Dynamic Decision Model for Tunnel Cross-Passage Layout Based on Multi-Source Sensor Data Fusion

The layout of tunnel cross-passages is a critical aspect of tunnel construction and operational safety. Traditional methods, primarily based on static design, struggle to adapt to complex and variable geological and construction environments. This study proposes a dynamic decision model for cross-passage layout based on multi-source sensor data fusion to enhance the scientific rigor and adaptability of cross-passage design. A three-dimensional data fusion mechanism integrating “temporal-spatial-statistical” dimensions was developed. Bayesian network quantifies uncertainty, Kalman filter processes time series data, and PCA extracts spatial features. Reinforcement learning and non-dominated sorting genetic algorithm II (NSGA-II) are used to achieve multi-objective optimization of safety coverage and construction efficiency. The proposed model significantly outperforms the traditional methods in many indicators, and is verified by 100 Monte Carlo simulations and actual tunnel experiments. The dynamic scheme increased the safety coverage rate from 72.4% to 91.7%, shortened the average evacuation distance by 38.7% (from 248 meters to 152 meters), saved resources by 14.2% (about 9.8 million yuan), and shortened the construction period by 3-6 days. The comprehensive utility value is 0.91, which is 19% higher than the traditional static method, and the robustness is enhanced. The model realizes the safe, economical, and efficient real-time optimization of the layout of the transverse channel. It provides a technical path and data support that can be promoted for intelligent tunnel construction under complex geological conditions.