PERFORMANCE MEASUREMENT OF PAVEMENT CONSTRUCTION PROJECT MANAGEMENT THROUGH STRUCTURAL EQUATION MODELLING

Abstract

The performance measurement of pavement construction projects is crucial for ensuring efficiency, durability, and long-term sustainability. Given the complexity and dynamic nature of construction projects, traditional performance evaluation methods often fall short in capturing the multifaceted factors influencing project outcomes. This dissertation employs Structural Equation Modeling (SEM) to develop a comprehensive framework for assessing the critical success factors (CSFs) that impact pavement construction management. The research integrates advanced statistical methodologies, including Partial Least Squares SEM (PLS-SEM) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to quantify relationships between key performance indicators (KPIs) and project success. A mixed-methods approach is utilized, incorporating qualitative and quantitative data collection techniques. Surveys and structured interviews with industry experts, project managers, and stakeholders provide empirical data, while case studies of diverse pavement construction projects offer real-world Practical Implementation of the Model of the proposed framework. The study identifies essential performance drivers, including stakeholder engagement, quality control measures, sustainability practices, risk management strategies, and the integration of emerging technologies such as intelligent compaction and data-driven decision-making tools. The findings underscore the importance of adopting systematic performance measurement frameworks in pavement construction to mitigate risks, improve decision-making, and enhance overall project efficiency. The developed SEM-ANFIS model serves as a predictive tool for evaluating pavement project performance, facilitating data-driven decision-making for industry professionals and policymakers. By addressing gaps in the existing literature, this study contributes to the advancement of pavement construction management by offering an empirically validated approach to performance assessment. Furthermore, the research provides actionable recommendations for improving construction project outcomes through enhanced planning, resource allocation, and stakeholder coordination. The proposed model offers a replicable and scalable methodology for assessing pavement construction project performance across different contexts, enabling more effective infrastructure planning and investment. Ultimately, this study seeks to bridge the gap between theoretical research and practical implementation, equipping industry stakeholders with the tools necessary to optimize pavement construction processes and achieve long-term infrastructure sustainability.