N, H. K. and Padala, S. P. S. (2026) A bibliometric review of digital twin-enabled technologies for construction project monitoring and control. Built Environment Project and Asset Management, 16(3), pp. 441-460. ISSN 2044-124X
Abstract
Purpose – Construction projects often suffer from delays, cost overruns, and fragmented control due to isolated implementation of Computer Vision (CV), Internet of Things (IoT), Building Information Modelling (BIM), and Machine Learning (ML). This study addresses this gap by proposing a comprehensive Digital Twin framework that integrates these technologies into a unified system for real-time monitoring and predictive control of labor, material, equipment, and activity (LMPA). Design/methodology/approach – A four-phase bibliometric approach was followed: (1) retrieving 2, 032 studies (2014–2024) from the search database; (2) screening through multi-level filtering to retain 534 relevant papers; (3) network analysis and mapping keywords in Gephi and forming thematic clusters using the Louvain algorithm; and (4) identifying the research gap of fragmented CV, IoT, BIM, and ML applications and developing a Digital Twin framework for real-time LMPA control. Findings – Nineteen functional clusters were identified across the four domains: CV (Visual Understanding, Edge Hardware, Metrics, LMPA Algorithms, Analytics), IoT (Sensors, Transmission, Edge Devices, Signal Processing, Dashboards), BIM (Foundations, 4D/5D Control, Progress Tracking, As-Built Alignment), and ML (Data Preparation, Model Training, Forecasting, Optimization, Real-Time Feedback). These form a Digital Twin framework for real-time, closed-loop project monitoring and control. Originality/value – Unlike prior reviews focusing on single technologies or static visualisation, this study integrates CV, IoT, BIM, and ML into a single, evidence-based Digital Twin framework tailored to closed-loop LMPA control.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | building information modelling; computer vision; construction monitoring; digital twin; internet of things; machine learning |
| Index terms: | implementation, forecasting, construction project, cost overrun, computer vision, internet, hardware, mapping, network analysis, database, building information modelling, machine learning, predictive control, methodology, dashboard, digital twin, monitoring, visualization, foundations, evidence, progress tracking, screening, gephi, project monitoring |
| Subjects: | data management, data analysis and analytics, artificial intelligence, financial and cost management, prediction and forecasting, research methods, data science, evaluation and assessment methods, control systems, project controls, contractual arrangements, management, production management, computing systems, computer vision, design practice, spatial and geospatial analysis, digital engineering, structural engineering, information systems, computer hardware |
| Topics: | Digital Applications, Research Practice, Time Control, Project Management, Site Management, Design Practice, Cost Management, Human Resources, Engineering Principles, Procurement |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here