Tunnel Detection Structure Analysis Based on Neural Network Technology

The existing tunnel detection structural analysis methods have certain problems in fault identification, health assessment and positioning accuracy. In particular, when processing complex tunnel structure data, traditional methods are difficult to adapt to diverse fault types and real-time data changes. To this end, this paper introduces neural network technology, aiming to solve the shortcomings of traditional methods in tunnel detection through deep learning models and improve the accuracy and efficiency of fault detection. This paper adopts a recurrent neural network (RNN) model and combines various sensor data from the tunnel for structural analysis. In the model design, the input layer receives various sensor data such as temperature, pressure, crack width, settlement displacement, etc. from the sensor, and outputs the tunnel health assessment results after multi-layer neural network processing. The model uses the ReLU activation function to optimize nonlinear feature extraction, adjusts network weights through the back-propagation algorithm, and accurately identifies fault types such as cracks, settlement, and deformation. In fault location, through the relationship between network learning and sensor data, the model can locate the fault location and output accurate location results. The experimental results show that in the diagnosis of "crack" faults, the accuracy of the neural network is 90%, much higher than the 75% based on the threshold algorithm; in the diagnosis of "settlement" and "deformation" faults, the accuracy of the neural network is also about 7% and 12% higher than the threshold algorithm. In addition, the accuracy of fault location is also significantly improved. The model can locate faults within an error range of ±1 meter and has a high spatial resolution.