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

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 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

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