Journal of Construction Engineering and Management

The construction industry is becoming increasingly interested in blockchains, and relevant publications have blossomed in recent years. This study provided a state-of-the-art review of blockchain research in the industry in order to better comprehend the fast-evolving technology. A systematic method integrating quantitative and qualitative analyses was used to capture the research status, challenges, and future directions based on 141 publications published between 2017 and 2021. The distributions of 141 papers were quantitatively analyzed in terms of chronology, geography, journal, and research type. The majority of blockchain applications focused on the construction stage, while progress payment management and supply chain management were the top two topics. Different blockchain applications throughout the entire lifecycle of building projects were also introduced through qualitative analyses. Finally, this research indicated the challenges and future directions of blockchain research in the construction industry. This review provides valuable information to help construction researchers and practitioners understand blockchain research advances and better determine research opportunities.

Abstract

The construction industry has grown significantly to fulfill the expanding need for accommodation, facilities, welfare, and services resulting from the rapid increase in the global population. Infrastructure projects in particular facilitate transportation and services for both new and existing areas, yet they are also one of the primary producers of construction waste, which is harmful to both society and the environment. Hence, the objective of this paper is to identify the factors affecting construction waste management in infrastructure projects (CWMIIP) and study their respective impacts on construction waste management (CWM) performance. This study makes a unique contribution by not only addressing the factors that influence the development of construction wastes and their importance, but also by giving quantifiable solutions and action plans to reduce the waste. Using a literature review, 26 factors were selected as the major contributors to construction waste in infrastructure projects. An online questionnaire was then developed to collect data on the respective impacts of these factors. A total of 167 complete responses to the questionnaire were collected, and a structural equation model (SEM) was developed to quantitatively measure the impact of each factor on waste management performance. The model was validated using goodness of fit (GOF), multivariate normality, construct validity, reliability, and hypotheses analyses. The results indicate that the most significant factors affecting the generation of construction wastes are “quantity take-off error by contractor,” “unforeseen incidents damaging site and/or completed works,” “design errors,” and “extreme weather conditions damaging completed works.” The model results showed that all predefined hypotheses were supported, except for the positive effect of the logistics group, which was dropped during model development. Individual factors were also divided into groups, and the total impact of each group was assessed; the “management” group had the greatest impact, followed by “execution,” “others,” and “procurement.” Based on these findings, we make several recommendations to construction industry professionals to reduce waste and mitigate its harmful effects, such as requiring a construction waste management implementation plan before awarding a project to a contractor; engaging with project suppliers to provide execution training for contractor staff; ensuring proper implementation of health, safety, and environmental plans; and purchasing locally available materials.

Practical Applications

This research built a model based on structural equation modeling that will help researchers better understand the links between building waste management parameters. To examine the impact of each waste component on construction waste management performance, a quantitative model is built based on the data obtained. This quantitative waste management model could be used to track construction waste management performance. The findings of this study are not limited to infrastructure projects because they may be tailored to fit any project. As a result, it is recommended that project contractors and supervision consultants rely on the factors responsible for construction waste generation identified in this study to capture their actual construction waste factors. Also, having a record and log of all construction wastes generated during a project, as well as the reasons for these wastes, would help the organization improve its waste management learning curve for future projects.

This paper proposes a coherent risk measure to assess project cost overrun contingency reserves: the project expected cost overrun ($ECO$). It is shown that the traditional project cost-percentile metric does not qualify as a coherent risk measure and, therefore, that the $ECO$ metric should be adopted. Obtained from the tail expectation of the project cost probability distribution, it measures the average value of project costs exceeding, at any significance level, the project cost baseline. A positive, unique, and exact closed-form solution to the $ECO$ risk measure is devised under, among others, the normal probability distribution. The resulting project cost overrun contingency reserve may be interpreted as an insurance contract covering contingent risk factors and claimable by project managers from their project director once preidentified and agreed-upon contingent high-risk cost impacts are taken into account. The project $ECO$ risk measure is extended to assess the project expected time overrun ($ETO$) and the project expected time overrun penalty ($ETOP$) risk measures, quantities that will prove indispensable for successfully tendering under any contractual form the minimum value of project time and project cost bids. They will be covering, at the very least, all relevant potential low-probability high-risk contingent risk factor impacts.

