For the value of the surrounding rock parameters of the underground construction, a hybrid network approach combining backtracking search optimization algorithm (BSA) and BP neural network (BSA-BP) was proposed for the inversion study of the tunnel surrounding rock parameters. By establishing a tunnel finite element excavation model, the inversion parameters were used to calculate the displacement of the monitoring section and compare with the measured values in the field. Finally, the stability of the surrounding rock was analyzed and predicted. Compared with the GA-BP neural network, the results show that the BP neural network optimized by BSA algorithm has faster inversion speed and computational efficiency. The relative errors between the calculated displacement values and the field measured values obtained by using BSA-BP neural network inversion parameters are within 5%, indicating that the model has high inversion accuracy and is reasonable and feasible. The research results provide a new method for the inversion of underground engineering parameters.

fault-rupture zone  /  parameter inversion  /  BP network  /  backtracking search techniques