Work

Digital twin with uncertainty‐informed deep learning for prompt quantitative risk assessment of deep excavation

Year: 2025

Type: article

Abstract: Deep excavation projects are inherently fraught with significant risks due to complex geological conditions, intricate construction processes, and uncertain surrounding environments. This study proposes a novel digital twin-driven framework that integrates the physical excavation process with a virtual digital model to enable prompt and closed-loop risk management. The framework combines building information modeling (BIM), Internet of Things (IoT), and uncertainty-informed deep learning models. First, multisource and heterogeneous data from the physical excavation process are captured via the BIM-IoT system to form a comprehensive database. The database is then input to the virtual digital model to facilitate synchronous risk prediction, influence factors identification, and risk control. A key innovation lies in the incorporation of an uncertainty-informed long-term time series model, which combines bidirectional long short-term memory-based point prediction and deep Gaussian process regression-based interval prediction. This hybrid model effectively addresses data and model uncertainties, bolstering forecasting precision. Finally, the proposed digital twin with uncertainty-informed deep learning model is applied to a practical deep excavation project, showcasing its capability to improve risk assessment and management throughout the entire construction process. These findings provide a solid foundation for enhancing risk management level with increased intelligence, promptness, and reliability in underground construction through the application of digital twin technology.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1111/mice.70049

Authors: Xiong Wang, Yue Pan, Jin‐Jian Chen

Institution: Shanghai Jiao Tong University

Language: English

Topic: BIM and Construction Integration

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,230