There are many manuals and codes to normalize each procedure in civil and construction engineering projects. Data tables in the codes offer various references and are playing a more and more valuable role in knowledge management. However, research has focused on regular table structure detection. For nonconventional tables— especially for nested tables—there is no efficient way to conduct automatic interpretation. In this paper, an automatic table knowledge interpretation model (TableGraph) is proposed to automatically extract table data from table images and then transform the table data into table cell graphs to facilitate table information querying. TableGraph considers that a table image is composed of three types of semantic pixel classes: background, table border, and table cell contents. Because TableGraph only considers pixel semantic meaning rather than structural rules or form features, it can handle nonconventional and complex nested table situations. In addition, a cross-hit algorithm was designed to enable fast content queries on the generated table cell graphs. Validation of a real case of automatic interpretation of inspection manual table data is presented. The results show that the proposed TableGraph model can interpret the structure and contents of table images.

The effective use of building information modeling (BIM) in the design phase generates countless benefits that contribute to risk management (RM). However, a better understanding of the relationship between the critical-success factors (CSFs) in the design phase has not yet been addressed. This study aims to investigate the influence of BIM CSFs in the design phase in the RM process, exploring the mediating effect played by BIM knowledge, RM knowledge, and BIM maturity. The research design applies the partial least-squares structural equation modeling technique, and the variables were collected by a survey with a sample of 195 respondents from different countries. The results pointed out that earlier and accurate three-dimensional (3D) visualization of the design was the top-ranked recognized design factor in the use of BIM. The findings also indicated that BIM design CSFs have a positive impact on the RM process. Furthermore, there is a positive and significant indirect effect of BIM knowledge, RM knowledge, and BIM maturity through the path of BIM Design CSF on RM.

The absence of a clear collaboration process at the design stage of rail projects hinders the ability to provide the right information at the right time for the right purposes. The scope and complexity of rail projects make them particularly sensitive to problems arising from poor collaboration. Within projects, collaboration is a key enabler for effective decision-making and rework reduction, and this leads to time and cost savings. Strategies and technologies such as Building Information Modeling (BIM) and Geographic Information System (GIS) facilitate collaboration among project stakeholders during the asset delivery phase, but require a roadmap to ensure successful implementation. Using collaboration as a theoretical lens, and applying collaboration technologies, this paper conceptualized the collaboration processes for rail delivery with the focus on the design stage. The aim of this research was to develop a collaboration process model at the design stage of rail delivery. Ten in-depth interviews with BIM, GIS, and rail delivery experts were carried out to identify the dimensions and characteristics of the collaboration process model. The collaboration process emerging from the results was modeled using Integrated Definition (IDEF) notation, and revealed the project activities, key players’ roles, and responsibilities for effective BIM-GIS–based collaboration in the design stage of rail projects. The process was operationalized by customizing a commercial common data environment platform and was validated using a focus group and an in-depth interview to establish the trustworthiness of the theoretically modeled collaboration process for rail delivery. The precise model of the collaboration process, based on integrated use of BIM and GIS, constitutes a significant original contribution of this research. This model should facilitate smoother implementation of these technologies, timely information flows between project participants, and ultimately more-effective delivery of rail projects.

