A Comparative Analysis of Cost Estimation Accuracy in Traditional Versus Digital BIM-Based Quantity Surveying

Thesis Abstract

Effective cost estimation is a fundamental component of successful project management in the construction industry, directly influencing project viability, budgeting, and stakeholder confidence. Traditionally, quantity surveying practitioners rely on manual methods and 2D drawing interpretations, which often introduce inaccuracies due to human error, incomplete data, and the limitations of conventional techniques. The advent of Building Information Modeling (BIM) has introduced digital tools promising enhanced precision and efficiency in cost estimation processes. However, comparative analyses quantifying the accuracy benefits of BIM over traditional methods remain limited, especially in contexts where resource allocation for digital adoption is substantial. This study aims to systematically evaluate and compare the accuracy of cost estimates generated through traditional quantity surveying methods and those produced using digital BIM-based techniques within the construction industry. The specific objectives include (1) to assess the accuracy of cost estimates derived through conventional quantity surveying practices; (2) to evaluate the accuracy of BIM-based cost estimates; (3) to identify and analyze the factors influencing estimation accuracy in both approaches; and (4) to provide recommendations for optimizing cost estimation practices based on the findings. The research employs a quantitative comparative research design, grounded in a positivist paradigm, to facilitate objective measurement of estimation accuracy in real-world projects. The population comprises 120 construction projects undertaken within the last three years across various sectors, with a focus on medium to large-scale projects. A stratified random sampling technique is used to select a sample of 60 projects—30 utilizing traditional estimation methods and 30 employing BIM-based tools—ensuring diversity across project types and contract sizes. Primary data collection relies on structured questionnaires and in-depth project document analysis, including tender documents, cost breakdowns, and final project cost reports. Data collection instruments are validated through expert review and pilot testing, ensuring content validity and internal consistency, with reliability confirmed through Cronbach’s alpha coefficients exceeding 0.8. To quantify estimation accuracy, the study calculates the percentage deviation of estimated costs from actual project final costs. The data analysis involves descriptive statistics, independent t-tests to compare mean deviations between the two groups, and multiple regression analysis to identify significant predictors of estimation accuracy; all analyses are executed within the SPSS software environment. The expected findings suggest that BIM-based estimates exhibit significantly lower deviation percentages compared to traditional estimates, indicating higher accuracy. Additionally, factors such as project complexity, data completeness, and software proficiency are anticipated to significantly influence estimation outcomes. These results are expected to substantiate the hypothesis that digital BIM tools enhance cost estimation precision, aligning with the Technology Acceptance Model and the Rational Choice Theory, which posit that technological innovations improve productivity and decision-making accuracy. This study contributes to the existing body of knowledge by providing empirical evidence of the quantifiable benefits of BIM technology in cost estimation. It extends theoretical understanding by integrating technological and process-based factors influencing accuracy, offering a nuanced perspective on the mechanisms driving improvements. Practically, the research offers critical insights for quantity surveyors, contractors, and policymakers regarding resource investment and methodological shifts towards digital tools. The conclusion emphasizes the superiority of BIM-based estimation accuracy and recommends increased training, investment in BIM technologies, and development of standardized digital estimation protocols. It advocates for further research into the integration of BIM with other project management tools and explores longitudinal studies to assess the ongoing impact of digital transformation on cost management practices in construction. Overall, the study underscores the imperative for industry stakeholders to adopt innovative digital methods to improve project cost control, reduce financial risks, and enhance competitiveness within the sector.