Abstract
A MATLAB based backpropagation neural network (BPNN) model has been developed. Two major geo-engineering applications, namely, earth slope movement and ground movement around tunnels, are identified. Data obtained from case studies are used to train and test the developed model and the ground movement is predicted with the help of input variables that have direct physical significance. A new approach is adopted by introducing an infiltration coefficient in the network architecture apart from antecedent rainfall, slope profile, groundwater level and strength parameters to predict the slope movement. The input variables for settlement around underground excavations are taken from literature. The neural network models demonstrate a promising result predicting fairly successfully the ground behavior in both cases. If input variables influencing output goals are clearly identified and if a decent number of quality data are available, backpropagation neural network can be successfully applied as mapping and prediction tools in geotechnical investigations.
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Neaupane, K., Achet, S. Some applications of a backpropagation neural network in geo-engineering. Env Geol 45, 567–575 (2004). https://doi.org/10.1007/s00254-003-0912-0
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DOI: https://doi.org/10.1007/s00254-003-0912-0