Predictive digital monitoring of construction resources: an integrated digital twin solution

Purpose 

Optimising resource utilisation on construction sites is essential for achieving key performance indicators related to cost, time and sustainability. While recent advances have explored the integration of 4D building information modelling (BIM) with technologies such as the Internet of things (IoT) and immersive tools, widespread adoption remains limited due to system complexity, scalability and integration challenges. This paper introduces the digital twins–based site resource monitoring (DTSRM) system as an integrated solution to support efficient and sustainable construction resource management.

Design/methodology/approach 

A research gap was identified through a critical literature review. A predictive DTSRM system was then developed in multiple phases, including the creation of digital twin (DT) models and components such as ontology, IoT networks and machine learning (ML) algorithms. The system was tested using IoT simulators and C# scripts to ensure sensor data integration and functional validity. Synthetic datasets, designed to realistically simulate construction site conditions, were generated using Python to evaluate the system’s performance. 

Findings 

DTSRM offers a real-time, integrated approach by collecting data from construction equipment and material storage through IoT sensors visualised within the BIM model. ML techniques enable the prediction of equipment productivity and the tracking of material consumption and inventory levels. This allows for timely, data-driven decisions that minimise delays and excess inventory costs. 

Practical implications 

DTSRM enables project stakeholders to prioritise resource allocation across multiple sites based on live productivity and inventory data. By reducing waste and improving efficiency, it directly supports circular economy principles in construction. The DTSRM ontology, though currently focused on equipment and materials, can be extended to include human resources for productivity and health and safety monitoring.

Originality/value 

This research offers a practical, scalable solution that integrates DT, BIM, IoT and AI technologies to monitor construction resources in real time. Unlike many existing frameworks, DTSRM explicitly contributes to the circular economy by promoting resource efficiency, minimising waste and supporting informed decision-making across the construction lifecycle.