Extended reality (XR) technologies are increasingly being used as a novel research instrument to facilitate scientific inquires in the construction engineering and management (CEM) domain. By allowing humans to interact with immersive environments in controlled and monitored experimental settings, XR technologies have opened new opportunities for researchers to conduct CEM research involving human participants or concerning human behavior. Yet, XR-enabled research, as an independent, rigorous methodology for the CEM domain, is still underexplored. This paper serves as an effort to build an organized knowledge base and workflow for using XR technologies in various CEM research areas and methodological contexts. The paper first investigates the status quo of XR-enabled CEM research, by identifying current research areas in the CEM domain where XR technologies are considered the preferred or recommended methodological solutions. A process model for XR-enabled research is then proposed, with actionable recommendations about how XR-enabled research should be planned, designed, implemented, analyzed, verified, and validated. This process model is demonstrated with two illustrative case studies. Last, the paper discusses the philosophical, methodological, and technological roots of the evolution of XR-enabled CEM research and describes our vision of more enabling, adoptable, and value-adding XR-enabled research in CEM in the near future.

This paper presents the results of the first stage of a research project focused upon supervisor–apprentice communication in the construction industry and the influence that this communication has on apprentices’ health, safety, and wellbeing. In-depth interviews were conducted with 30 apprentices and 11 supervisors in the Australian construction industry. Interviews explored the quality and nature of supervisor–apprentice communication. The data was analyzed using an inductive and iterative process of reading, coding, and reflection. The results show that apprentice–supervisor interactions play an important role in what is learned and, ultimately, how safety is enacted. Previous research has identified the frequency and quantity of supervisor communication as important for safety in the construction industry. However, The findings of the present study extend this work to provide qualitative insights into the role played by supervisors’ safety-related communication in determining: (1) what is learned about safety and risk by inexperienced workers during their on-the-job training; and (2) how work is performed in ways that reflect varying degrees of risk acceptance among apprentices. The qualitative data provide rich and nuanced information regarding the dynamic interactions that take place between apprentices and supervisors within the material environment of the construction site. The results suggest that providing classroom-based safety training to apprentices may be insufficient to ensure safety learning in practice. Rather, effective safety learning programs for apprentices need to consider the social context of the workplace and ensure that supervisor–apprentice communication is supportive of positive safety learning outcomes and behaviors.

Collaboration has become a popular way for off-site construction (OSC) project teams to achieve cross-operation construction tasks. However, prior studies of collaboration have focused on the stakeholders’ in-role collaboration behavior and the effects of a narrow subset of organizational elements (i.e., contract, organizational structure, trust, and social relationship). Our study focuses on collaborative organizational citizenship behavior (COCB), viewed as a form of extrarole behavior. It explores organizational elements’ configuration effects using a fuzzy-set qualitative comparative analysis (fsQCA). Our main findings verify the configuration effects of organizational elements on COCB. The double-edged sword effects of contractual control and organizational formalization are identified. A social exchange strategy is explored, consisting of benevolence trust, organizational integration, and social relationship, as it is the most prevalent strategy found in OSC project team COCB. Configuration conditions are found to be asymmetric in the presence or absence of high COCB. This study expands the theoretical understanding of COCB, showing that its emergence is not a completely altruistic behavior but is instead a limited discretionary behavior. This study also contributes to organizational citizenship behavior and stakeholder theory by linking projectized stakeholder management based on formal contracts to COCB based on informal social exchange. It offers policymakers, project managers, and stakeholders a complete picture of how the organizational strategy configurations, including the mutual benevolence trust, agile organizational integration, and close social relationships foster high COCBs in the OSC project team.

To improve the efficiency of safety management, it is important to classify massive and complex construction site safety hazard texts in large-scale projects. High-precision safety hazard text classification is a lengthy and challenging process. Most existing safety hazard text classification methods capture semantic information using machine learning or deep learning, ignoring the syntactic dependency between words. However, syntactic dependency contains rich structural information that is useful to alleviate information loss and enrich text features. To address these issues, this study proposes a graph structure–based hybrid deep learning method to achieve the automatic classification of large-scale project safety hazard texts. The method uses syntactic dependency and Bidirectional Encoder Representation from Transformers to express the syntactic structure and semantic information of text, and a graph structure fusing the syntactic structure and semantic information is constructed to quantify text information. Further, an encoding-decoding mechanism is built using a graph convolutional neural network and bidirectional long short-term memory to address graph structure data and classify safety hazard texts. Our proposed method is used to classify hydraulic engineering construction safety hazard texts, and the classification accuracy reaches 86.56%. Meanwhile, the experimental results demonstrate that our model achieves superior performance compared to existing methods. This proves the ability of our model to capture and analyze text information and verifies the reliability and effectiveness of this method in large-scale project safety hazard management.

