System Dynamic Modeling To Study The Impact Of Construction Industry Characteristics And Associated Macroeconomic Indicators On Workforce Size And Labor Retention Rate

Limited skilled labor has been one of the greatest challenges facing the construction industry. The COVID-19 pandemic has further exaggerated the already strained construction labor market, leading to an additional negative impact. One of the major contributors to skilled labor shortages in construction is the issue of labor retention. Overall, this is a complex and dynamic situation that requires effective and efficient simulation-based techniques to capture the interdependent relationships that affect the performance of the construction labor market. This paper fills this knowledge gap. To this end, the authors used a multistep research methodology that involved (1) identifying factors that affect skilled labor shortages; (2) developing a one-module system dynamics model that consists of three interconnected systems (namely, construction labor market system, industry characteristics system, and economic conditions system); (3) initializing and calibrating the model to simulate the construction labor market; (4) validating the model through structural, behavioral, and calibration tests; and (5) conducting sensitivity analysis to simulate different parameters and examine their impact on skilled labor shortage. Among other findings, results indicated that all scenarios were successful in improving the conditions of the skilled labor market by increasing the workforce size and labor retention rate. Further, the model demonstrated that economic indicators have a more impactful influence on labor retention patterns compared with industry characteristics. The developed model offers industry practitioners, policymakers, business analysts, and other associated stakeholders a useful tool to test various scenarios including national-level economic policies and labor retention regulations that affect the construction skilled labor market. Consequently, this allows users to analyze the impact of variables such as fiscal policies, economic support plans, and construction spending strategies

Skilled Worker Shortage Across Key Labor-Intensive Construction Trades In Union Versus Nonunion Environments

Skilled labor plays a crucial role in ensuring that construction projects are completed on time, within budget, and to the required standards of quality and safety. However, the construction industry has been facing a labor shortage in recent years, which poses a significant challenge to the industry\u27s growth and sustainability. Therefore, it is important to examine the characteristics of the construction skilled labor market to understand the factors that contribute to the shortage of skilled workers and develop strategies to address the issue. This paper fills this knowledge gap. To this end, the authors (1) collected and processed project documentation in relation to 67 construction projects to identify key construction labor-intensive trades, (2) conducted an expert-based survey to collect data in relation to union participation rates and degrees of skilled labor shortages across the identified trades, (3) performed clustering analysis to examine the observed levels of labor shortage across the identified trades, (4) applied a binomial test to analyze the levels of union participation for each of the labor trades, and (5) used a chi-square test of independence to investigate the correlations between workforce location and union participation on the one hand and union participation and labor shortage on the other. As such, the authors identified 10 key labor-intensive trades. It was found that plumbing and electrical trades have the highest degrees of skilled labor shortage, whereas finishing work trades (i.e., plastering and painting, flooring, and waterproofing) had the lowest. Results also showed a significant correlation between high union membership rates and the availability of skilled workers in 3 of the 10 identified trades (i.e., ironworking, flooring, and waterproofing) and that union reach in urban locations is less than that in rural areas where workers are employed. Ultimately, this paper adds to the body of knowledge by offering a closer look into the construction skilled labor market. Such knowledge can be used to mitigate the current labor shortages

Circular Economy Strategies For Reducing Embodied Carbon In US Commercial Building Stocks: A System Dynamics Modeling Approach

Environmental concerns over embodied carbon - which is generated during the extraction, transportation, manufacturing, construction, and disposal of building materials - have been increasing as the industry shifts to renewable energy and grid decarbonization efforts prevail. Commercial buildings, a rapidly growing sector and a major source of embodied carbon, can contribute immensely to the national climate goals by transitioning into a circular economy (CE). Nevertheless, embodied carbon research is rather dispersed, with sparse data on the actual impact of different CE strategies and how they scale on nationwide commercial building stocks. To address this research need, the goal of this paper is to provide policymakers with a conceptual model that depicts the potential of CE strategy portfolios on embodied carbon reduction of commercial building stocks. Using US commercial buildings data from the Energy Information Administration, the authors (1) developed a systems dynamics model to conceptualize and serve as a baseline for calculating existing embodied emissions; and (2) evaluated the influence of various policy packages in terms of their overall emissions reduction potential over a planning horizon between the years 2022 and 2050. Findings of the study highlight the effectiveness of early design and construction CE interventions as compared to end-of-life strategies such as recycling, as well as traditional and business-as-usual approaches. Ultimately, results of the developed model can aid decision-makers to create multiple what-if scenarios for their policies, in addition to capitalizing on the most effective strategies for narrowing material loops and curbing embodied carbon emissions

