Throughout the building's entire lifetime, the construction industry faces significant challenges in the areas of real-time monitoring, energy efficiency, and intelligent decision-making. Traditional approaches lack the responsiveness and integration necessary for smart building operations. As a consequence, they frequently result in delays, inefficiencies in resource utilization, and unsatisfactory interior environments. This research presents a unique framework for enhancing building operations and maintenance, based on Multi-Dimensional Digital Twin Technology-assisted Building Information Modeling (MD-DTT-BIM). The purpose of this framework is to address the challenges that have been identified. The proposed system utilizes real-time data integration, simulation, and predictive analytics to enhance the planning and management of construction projects and facilities simultaneously. The MD-DTT-BIM model achieves substantial performance improvements over existing models, as demonstrated by experimental validation. These improvements include a 97.6% increase in operational efficiency, a 96.7% improvement in real-time monitoring accuracy, a 95.3% reduction in energy consumption, a 94.3% rise in occupant satisfaction, and a 98.3% accuracy in predicting the quality of the indoor environment. Based on these findings, it is evident that the model has the potential to facilitate a transition toward more intelligent and environmentally responsible building methods.
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Junjie Zhou (Mon,) studied this question.
synapsesocial.com/papers/68bb49db6d6d5674bcd00458 — DOI: https://doi.org/10.1038/s41598-025-17100-3
Junjie Zhou
Guangdong University of Technology
Scientific Reports
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