2025, Vol. 4, Issue 2, Part A

Background: The construction industry sector faces persistent challenges in ensuring accurate progress monitoring, timely deviation detection, and efficient decision-making. Digital twin (DT) technology, which integrates Building Information Modelling (BIM), Internet of Things (IoT) sensors, and real-time analytics, has emerged as a transformative tool for enhancing project monitoring capabilities.

Objective: This study aimed to assess the effectiveness of digital twins in improving monitoring accuracy, schedule adherence, anomaly detection speed, and decision latency in construction projects compared with traditional monitoring approaches.

Methods: A mixed-methods design was employed across six DT-enabled and six traditionally monitored construction projects. Quantitative analysis included descriptive statistics, Welch’s t-tests, and Cohen’s d to evaluate performance indicators. PLS-SEM was applied to model the impact of data quality, integration maturity, and stakeholder training on DT integration and performance. Qualitative interviews with project managers, engineers, and site supervisors provided contextual insights into DT adoption factors and operational impacts.

Results: DT-enabled projects demonstrated significantly higher monitoring accuracy, lower schedule variance, and markedly reduced deviation detection and decision latency compared to conventional methods. Effect sizes were large across all metrics, indicating robust performance gains. Structural equation modelling confirmed that data quality, integration maturity, and training exerted significant positive effects on DT adoption and monitoring outcomes.

Conclusion: The study confirms that DT implementation substantially enhances construction monitoring performance. Beyond technological deployment, organizational readiness—including data management protocols, integration maturity, and workforce capacity building—is crucial for maximizing DT benefits. Practical recommendations include establishing standard deviation detection workflows, aligning DT with existing BIM processes, ensuring data interoperability, and institutionalizing training frameworks. DT adoption offers a strategic pathway toward predictive, agile, and data-driven construction management, setting a new benchmark for real-time project control.