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.

中文翻译：

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通过计算智能识别分散土壤

当暴露在水中时，分散的 (D) 土壤会被地下或地表流水侵蚀和冲走。虽然土壤分散是由于其化学成分，但常用的化学方法即 Sherard 方法的结果与流行的鲁棒针孔试验的结果不匹配。由于化学方法的不足，本研究旨在采用基于人工智能 (AI) 的方法来预测土壤的 D 分类。为此，从伊朗的各个建筑工地收集了一个数据库，其中包含 321 条借土样品的化学和针孔测试结果记录。土壤分散评估的预测模型是使用人工神经网络 (ANN) 和支持向量机 (SVM) 开发的。D 分类结果呈现为输出类与目标类。通过统计指标的比较，发现所提模型的结果与针孔试验的结果相符。还表明，ANN 模型比 SVM 模型更准确地预测土壤的分散潜力。此外，表明新模型在确定土壤的 D 分类方面明显优于 Sherard 方法。