Abstract: The compression modulus (E_s) is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems, such as foundations. However, it is difficult and sometime costly to obtain this parameter in engineering practice. In this study, we aimed to develop a non-parametric ensemble artificial intelligence (AI) approach to calculate the E_s of soft clay in contrast to the traditional regression models proposed in previous studies. A gradient boosted regression tree (GBRT) algorithm was used to discern the non-linear pattern between input variables and the target response, while a genetic algorithm (GA) was adopted for tuning the GBRT model’s hyper-parameters. The model was tested through 10-fold cross validation. A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model’s predictions. The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91, respectively, indicating that the proposed machine learning (ML) model has great potential to improve the prediction of E_s for soft clay. A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems. This model, however, cannot be directly applied to the prediction of E_s in other sites due to its site specificity. This problem can be solved by retraining the model using local data. This study provides a useful reference for future multi-parameter prediction of soil behavior.

Key words: Compression modulus prediction; Machine learning (ML); Gradient boosted regression tree (GBRT); Genetic algorithm (GA); Foundation settlement

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