An Integrated Decision Support Model Based on BWM and Fuzzy-VIKOR Techniques for Contractor Selection in Construction Projects

Performance Prediction of Construction Projects Based on the Causes of Claims: A System Dynamics Approach

Conflict in human relations is unavoidable; therefore, it can occur in construction projects that are full of many human relationships. These conflicts can lead to claims if interlocutors do not agree. The main result of the claims is the delay and overrun of costs in construction projects. Additionally, poor management of claims affects the success of construction projects and their budget and schedule. Moreover, controlling claims ensures the successful completion of construction projects and minimizes delays and disputes. This study sought to improve the project performance by ranking the causes of claims and examining their impacts on Key Performance Indicators (KPI) using the combined AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) method. Given that construction projects are constantly evolving and becoming more complex and have many linear and nonlinear relationships and structures, these projects can be considered as a kind of system dynamics. The system dynamics have been used for nearly four decades to analyze and improve the performance of construction projects. This study attempted to provide a model with a system dynamics approach to predict the performance of construction projects based on the reasons for the claim. In this model, the most important performance indicators, interactions between them, and the relationship of these indicators with the reasons for claiming were simulated. The results showed that delays are the most important reasons for making claims, and they have a great impact on the KPIs such as scheduling, sustainability, customer satisfaction, quality, and team satisfaction, respectively. In the modeling and simulation discussion in this study, the proposed model simulated the dependencies between the KPIs of construction projects and their changes over time and provides a better understanding of how performance indicators interact with each other. Additionally, it can be used as a basis for simulating different management policies to find the best solution to prevent and correct the negative effects of poor performance.