Identifying Design-Build Decision-Making Factors and Providing Future Research Guidelines: Social Network and Association Rule Analysis

There is a dire need to rebuild existing infrastructure with strategic and efficient methods. Design-build (DB) becomes a potential solution that provides fast-tracked delivery as a more time and cost-efficient project delivery method. Past research studied factors influencing DB but without providing a holistic analytic approach. This paper fills this knowledge gap. First, a systematic literature review is performed using the preferred reporting items for systematic reviews and meta-analyses techniques, and a set of factors affecting DB projects are then identified and clustered, using k-means clustering, based on the whole literature discussions. Second, a graph theory approach, social network analysis (SNA), is conducted methodically to detect the understudied factors. Third, the clustered factors are analyzed using association rule (AR) analysis to identify factors that have not been cross-examined together. To this end, the findings of this research highlighted the need to investigate a group of important understudied factors that affect DB decision-making and procedures that are related to management, decision-making and executive methods, and stakeholder and team related aspects, among others. Also, while the majority of the existing research focused on theoretical efforts, there is far less work associated with computational/mathematical approaches that develop actual DB frameworks. Accordingly, future research is recommended to tackle this critical need by developing models that can assess DB performance, success, and implementation, among other aspects. Furthermore, since none of the studies evaluated DB while factoring in all 34 identified relevant factors, it is recommended that future research simultaneously incorporates most, if not all, these factors to provide a well-rounded and comprehensive analysis for DB decision-making. In addition, future studies need to tackle broader sectors rather than focusing over and over on the already saturated ones. As such, this study consolidated past literature and critically used it to offer robust support for the advancement of DB knowledge within the construction industry

Quantitative Holistic Assessment of Implementing Collaborative Planning Practices

Practices of collaborative planning - as related to novel project delivery methods, information technologies, lean construction, and supply chain practices - can impact the cost and schedule performance of projects in the architectural, engineering, and construction (AEC) industry. However, there is a lack of research providing a quantitative holistic assessment of implementing collaborative planning practices. This paper fills this knowledge gap. Using an interdependent multistep research methodology, the authors (1) analyzed a holistic literature-based list of collaborative planning risks using 46 responses from industry expert surveys; (2) calculated the criticality of these risks and compared the obtained results using Spearman rank correlation; (3) statistically analyzed the impact of these risks - based on a project-based survey that collected data from 65 different projects - using distribution fitting analysis and weighted average calculations; (4) developed a framework for predicting the cost and schedule performance impacts in relation to utilizing collaborative planning in the AEC industry; and (5) mathematically verified the research steps using an extreme condition test and sensitivity analysis, and practically validated the research output utilizing a case study example and the insights of 25 industry experts. Within the context of collaborative planning, this paper highlighted and discussed the top six risks that affect cost and schedule project performance: resistance to change, no early involvement of key project participants, lack of construction coordination, late and ineffective communication, lack of leadership, and absence of flexibility and coordination of design. Ultimately, this study provides a necessary and highly customizable metric for industry practitioners to manage their collaborative planning practices efficiently and improve their project performance

Agent-Based Modeling For Understanding Incentives Associated With Distributed Solar Generation

Distributed solar generation (DSG), such as residential photo-voltaic (PV) solar panels, offers many benefits to consumers and can improve the sustainability and resilience of the electric power infrastructure. As such, many local and national authorities implemented policy incentives, such as rebates and loans with reduced interests, to promote the adoption of DSG. However, the increasing penetration of DSG is creating new obstacles for system operators due to the uncertainty in forecasting electrical power demand. There is a need to investigate how incentives affect DSG adoption. To achieve that goal, a complex system-of-systems (SoS) model is developed using agent-based modeling (ABM). The model can simulate the complex relationships between utilities and generating companies in wholesale power markets where consumers can make an economic decision to install DSG systems. The model is tested using a case study based on a modified IEEE 6-bus system. The findings highlight the need to carefully consider the rippling effect of incentives on the grid to ensure sustainable DSG diffusion as demonstrated using ABM

