Real-Time Construction Cost Control through BIM-Based Financial Digital Twins: A Framework for Predictive Budget Management

Mohammad Elahi

Authors

Mohammad Elahi M.Sc., Department of Civil Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran https://orcid.org/0009-0008-4954-1445

Abstract

This study aimed to develop and empirically evaluate a BIM-based financial digital-twin framework for real-time construction cost control by examining how BIM information integration, real-time data connectivity, automated financial synchronization, predictive cost analytics, early cost-variance detection, and decision-support capability contribute to predictive budget management and construction cost-control effectiveness. This applied, quantitative, cross-sectional study was conducted among construction professionals in Tehran, Iran. A total of 218 valid responses were obtained from project managers, BIM managers and coordinators, planning and project-control engineers, cost engineers, quantity surveyors, financial managers, construction managers, and consulting engineers with relevant experience in BIM, cost management, and digital construction. Data were collected using a structured questionnaire measuring BIM information integration, real-time data connectivity, automated financial synchronization, financial digital-twin capability, predictive cost analytics, early cost-variance detection, decision-support capability, predictive budget management, and construction cost-control effectiveness. The measurement and structural models were assessed using partial least squares structural equation modeling in SmartPLS, with reliability, convergent validity, discriminant validity, multicollinearity, path coefficients, effect sizes, explained variance, predictive relevance, and model fit examined. BIM information integration (β = 0.286, p < 0.001), real-time data connectivity (β = 0.318, p < 0.001), and automated financial synchronization (β = 0.354, p < 0.001) significantly predicted financial digital-twin capability. Financial digital-twin capability strongly predicted predictive cost analytics (β = 0.647, p < 0.001) and early cost-variance detection (β = 0.423, p < 0.001). Predictive cost analytics significantly improved decision-support capability (β = 0.462, p < 0.001), early cost-variance detection (β = 0.391, p < 0.001), and predictive budget management (β = 0.437, p < 0.001). Predictive budget management was the strongest direct predictor of construction cost-control effectiveness (β = 0.521, p < 0.001). The model explained 69.3% of the variance in predictive budget management and 71.8% of the variance in construction cost-control effectiveness, with satisfactory model fit (SRMR = 0.057). The findings demonstrate that BIM-based financial digital twins can substantially improve construction cost control when integrated project data are continuously synchronized and transformed into predictive financial intelligence, early variance warnings, and actionable managerial support, thereby enabling a shift from retrospective cost reporting toward proactive and real-time budget management.

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