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

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

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

Data-Driven Analysis Of Construction Bidding Stage-Related Causes Of Disputes

Construction bidding is a complex process that involves several potential risks and uncertainties for all the stakeholders involved. Such complexities, risks, and uncertainties, if uncontrolled, can lead to the rise of claims, conflicts, and disputes during the course of a project. Even though a substantial amount of knowledge has been acquired about construction disputes and their causation, there is a lack of research that examines the causes of disputes associated with the bidding phase of projects. This study addresses this knowledge gap within the context of infrastructure projects. In investigating and analyzing the causation of disputes related to the bidding stage, the authors implemented a multistep research methodology that incorporated data collection, network analysis (NA), spectral clustering, and association rule analysis (ARA). Based on a manual content analysis of 94 legal cases, the authors identified a comprehensive list of 27 causes of disputes associated with the bidding stage of infrastructure projects. The NA results indicated that the major common causes leading to disputes in infrastructure projects comprise inaccurate cost estimates, inappropriate tender documents, nonproper or untimely notification of errors in a submitted bid, nonproper or untimely notification of errors in tender documents, and noncompliance with Request for Proposals\u27 (RFP) requirements. Upon categorizing and clustering the causes of disputes, the ARA results revealed that the most critical associations are related to differing site conditions, errors in submitted bids, unbalanced bidding, errors in cost estimates, and errors in tender documents. This study promotes an in-depth understanding of the causes of disputes associated with the bidding phase within the context of infrastructure projects, which should better enable the establishment of proactive plans and practices to control these causes as well as mitigate the occurrence of their associated disputes during project execution

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

Data-Driven Analysis Of Progressive Design Build In Water And Wastewater Infrastructure Projects

The United States has invested heavily in water and wastewater infrastructure projects to address growing demand and aging systems. To ensure the effective delivery of these projects, agencies are shifting toward alternative delivery methods such as progressive design build (PDB), which has demonstrated accelerated schedule and enhanced cost performance across the literature as well as multiple projects compared to traditional DB. This has raised a need for evaluating PDB\u27s state of adoption and performance in the water and wastewater sector. To this end, the authors: (1) conducted descriptive and statistical analyses of the 21 PDB water and wastewater projects available on the Design-Build Institute of America database evaluating their characteristics and performance metrics; (2) investigated the frequency of materialized risks impacting schedule and cost in these projects; and finally (3) identified the key adoption drivers and challenges for PDB in the water and wastewater sector by triangulating findings from the studied narratives with a literature and practice review. Results revealed that 71% and 57% of the investigated projects were completed on or before the contracted schedules and costs, respectively. From the studied project narratives, owner-led changes and COVID-19 impacts were the most frequently encountered risks. Also, it was shown that project planning and risk management drivers were the most influential causes for PDB adoption, whereas legal and contractual restrictions as well as the owner\u27s mindset and culture-related concerns were the most pressing challenges. This study contributes to the body of knowledge by delivering managerial insights through an aggregated snapshot of PDB implementation in the water and wastewater sector. Ultimately, the provided managerial insights can assist stakeholders in making better-informed decisions by weighing the advantages and challenges of PDB identified in this research against more traditional delivery approaches

An Agent-Based Model to Study Competitive Construction Bidding and the Winner\u27s Curse

Reverse auction theory is the basis for competitive construction bidding process. The lowest bid method is utilized for selecting contractors in public projects. The winning contractor having the lowest bid value could be cursed when the submitted bid value results in negative profits. This is caused by many factors such as the contractor\u27s estimation accuracy and markup. This is addressed in this paper by providing a model simulating the construction competitive bidding and the occurrence of the winner\u27s curse. To this end, the authors show the extent to which the winner\u27s curse affects the status of contracting companies. The objectives are to understand the characteristics of the competitive bidding phase in construction and to study the behavior of contractors subject to competitive bidding and the occurrence of the winner\u27s curse. As such, the authors implemented a two-step methodology that incorporates (1) developing a general simulation model involving a population of contractors and projects using agentbased modeling for the competitive bidding process and (2) analyzing the results of the simulation model. This model should provide a better understanding to the construction profession as in contractors, project owners and Departments of Transportation of how decisions are made in this bidding environment

Understanding Collaboration Requirements for Modular Construction and their Cascading Failure Impact on Project Performance

Effective Implementation of Modularization Demands Close Collaboration among the Various Project Stakeholders Due to the Distinct and Complex Needs of Such Construction Method. in Fact, Lack of Adequate Collaboration is One of the Main Factors Impacting Modular Construction Performance. Despite that, No Previous Study Has Yet Addressed Collaboration Requirements in Modular Construction and their Cascading Failure Impact on Project Performance. This Paper Fills Such a knowledge Gap. to This End, the Authors Followed a Multistep Research Methodology. First, Systematic Literature Analysis Was Performed to Identify the Factors Impacting Collaboration and the Impacted Modular Risks as Well as their Cause-Effect Relationships. Second, Two Surveys Were Distributed to Collect (1) Importance Weights and Failure Probabilities for the Collaboration Factors; and (2) Failure Probabilities and Performance Impacts for the Modular Risks. Third, Network Analysis Was Conducted using In- and Out-Degree Centralities to Determine the Most Influential and Sensitive Aspects in Terms of Collaboration. Fourth, Independent Cascade Modeling Was Performed to Capture the Cascading Failure Effect of Various Collaboration Aspects on Project Performance. Ultimately, a Total of 25 Factors Were Found to Impact Collaboration Categorized under Four Themes, Including (1) Project Organization and Control, (2) Stakeholders\u27 Relationships and Characteristics, (3) Information Sharing, Documentation, and Technologies, and (4) Design and Construction Planning. Furthermore, 10 Modular Operation Risks Were Found to Be Impacted by Collaboration in Construction Projects. Although the Most Influential Factors Were Related to Information Sharing, Documentation, and Technologies, the Most Sensitive Factors Fell within the Design and Construction Planning. Most Importantly, Results Show that Inadequate Collaboration during Design and Construction Planning Can Lead to 70.6% Direct Growth in Schedule and Cost of Modularized Projects. This Paper Contributes to the Body of Knowledge by Offering an Unprecedented Framework that Investigates Collaboration Requirements in Modular Construction and their Interdependencies

A System Dynamics Approach for Study of Population Growth and the Residential Housing Market in the US

The US Consensus bureau estimated the total construction spending at 1,320,305 Million Dollars, in February 2020, with an increase of 1.1% since last February. The construction market is large, and risky. Prediction of the market behavior, for several years ahead, is needed in order to take strategic investment decision for long and expensive projects. The goal of this research is to study the relationship between population growth and the housing market. To that end, a system dynamics model is developed. System dynamics is a top-down approach that starts with the high-level behavior of a complex system to simulate the behavior of that system over time. The developed model simulates the housing market by matching the population growth with the housing demand in monthly time steps. As such, the parameters of the developed model include birth rate, life expectancy, immigration, emigration, and construction seasonality. Using these parameters, the model simulates the population size and demand for housing. For validation, the outputs of the model are compared with real-life data for the US. When complete, the model should assist market researchers in simulating the housing market. This research benefits large real estate developers, construction companies, governmental and financial agencies