Prefabricated construction has recently played an increasingly significant role in social, environmental, and economic developments in the construction sector. Because prefabricated construction projects (PCPs) are interorganizational in nature, their successful delivery, especially under uncertain circumstances, relies on organizational resilience performance throughout the life cycle. The purpose of this study is to develop an ethnography-based methodological framework for the PCPs to evaluate, analyze, and improve the dynamic organizational resilience regarding efficiency, flexibility, and robustness from a life cycle perspective. A two-part framework is developed. The qualitative first part identifies the roles and relationships of participating stakeholders during each project phase, and the second quantitative part measures and analyzes the PCP’s organizational resilience. A typical engineering, procurement, and construction (EPC) contract PCP in China is used as a case analysis to assess the validity of this developed framework. The results show that the robustness and flexibility of the PCP should be improved. Such improvement can be achieved by managing and enhancing the workflow control and how stakeholders communicate. This scenario was examined via a comparative analysis of employing building information model (BIM) and decentralized collaboration platform (DCP) within the case project. Finally, this developed framework could be used as a practical decision-making tool for managers to improve the organizational resilience in the aspects of efficiency, flexibility, and robustness of PCPs in case unforeseen events are encountered. It contributes to the current knowledge body of PCPs by incorporating organizational resilience theory into its performance evaluation and improvements.

Offsite construction is gaining attention due to government policies promoting automation and productivity. Therefore, understanding the impact of building design on construction cost and the carbon footprint associated with offsite construction is important for the improved sustainability and climate resilience of the built environment. This study aims to develop a system approach, with the aid of building information modeling (BIM), for 3D geometric modeling and automatic generative design toward optimizing the carbon footprint and construction cost from offsite construction. A mathematical formulation is proposed to represent the topological relationships between different kinds of precast and cast-in-situ elements, which in turn, underpin 3D geometric modeling for possible geometric variations within precast buildings. New generative algorithms are developed to automatically manipulate building geometrics subject to pre-defined constraints, and create parametric BIM models in compliance with material types assigned by users. A BIM-based automation tool is developed to extract and match the model geometry/material information with a customized BIM object library, through which carbon emission factors and cost coefficients can be retrieved for multi-criteria sustainability analysis. The proposed new approach empowers 3D geometric modeling and geometry-based design automation, which enable comprehensive exploration of design alternates in precast construction. The proposed new method is illustrated via a case study that investigates the impact of different design variations on embodied carbon and construction cost of precast structures. The proposed BIM development takes around 30 minutes to create 1,000–1,500 new design options. Besides, it produces design options that contain a 30% less carbon footprint and construction cost than reference buildings from the literature. The results indicate that our proposed method can support automated design exploration at early stages, which help to identify optimal solutions for more informed decision making.

Abstract

The proportion of older workers in the construction workforce is increasing rapidly, due to an aging population globally. While the presence of older workers offers many benefits, they are deemed to pose added occupational health and safety (OHS) challenges. Understanding age-related OHS risks is essential to initiate appropriate interventions. Construction workers’ compensation data collected by Safe Work Australia from 2008 to 2019 was analyzed using three-way chi-square technique to investigate the influence of age on OHS performance, with consideration of moderating factors such as gender, organizational size, occupation, incident mechanism, and nature of injury or illness. Findings suggest that older ($\mathrm{aged}\ge 55$) and aging (aged 45–54) construction workers are more vulnerable overall to fatalities and permanent incapacities. Organizational size has a statistically significant influence on the relationship between age and incident frequency and severity, with the greatest number of fatalities among older workers in micro-sized organizations. Gender has a statistically significant influence on the relationship, with older female workers recording zero fatalities. Similarly, permanent incapacities increase with age among male workers whilst it decreases among females. There is a noticeable increase in fatalities and permanent incapacities among older workers in four occupations: carpenter and joiners, plumbers, electricians, and construction laborers. Regarding incident mechanisms, exposure to chemicals and substances caused the greatest number of fatalities among older workers, followed by falls and then vehicle incidents. Exposure to loud sound and pressure is associated with the greatest number of permanent incapacities. Occupational respiratory diseases, circulatory diseases, and cancer are associated more with fatalities among older workers than injuries. Nervous system and sense organ diseases caused the highest number of permanent incapacities. These insights can inform construction organizations and policymakers about aspects for implementing purposeful preventive measures to safeguard the aging workforce.

