Construction dispute mitigation through multi-agent based simulation and risk management modeling

The construction industry is regarded not only as a backbone of the nation’s economy but also as an integral indicator of its efficiency and effectiveness. However, as a result of the risks and complexities that are naturally inherent with construction projects as well as the diverging interests of the parties involved, claims and disputes could be considered an unavoidable consequence of the construction processes. In fact, over the years, the frequency and severity of claims and disputes have significantly increased to the extent that the estimated total annual cost of construction conflicts and disputes in the U.S. is $5 billion. That said, the main goal of this dissertation was to develop an integrated and coherent methodology for mitigation of construction disputes through both, multi agent based simulation concepts and risk management modeling principles. In this regard, the associated work carried out under this research has: (1) developed an innovative method for using logical induction decision support in construction claims and disputes; (2) created a multi agent system for construction dispute resolution (MAS-COR) that will simulate legal discourse in construction disputes; (3) developed a new method for addressing the issue of risks in the construction industry using portfolio insurance; and (4) created an innovative way for mitigating negative effects of contractor’s construction claims and disputes using a risk retention approach. It is conjectured that the attainment of the aforementioned objectives, as detailed under this dissertation, would mitigate the negative effects of claims and disputes in the construction industry and thus, have a positive impact on the contracting parties, their projects, the construction industry as a whole, and consequently, the nation’s economy

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

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

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

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

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