innovation, actively incorporating external technological components and exploring the new combinations of internal technological components. In terms of exploitative technology recombination, the reuse of existing technology combination may lead to knowledge solidification, which leads to the continuous decline of firm performance. Therefore, firms should pay attention to the balance and interaction of exploitation and exploration. Additionally, since the negative mediating effect of the novelty-centered business model has on the relationship between technological recombinant innovation and firm performance, firms should be mindful about the business model they employ. Adopting novelty-centered business model can be risky for construction firms that are in slow clockspeed industries. Instead, the best way for construction firms to keep thriving is by focusing on their efficiency-centered business model which can help them remain stable by protecting existing resources and competitive actions to maintain a competitive advantage.

Abstract

In the past two decades, Sub-Saharan Africa (SSA) has had massive infrastructure projects development to meet the increasing demand for public services and foster socioeconomic transformation. As a result, there has been an influx of Chinese international contractors (CICs) in SSA in order to close the infrastructure gap. Despite the significant involvement and active participation of CICs in the global construction industry, clearly few studies have investigated the performance of CICs in SSA, and their performance is not very well understood. The goal of this paper is to provide insights into the performance of CICs in SSA using a case study of an engineering-procure-construct (EPC) hydropower project in Uganda, East Africa. A literature review was carried out followed by a case study analysis including interviews with the owner’s key senior project participants. Performance is evaluated based on five criteria: schedule; change orders; quality; health, safety, and environment (HSE); and owner satisfaction. The findings indicate poor performance of the CIC with 20% schedule overrun, breach of change order procedures, poor quality of work, high rate of incidents and fatalities, and low level of owner’s satisfaction. The findings and lessons learned, though specific to this case study, will be useful for public agencies in developing countries, particularly in SSA, administering similar contracts and projects with CICs.

Practical Applications

The paper established the poor performance of a Chinese international contractor (CIC) on a hydropower engineering-procure-construct (EPC) project in Uganda, based on five project performance/success criteria: schedule; change orders; quality; health, safety, and environment (HSE); and owner satisfaction. Ultimately, the outcomes of this paper provide practical insights for industry practitioners on performance evaluation of CICs executing projects using EPC project delivery methods. Lessons learned and best practice strategies are also put forward to mitigate the identified performance shortcomings of CICs in future projects and improve the chances of project success. What is truly important, in the end, is owners dedicating sufficient resources for pre-project planning including the selection of competent and credible EPC contractors and the owner’s engineer to ensure enhanced project contract administration.

Transport agencies are under increasing pressure to mitigate the global warming impact of our pavement systems. This objective, however, must be carefully balanced with other performance metrics of interest (e.g., pavement condition) for federal and state agencies. This paper details a network-level tool aimed at supporting transport agencies in achieving two competing objectives: (1) maximizing the number of pavement segments in a good state-of-repair; and (2) minimizing the global warming impact of the network. The stochastic optimization model follows a two-stage bottom-up approach, where optimal policies are learned for individual facilities, and those decision-rules are subsequently used to guide the allocation of resources across the network. The model is applied to a realistic roadway network composed of 159 miles of pavement segments based on data made available via the Highway Performance Monitoring System. The case study results highlight that, over a 20-year analysis period, maximizing the condition of pavement assets across the network increases its expected global warming impact by 1% to 8%. The results also highlight that, invariant to the selected objective and/or available budget, increasing the allocation of funds toward rehabilitation activities rather than reconstruction treatments improves the overall condition of the network by as much as 25% and reduces its global warming impact by up to 7%. The results of the case study provide decision-makers with important insights around the impact of pavement management performance goals and budgetary policies on the global warming impact of pavement systems.