Practical Applications

The presence in the construction workforce of workers in different age groups offers both opportunities and challenges from the perspective of OHS management. Construction organizations must manage the unique OHS challenges posed by workers of different age, in order to improve their overall OHS performance. Specifically, attention must be paid to older and aging workers, in order to reap the performance benefits derivable from them. Construction organizations need to implement measures that better protect older workers against death and permanent incapacity due to their increased vulnerabilities. In addition, incidents causing temporary incapacity need to be reduced for younger construction workers, given their high representation. Similarly, construction practitioners, organizations, and policymakers could use insights into the relationship between age, incident severity, and other variables (such as occupation, organization size, gender, and injury mechanisms) to inform OHS management procedures and practices and implement purposeful preventive measures, rather than adopting a one-size-fits-all approach. Organizational initiatives to support an aging construction workforce should widen their focus by encompassing adjustments for task allocation, task design, and work environment, to safeguard vulnerable groups of workers. Additionally, increased awareness of OHS risks among workers of different ages could make them extra cautious about more vulnerable occupations, injury mechanisms, and injury types commensurate to their age.

Abstract

Among the various methods to resolve construction disputes, negotiation is considered the most cost-effective due to its informal, fast-tracking, and relationship-maintenance characteristics. Moreover, most negotiation studies assume that negotiators will decide to settle or otherwise based on rational analysis. This may not be true; negotiated settlement can only be achieved when negotiators have the intention to do so. To highlight the significance of intention to settle (ITS) in construction dispute negotiation (CDN), this study achieves two working objectives. First, a conceptualized ITS framework is developed. Using facilitators of negotiated settlement as the conceptual base, manifestations of intention to settle were developed. With data collected from construction negotiators, six underlying constructs of ITS were proposed by a principal component factor analysis (PCFA). The six constructs were then conceptualized as three intention forms, which were further validated by hierarchical confirmatory factor analysis (HCFA), thus developing the ITS framework. Second, the influence of ITS on the practice of negotiating behavior is examined. It is found that the ITS can foster cooperative negotiating behaviors that have been recognized as pivotal settlement agents. The findings not only confirm the indispensability of ITS in negotiation settlement but also embody negotiators’ intention by digging out its underlying formulations. The study contributes to the body of knowledge by conceptualizing sources of intention as technique based (preparation and integration), relationship based (goodwill and continuity), and cognition based (commitment and self-efficacy). In addition, the ITS framework is valuable for both negotiating parties to serve as an evaluation instrument. It enables management to review the readiness to settle before initiating a negotiation. During a negotiation, the manifestations can be applied as a checklist to gauge both parties’ settlement intentions, thereby assisting negotiators in formulating appropriate strategies. This study, therefore, makes both theoretical and practical advancements in construction dispute management.

