Simulation of Nonstationary Random Process

Integrated approach for seismic wave prediction and structural evaluation of Oya tuff quarry underground spaces: Field observations and numerical modeling

This study evaluates the structural stability of large underground spaces in seismic conditions, represented by the Oya underground stone mining plant. By directly monitoring the seismic response of the underground mining site, significant earthquake activities at the plate boundaries of the Tokyo region and Ibaraki Prefecture offshore area were observed. Additionally, through an in-depth analysis of seismic records from different locations within the underground structure, the dynamic characteristics and motion patterns of the Oya underground stone mining plant were revealed, revealing its movement trajectory during earthquakes. Additionally, this study innovatively applied seismic waves measured at the original site as input parameters and artificially generated seismic waves based on their response spectra. A numerical analysis was performed after ensuring the model's high correlation with the original site was met. The findings demonstrate that the results of both parameter input methods are confirmable and valuable. Under severe seismic conditions, instability was observed in some regions of the underground mining site. The study also discusses the location and damage mechanisms of the mining site's structure under seismic effects, providing valuable insights for the safety assessment of similar large underground spaces and proposing new approaches for selecting input parameters in seismic analysis.

Effects of the Earthquake Nonstationary Characteristics on the Structural Dynamic Response: Base on the BP Neural Networks Modified by the Genetic Algorithm

The intensity non-stationarity is one of the basic characteristics of ground motions, the influences of which on the dynamic responses of structures is a pressing issue in the field of earthquake engineering. The BP neural network modified by the genetic algorithm was adopted in this research to investigate the influence of intensity nonstationary inputs on the structural dynamic responses from a new perspective. Firstly, many training data were generated from the prediction formula of dynamic response. The BP neural network was then pre-trained by sparsely selected data to optimize the initial weights and biases. Finally, the BP neural network was trained by all data, and the mean square error of predicted responses compared with the target response were less than 10−5. The calculation formula of sensitivity was also derived here to quantify the influence of the input change on the output. The presented method combines the advantages of neural networks in nonlinear multi-variable fitting and provides a new perspective for the study of earthquake nonstationary characteristics and their influence on the structural dynamic responses.