Prediction of soil classification in a metro line from seismic wave velocities using soft computing techniques

At a particular location on the ground, geotechnical measurements of soil properties are utilized to offer information for infrastructure design. Design uncertainty and dependability may increase when little point data is used. Geophysical techniques offer constant geographic information about the soil and are less time-consuming and intrusive. Geophysical data, however, is not expressed in terms of technical specifications. To enable the use of geophysical data in geotechnical designs, correlations between geotechnical and geophysical characteristics are required. The S- and P- seismic wave velocities are the main focus of the present geophysical technique research. Artificial neural network (ANN) models are developed using published data to predict seismic wave velocity and soil classification for seismic site effect evaluation. The results of ANN models using publicly available data demonstrate that seismic wave velocity has a moderate to high degree of accuracy in predicting soil classification. Regression is not as effective as artificial neural networks (ANN) in terms of overall performance. To confirm this, enclosed areas were evaluated to accurately predict soil classification and assess the performance of both ANN and regression models. The artificial neural network predicted the enclosed areas with much higher accuracy.