Distributed Solar Generation: Current Knowledge And Future Trends

Distributed solar generation (DSG) has been growing over the previous years because of its numerous advantages of being sustainable, flexible, reliable, and increasingly affordable. DSG is a broad and multidisciplinary research field because it relates to various fields in engineering, social sciences, economics, public policy, and others. Developing a holistic understanding of the state of research related to DSG can be difficult. Motivated to provide that understanding, the goal of this paper is to explore current and emerging multidisciplinary research trends associated with DSG. To achieve that, (1) a large data set of approximately 66,000 publications was collected; (2) the papers were labeled using keywords for topics including Batteries and Storage, Solar, Complex Modeling, Machine Learning (ML) and Artificial Intelligence (AI), Resilience, Vulnerability, and Disasters, Policies and Incentives, Social Aspects, Economics, Smart Grid, Finance, Social Equity, Microgrid, and Virtual Power Plant ; and (3) the data set was analyzed using scientometric and social network analysis (SNA) in respect to publication counts, citation counts, and interconnectivity between the topics. Notable findings were analyzed to describe current and emerging trends. It was found that social equity has high citation counts contrasted by few publications, indicating a possible strong need for research. There is also rapidly growing research in ML and AI in the context of DSG during recent years. Other research topics, such as smart grids, have been attracting fewer publications. The results also highlight the need for multidisciplinary research connecting the topics. To conclude, future research is suggested to explore research needs in the areas of social aspects, social justice and equity, public policy and incentives, and ML/AI. The findings should benefit researchers and stakeholders with a holistic understanding of multidisciplinary DSG-related research and provide insight for planning new research and funding opportunities

Studying Contribution Of Associated Stakeholders In Risk Control Of Modularized Construction Under Different Project Delivery Methods: A Graph-Restricted Cooperative Games Approach

Modularization is associated with various project benefits including cost savings, schedule reduction, improved quality, among others. However, successful implementation of modular projects requires early collaboration and proactive management of various associated risks. Both project communication structures and early engagement of all project stakeholders have a great impact on risk mitigation efficiency in modular construction. The goal of this paper is to study the stakeholders\u27 contribution in risk control of modular construction projects under different delivery methods. To this end, the authors followed a multistep methodology including (1) literature analysis to assign 17 modular risks among the various project stakeholders; (2) Monte-Carlo simulation to valuate risk control contribution associated with each stakeholder and their subset coalitions; and (3) cooperative game theory to compute the risk control contribution of each stakeholder under three different delivery methods including design-bid-build (DBB), design-build (DB), and integrated project delivery (IPD). The findings show that suppliers and contractors have the highest contribution in risk control when compared to owners and designers. Furthermore, the results indicated that IPD increases risk control capacity of contractors and subcontractors due to the ability for open communication between owner, designer, and contractor with the modular suppliers or subcontractors. Ultimately, this paper contributes to the body of knowledge by offering an unprecedented framework that captures the impact of delivery methods on risk control capabilities of stakeholders in modular construction projects

A Proactive Risk Assessment Framework to Maximize Schedule Benefits of Modularization in Construction Projects

Various studies developed models and decision support tools to assess the feasibility and optimize the use of modularization. However, none has explored the schedule benefits of modular construction. This paper fills this knowledge gap. To this end, the authors completed the following: (1) analyzed the criticalities of the various modular risk factors on potential schedule savings using data collected from 48 industry professionals, (2) investigated the schedule savings associated with the use of modularization using data collected from 68 modular construction projects, and (3) developed an interrelated assessment model to calculate the schedule savings of using modularization. The provided model was verified using extreme conditions, surprised behavior tests, and sensitivity analysis. Also, it was validated by industry experts. The results show that design and engineering issues, regulatory and organizational matters, and resources and technology aspects are among the top parameters affecting schedule savings of modularized construction projects. This research adds to the body of knowledge by developing a decision-making benchmark that can assist project stakeholders in making proactive decisions, suitable mitigating strategies, and early corrective actions to ensure maximized capitalization on the schedule benefits of modularization in the construction industry

A System-of-Systems Model to Simulate the Complex Emergent Behavior of Vehicle Traffic on an Urban Transportation Infrastructure Network

Transportation agencies face escalating challenges in forecasting the traffic demand. Traditional prediction methods focused on individual transportation sectors and failed to study the inter-dependencies between the different transportation systems. Hence, there is a need for more advanced and holistic modeling techniques. To this end, this paper models and analyses an urban transportation system-of-systems incorporating seven various systems: population and GDP, CO2 emission, gasoline price and total vehicle trips, traffic demand, public and private transportation, transportation investment, and traffic congestion. Accordingly, this research simulates transportation networks as a collection of task-oriented systems that combine their resources to form a complex system with increased functionality. The goal of this paper is to understand the traffic complex behavior of urban transportation networks and to study the interdependencies between the different variables. The proposed framework could be implemented to any urban city, county, state, or country. The developed model incorporates a hybrid modeling approach that includes: logistic model, system dynamics, stochastic cellular automata, chaos theory, and Lotka-Volterra model. The final model is demonstrated using a case study. The contribution of this paper lies in modeling the transportation network as a dynamic system of systems rather than as static model as provided in previous studies