Practical Applications

Most negotiation studies have focused more on tactics or strategies to enhance the overall utility of negotiation. However, negotiators’ choice of strategies may not solely depend on rational cost-benefit analysis because other behavioral factors such as settlement intention are of equal importance. This study affirms that having the intention to settle (ITS) is a necessary condition for a negotiated settlement. To go beyond this conventional understanding, how settlement intentions are formulated and operationalized are examined. Drawing from the wealth of relevant literature review and experience of construction professionals, a conceptual framework of ITS was developed and verified by statistical analysis. Categorically, settlement intention can be technique based, relationship based, and cognition based. Respectively, these forms are manifested by the negotiators’ behavior in dealing with the negotiation issues, the counterpart, and the self. The significance of ITS is further exemplified by its ability to foster cooperative negotiating behaviors. The ITS can be put into dispute management practice in two ways. First, the ITS framework can be used to assess both parties’ readiness and worthiness to commence negotiation. Second, ITS manifestations can be used as a checklist to gauge the level of intention to settle of the negotiating parties during a negotiation.

Robot-assisted assembly has shown great potential in modular construction for the future. However, as the complexity of prefabricated structural modules increases, the likelihood of unexpected robotic collisions also rises, such as unexpected collisions between robots and structural modules during the assembly process. Most robotic collision avoidance methods rely on known robot specifications, especially the work envelope, that is, the range profiles of movement created by a robot arm moving forward, backward, up, and down. In contrast, it is unknown what models of robots will be applied for future modular construction because of the rapid development of robot mechanical designs. The deep uncertainties related to future robot work envelopes can challenge any effort for planned robotic collision avoidance for robot-assisted modular construction. There is a pressing need for a simulation-based robot collision avoidance planning method for future robot-assisted modular construction that captures the deep uncertainties of work envelopes of future robots. As a result, this paper presents an active robotic collision avoidance method, called collision-free workspace and collision-avoidance path planning (CWCP), to tackle the unique robotic collision challenges in modular construction. First, CWCP uses an inverse kinematics Monte Carlo (IKMC) simulation in the design phase of a pipe skid module to scan a large number of possible robotic movement trajectories and the corresponding boundaries of the robotic space. This result can be used to identify a robust structural design with enough clearance for most robotic actions. Then, immediately before an operation, physics engine simulation is used to optimize the waypoints of the robotic arm movement to further eliminate the collision possibility given the established structural design and robotic specifications. The proposed method was tested with a simulated pipe skid installation task. The result showed that the proposed method helped strike a balance between a safe-tolerant design and robotic planning on site and thus is more suited for future robot applications with no prior knowledge about the work envelope specifications.

Building service robots rely on dense instrumentation of the building (e.g., Bluetooth beacons) or require high computational capabilities [e.g., simultaneous localization and mapping (SLAM)]. To overcome these limitations, studies explored a landmark-based localization and navigation approach based on inexpensive, computationally efficient, and easily configurable fiducial markers (e.g., AprilTags). However, context-specific assumptions were made regarding the fiducial marker characteristics and sensor configurations. Taking this forward, this study develops a generalized simulation-based approach to determine the optimal design characteristics of the fiducial marker network to achieve successful autonomous mobile indoor robot navigation with low instrumentation and computation. Different marker and camera parameters such as marker size and marker placement to optimize the density of fiducial markers (i.e., the ideal distance between subsequent markers) were investigated using Robot Operating Systems (ROS) and Gazebo (simulator) software. The simulation experiments focused on a specific robotic platform (i.e., TurtleBot3) and marker type (i.e., AprilTag). Results from the simulations suggested that with an increase in marker distance, the navigation success rate does not necessarily decrease. In addition, a marker size of 0.1 m performed the best in terms of navigation success rate (as high as 100% for a specific combination of marker height, marker size, and marker-to-marker distance). Future work aims to test this in a real-world setting to compare and analyze the simulated and actual performance of the robot. The proposed methodology is generic and can be applied to any mobile robotic platform, marker type, building type (e.g., residential), and application (e.g., construction progress monitoring).

