When 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.
Highlights
Dispersive 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.
In this paper, the effect of polypropylene (PP) geotextile as reinforcing phase in enhancing the mechanical properties of soil structures under different loading conditions is investigated. For this purpose, the effect of effective parameters such as type of surface geometry and the locality of loading on load-bearing capacity changes and the structure failure mechanism were studied using modeling by centrifuge method. Therefore, the horizontal and vertical displacements on the structure, which simulated by geotechnical centrifuge model, were recorded using the displacement measurement sensor and digital camera. The results show that for loads of the same width in the soil/PP composite structures, which is affected by the presence of PP reinforcing phase, whatever the shape of the loading support area changes from square to strip and the length of the loading level increases, the final load-bearing capacity of the structure will increase. Also, by examining the soil reinforcement PP fabric after the experiments, it was found that the maximum and minimum expansion rate of damage in PP reinforcing phase was related to the condition of the soil/PP composite structure, under loading with strip and square geometry, respectively.
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