Abstract
Pore-water pressure (PWP) is influenced by climatic changes, especially rainfall. These changes may affect the stability of, particularly unsaturated slopes. Thus monitoring the changes in PWP resulting from climatic factors has become an important part of effective slope management. However, this monitoring requires field instrumentation program, which is resource and labour expensive. Recently, soft computing modelling has become an alternative. Low degree polynomial kernel support vector machine (SVM) was evaluated in modelling the PWP changes. The developed model used pore-water pressure and rainfall data collected from an instrumented slope. Wrapper technique was used to select input features and k-fold cross validation was used to calibrate the model parameters. The developed model showed great promise in modelling the pore-water pressure changes. High correlation, with coefficient of determination of 0.9694 between the predicted and observed changes was obtained. The one degree polynomial SVM model yielded competitive result, and can be used to provide lead time records of PWP which can aid in better slope management.
Highlights
Climatic factors, such as rainfall and evaporation play an important role in pore-water pressure (PWP) changes, which in turn is vital to slope studies
The wrapper method was employed in this study to determine the optimal subset of input features
Evolutionary algorithm was used as the search engine in the wrapper method
Summary
Climatic factors, such as rainfall and evaporation play an important role in pore-water pressure (PWP) changes, which in turn is vital to slope studies. High levels or positive PWP levels may lead to failure in slopes [1, 2] Because of such changes, it is important to monitor PWP changes, to rainfall. Pore-water pressure monitoring typically entails field instrumentation of the slope in question [4, 5]. This field instrumentation is expensive, time consuming and requires expertise. Aims to evaluate the capability of SVM with low degree polynomial kernel in modelling the nonlinear responses of PWP to rainfall
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