Increased heat stress and air pollution are two major causes of health issues and productivity loss in the construction industry. This will be exacerbated by global warming and an increased possibility of extreme weather conditions in the future. Previous studies revealed that each 1°C rise in temperature reduces construction workers’ productivity by up to 2%. Also, it is found that a $10\mathrm{\mu g}/{\mathrm{m}}^3$ particulate matter sized 2.5 μm (PM2.5) fluctuation over 25 days lowers workers’ daily production by 1%. As an effective dust control and cooling strategy, water spraying is used by practitioners to mitigate the effect of air pollution and heat-related problems on construction workers. Considering low costs and high mobility, unmanned aerial vehicles (UAVs) could be considered as a potential alternative for ground-based, stationary water spraying systems. To this end, a case study approach is adopted in this project to investigate the feasibility of using nebulizer-retrofitted UAVs in controlling air pollution and reducing heat stress at construction job sites. The case study consists of two different residential job sites in the state of Utah: one in Salt Lake City and the other one in St. George. The main contribution of this study is to determine the effect of aerial water spraying on air temperature and pollution at job sites. The outcomes of this study show that the average wet bulb globe temperature (WBGT) decreases 1.7° during the flight phase compared to average values for preflight and postflight phases. The aerial water spraying technique yields better and more water-efficient results in decreasing temperature at job sites compared to the existing approaches, such as stationary fans. Moreover, the results of PM variations illustrate that the mean value of particulate change was significantly different between flight and preflight ($p=0.005$), and flight and postflight ($p<0.001$) modes. Future studies should include the deployment of multiple drones flying simultaneously at job sites to cover larger areas.

Construction projects often involve numerous decision-making activities during the design and operation phases. The decision-making process in the construction industry sometimes does not follow an organized manner, and often, decisions are made without having any systematic approach. An important element of a construction project is to select the most preferred building material. The construction operation process is known for its high energy intensity and negative environmental impacts on surrounding areas. Therefore, a quest for sustainable and value-generating building materials has long been overdue. This study proposes a multicriteria decision-making framework called Choosing by Advantages (CBA) to analyze some critical success factors associated with the decision-making process of a novel building material called mass timber. Mass timber is a sustainable and environmentally friendly building material, which has gained wide acceptance in European construction market. However, in the US, the majority of the construction practitioners are hesitant to use mass timber as a mainstream building material. This study aims to bridge the knowledge gap on mass timber and implement a decision-making framework to evaluate the constructability factors of mass timber in comparison with the two most traditional building materials in the US: concrete and steel. A questionnaire survey was distributed among the US construction practitioners experienced with all three materials. The outcomes of the study suggested that concrete is currently the most preferred and value-generating building material in the US construction industry based on eight predefined critical factors. Although mass timber is not the most preferred material for now, it has strong potential to be the most preferred building material in the future. The outcomes of the study will be helpful for the industry stakeholders to analyze the prospect of mass-timber building materials in the US construction industry. Furthermore, it will strengthen the decision-making process of construction operations.

The construction sector has not evolved with respect to the productivity of its processes and has not grown as expected in the last 50 years. Therefore, the Last Planner System (LPS) was created, which is based on commitment management to improve productivity. However, only one system has been proposed to manage commitments properly, the Commitment Management System (CMS). This research created an online intervention to measure the evolution and correlation of the commitment and maturity indicators of LPS and CMS, and the behavior of social network analysis (SNA) in four construction projects. The research was divided into four parts: (1) a literature review of commitment management in the field; (2) an online adaptation of the intervention; (3) an implementation and follow-up for 10 weeks in online format, observing the evolution of CMS, SNA, and LPS; and (4) an evaluation and analysis of how the online implementation impacts the indicators, establishing trend lines and relationships among the indicators. With this intervention, the indicators of commitment management, the level of implementation of the Last Planner System, the percentage of plan completed (PPC), and the interactions among the participants were analyzed, obtaining a sustained increase of as much as 7.02% in the first 5 weeks, with a slight decrease after the intervention in some cases, such as PPC ($-0.01\%$). The intervention showed that the percentage of compliance negotiation and agreements is a fundamental indicator for establishing a better PPC, because the SNA metrics also were affected indirectly, based on Spearman’s correlation analysis between all the measured indicators. This research demonstrated that online intervention works as a novel tool, without the need for researchers to be in the field, obtaining promising results during project monitoring.