Identification of dispersive soils via computational intelligence

Abstract

AbstractWhen exposed to water, dispersive (D) soils are eroded and washed away by underground or surface flowing waters. Although soil dispersion is due to its chemical composition, the results of the commonly used chemical method, that is, the Sherard approach, do not match with those of the popular robust Pinhole test. Due to the deficiency of the chemical method, this study aimed to employ artificial intelligence (AI)‐based approaches for predicting the D classification of the soils. To this end, a database containing 321 records of the results of chemical and Pinhole tests on borrow soil samples was collected from various construction sites in Iran. The predictive models for soil dispersion evaluation were developed using the artificial neural network (ANN) and the support vector machine (SVM). The D classification results were presented as output classes versus target classes. Through the comparison of statistical indices, it was found that the results of the proposed models conform to those of the Pinhole test. It was also shown that the ANN model is more accurate than the SVM model for predicting the dispersion potential of the soil. Furthermore, it was indicated that the new models significantly outperform the Sherard method in determining the D classification of the soil.HighlightsDispersive soils can be classified employing artificial intelligence (AI)‐based methods.AI‐based approaches have superior predictive ability in contrast to traditional approachs.ANN and SVM techniques can accurately identify soil dispersion potential.Higher accuracy of new methods compared to common Sherard approach was validated with Pinhole test results.