Constructing a Gas Explosion Inversion Model in a Straight Roadway Using GA-BP Neural Network

Abstract

When the location and intensity of the source of the explosion is determined, the severity and impact of the explosion can be analyzed and predicted, such as the overpressure, temperature, and toxic gas propagation. In order to provide the theory of emergency rescue work, improve rescue efficiency, to protect the safety of rescue personnel. In addition, to determine the gas explosion source location and intensity of the accident investigation also has an important role, on the one hand,it helps to determine the accident-related responsible person, on the other hand,it also can more accurately judge the nature of the accident and the cause of the explosion, summarize the accident experience, for the future prevention of such accidents provide guidance experience. Therefore, the location and intensity of the source of the explosion through the field data inversion is of great significance. Based on Genetic Algorithm (GA, and similarly hereinafter) to improve the back propagation(BP) neural network theory, the use of the method through the gas explosion experiments and simulation of overpressure data inversion of roadway gas explosion source location and intensity, the establishment of roadway gas explosion disaster inversion model for emergency rescue and accident investigation to provide data support.

Deformation Prediction and Analysis of Soft Rock Roadway with High Altitude and Large Buried Depth Based on Particle Swarm Optimization LSTM Model

“…As a new deformation prediction technology developed in recent years, the neural network prediction method, due to its strong nonlinear processing ability [16][17][18][19], when predicting the deformation of roadway surrounding rock, does not need to assume harsh preconditions like the theoretical method, nor many parameters like the numerical simulation method, but only some basic parameters and data to realize the accurate prediction of surrounding rock, and gradually liked by people [4,[20][21][22][23]. At present, the artificial neural network model has been widely used in various fields of engineering, and the prediction accuracy of surrounding rock deformation of relevant tunnels has also been improved year by year, but there is still a large research space [3,24,25].…”