Reconceptualizing Quantity Take-Off and Cost Estimation through Building Information Modeling: A Theoretical and Empirical Synthesis of BIM-Based Estimation Practices

Abstract

The accuracy of quantity take-off and cost estimation has long been recognized as a cornerstone of effective construction project management. Traditional estimation methods, largely dependent on two-dimensional drawings and manual interpretation, have historically been prone to human error, inefficiency, and fragmentation across project phases. In response to these persistent challenges, Building Information Modeling has emerged as a transformative paradigm, offering data-rich, object-oriented representations of built assets that promise enhanced precision, transparency, and integration. This research article develops a comprehensive, publication-ready theoretical and empirical synthesis of BIM-based quantity take-off and cost estimation practices, grounded strictly in established scholarly literature. Drawing upon foundational cost estimation theory, early BIM adoption studies, comparative analyses between traditional and BIM-based workflows, and recent advances in semantic data processing, this study critically examines how BIM reshapes estimation logic, professional roles, and decision-making processes. The article elaborates in depth on methodological shifts, organizational implications, technological constraints, and data governance issues associated with BIM-enabled estimation. Particular emphasis is placed on understanding the mechanisms through which BIM reduces estimation errors, the conditions under which these benefits are realized, and the structural limitations that continue to inhibit full automation and reliability. Through descriptive and interpretive analysis, this work identifies persistent gaps between theoretical potential and practical implementation, highlighting the influence of modeling standards, data semantics, estimator expertise, and institutional readiness. The study concludes that while BIM fundamentally redefines quantity take-off as a dynamic, model-driven process rather than a static measurement task, its effectiveness depends on disciplined modeling practices, cross-disciplinary collaboration, and the integration of complementary data analytics techniques. By synthesizing insights across diverse but interconnected research streams, this article contributes a holistic academic framework for understanding BIM-based estimation as both a technological system and a socio-technical transformation within the construction industry.