Development of a BIM-based Cost Estimation Framework for Construction Projects

Thesis Abstract

In the evolving landscape of construction management, the integration of Building Information Modeling (BIM) has emerged as a transformative approach to enhance project planning, execution, and control, particularly in the domain of cost estimation. Despite its potential, the adoption of BIM for precise and efficient cost estimation remains limited by the lack of standardized frameworks that seamlessly incorporate detailed cost data, thereby impeding accurate budgeting, resource allocation, and stakeholder collaboration. This study aims to develop a comprehensive BIM-based cost estimation framework tailored for construction projects, with specific objectives to identify critical BIM components influencing cost accuracy, formulate a standardized process model for cost estimation within BIM environments, and validate the framework through empirical application. Employing a mixed-method research design, the study incorporated qualitative and quantitative approaches to ensure robust framework development and validation. The qualitative phase involved thematic analysis of semi-structured interviews with thirty-five industry experts, including quantity surveyors, BIM managers, and construction project coordinators, to extract key elements impacting cost estimation accuracy within BIM models. The quantitative phase consisted of a survey distributed to 120 construction firms across the region, achieving a 75% response rate to gather numerical data on current practices and perceived barriers. To empirically validate the proposed framework, a case study approach was adopted, involving three ongoing construction projects where the framework was implemented and performance metrics were recorded. Data collection instruments included semi-structured interview guides, structured questionnaires, and project documentation analysis. The reliability and validity of the instruments were ensured through pilot testing, Cronbach’s alpha coefficient (?=0.84), and expert reviews. The primary analytical techniques employed encompassed thematic analysis for qualitative data to identify recurring themes and constructs, descriptive statistics to summarize survey data, and multiple regression analysis to determine the influence of identified BIM components on cost estimation accuracy. Additionally, ANOVA tests were conducted to compare differences in cost estimation performance before and after framework implementation. The case study data were analyzed through comparative performance measures and effectiveness assessments. The anticipated findings are that integrating specific BIM elements—such as detailed object attributes, cost databases, and automation features—significantly improves the accuracy and consistency of cost estimates. The framework is expected to delineate a clear procedural sequence for integrating cost data within BIM models, encompassing data input, model analysis, and visualization tools, thereby facilitating standardized workflows across projects. The empirical validation is projected to reveal measurable reductions in cost estimation errors, averting potential budget overruns and enhancing project delivery efficiency. The study also anticipates identifying barriers to implementation, such as technological skills gaps and interoperability issues, which influence the framework’s adoption. This research contributes to the existing body of knowledge by bridging the gap between theoretical BIM applications and practical cost estimation needs, offering a validated, replicable framework that enhances cost management practices in construction. The developed framework serves as a strategic guide for practitioners seeking to harness BIM technologies for more reliable and transparent cost estimation processes, aligning with contemporary industry standards and digital transformation objectives. In conclusion, the study recommends targeted capacity-building initiatives, development of integrated BIM-cost databases, and further longitudinal research to assess long-term impacts of framework adoption on project performance. Ultimately, the findings aim to promote best practices in BIM-enabled cost estimation, fostering industry-wide efficiency and sustainability in construction project management.