Risk Warning and Decision Support Model for Shield Tunneling Construction in Urban Rail Transit
DOI:
https://doi.org/10.13052/jicts2245-800X.1424Keywords:
Risk warning, decision support, construction, proximal policy optimization (PPO), regularized random forest (RRF), safety standards, ICT protocolsAbstract
Risks occur due to variable geological conditions and urban constraints. Inadequate risk identification and management in shield tunneling can result in safety hazards, schedule overruns, and increased project costs, necessitating an effective risk assessment and decision-making framework. This research aims to develop a comprehensive risk warning and decision support model to facilitate early risk detection and informed mitigation strategies during shield tunneling construction in urban rail transit projects. The dataset includes geotechnical conditions, tunneling parameters, environmental factors, operational records, and safety monitoring data. Data sources include multi-sensors, and project logs, enabling comprehensive risk analysis and modeling. These multi-sensor inputs include cutterhead torque sensors, thrust force sensors, slurry pressure sensors, vibration and displacement sensors, as well as ground settlement monitoring instruments. The project logs consist of TBM operational logs, geotechnical investigation records, and safety monitoring logs documenting environmental and structural conditions during tunneling. To ensure data quality, pre-processing methods including Interquartile Range (IQR) for outlier detection and mean imputation for missing values are applied. Predictive risk modeling was conducted using Regularized Random Forest (RRF). Risk thresholds were established in accordance with model outputs and relevant safety standards to enable proactive early warning alerts. The decision support module leverages Proximal Policy Optimization (PPO) to recommend adaptive mitigation actions, such as tunneling parameter adjustments and structural reinforcements. Additionally, it facilitates forecasting of potential outcomes, providing dynamic, real-time feedback to the construction management team for rapid operational response. The proposed model demonstrated robust performance in early risk identification and offered actionable recommendations that enhance safety management and operational efficiency within urban shield tunneling projects. The integrated risk warning and decision support framework provides a technically sound and practical tool to improve risk mitigation efficacy, optimize construction decision-making processes, and promote the safe and efficient advancement of urban rail transit shield tunneling construction.
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