Implementing Building Information Modeling (BIM) for Cost Estimation Accuracy Enhancement

Thesis Abstract

The accuracy of cost estimation remains a critical challenge in construction project management, often leading to project overruns, budget shortfalls, and compromised project quality. Traditional estimation methods exhibit limitations in capturing the complexities and dynamic nature of modern construction projects, thereby necessitating innovative technological solutions to enhance precision and efficiency. Building Information Modeling (BIM) has emerged as a transformative technology capable of integrating multi-dimensional project data and facilitating improved cost forecasting; however, its systematic implementation specific to cost estimation processes remains underexplored. This study aims to investigate how BIM can be effectively implemented to enhance the accuracy of cost estimation in construction projects, with specific objectives to evaluate current estimation challenges, determine BIM integration strategies that influence estimation accuracy, and develop a framework for optimal BIM use in cost management. The research adopts a mixed-methods design comprising both qualitative and quantitative approaches. The population encompasses construction project stakeholders—including architects, quantity surveyors, project managers, and contractors—in the metropolitan region of the city, with an accessible population size of approximately 200 professionals. A stratified random sampling technique is employed to select a sample of 80 participants, ensuring representation across different project roles and experience levels. Data collection involves semi-structured interviews for qualitative insights and structured questionnaires for quantitative data, utilizing validated instruments adapted from prior studies on construction technology adoption and cost estimation accuracy. The validity of instruments is confirmed through expert reviews and pilot testing, with reliability assessed via Cronbach’s alpha coefficients exceeding 0.8. Quantitative data are analyzed using multiple regression analysis to identify key factors influencing estimation accuracy, while thematic analysis is employed for qualitative interview transcripts to elucidate perceived barriers and facilitators to BIM integration. Additionally, Structural Equation Modeling (SEM) tests the relationships between BIM implementation variables and cost estimation performance. The study also incorporates a comparative analysis of project data before and after BIM implementation on a sample of 15 recent projects, applying paired t-tests to assess statistical improvements in estimation precision. Expected findings suggest that integrated BIM workflows significantly improve cost estimation accuracy by enhancing detail clarity, reducing estimation errors, and fostering cross-disciplinary collaboration. The study anticipates identifying specific BIM features—such as real-time quantity updates and parametric modeling—as critical enablers of precise cost forecasting. Furthermore, the research is expected to reveal organizational and technical barriers to BIM adoption, including resistance to change, lack of skilled personnel, and interoperability issues, providing a comprehensive understanding of implementation nuances. This research contributes novel insights into the empirically supported relationship between BIM integration and cost estimation performance in the construction sector, filling existing gaps in the literature concerning practical implementation strategies and contextual challenges. It offers a framework for construction firms and policymakers to optimize BIM deployment for cost control, emphasizing the role of technology in fostering sustainable project delivery. The main conclusion underscores that effective BIM implementation, when tailored to project-specific contexts and supported by appropriate training and standards, substantially enhances cost estimation accuracy. Based on these findings, the study recommends the development of standardized BIM protocols for cost estimation processes, investment in capacity building for construction professionals, and the integration of BIM tools into regulatory and contractual frameworks to facilitate systematic adoption across projects. Future research should focus on longitudinal case studies to evaluate long-term impacts and explore the integration of emerging technologies such as artificial intelligence and machine learning in BIM-driven cost management.