Inverse Back Analysis Based on Evolutionary Neural Networks for Underground Engineering

In essence, back analysis is a process of system identification. Therefore, artificial neural networks represent a suitable solution methodology for this problem. To overcome the shortcomings of the neural networks and evolutionary neural networks, based on immunized evolutionary programming, a new evolutionary neural network whose architecture and connection weights simultaneously evolve is proposed. Using this new evolutionary neural network, a novel inverse back analysis for underground engineering is studied. Using a numerical example and a real engineering example, namely, an underground roadway of the Huainan coal mine in China, the accuracy of this inverse back analysis is verified. Moreover, the non-uniqueness of the solution generated by the inverse back analysis is analyzed. The results show that, using the back-calculated parameters, the computed displacements agree with the measured ones. Thus, the new inverse back analysis method is demonstrated to be a high-performance method for usage in underground engineering. Moreover, various other conclusions can be drawn: the training samples of the neural network should be collected from the results of the positive analysis by the finite element method and selected based on the orthogonal experimental design, and the precision of the back analysis using multiple parameters is worse than that using a single parameter.