Risk Assessment and Decision Support Model for the Entire Lifecycle of Buildings

With the growth of Building Information Modeling (BIM) technology, the construction industry is undergoing a second information revolution, promoting information integration, collaborative management, and intelligent decision-making throughout the entire lifecycle of construction projects. The health monitoring of building engineering structures is crucial for ensuring the long-term safe operation of the project. However, current construction still faces problems such as insufficient information integration, lagging risk feedback, and low efficiency of remote decision-making. Therefore, this article proposes a risk assessment and decision support model based on the fusion of BIM and deep learning (DL), aiming to achieve dynamic risk prediction and intelligent management in the construction process. This model utilizes the geometric and semantic information of BIM to construct a digital twin environment, and combines DL algorithm to analyze multi-source heterogeneous data, achieving real-time identification and evaluation of risk factors. The results indicate that our model can effectively improve the precision of risk warning and the speed of decision response, providing data-driven intelligent support for the full lifecycle management of engineering.