In this study, a multivariate adaptive regression splines (MARS) model has been developed to predict the settlement of shallow reinforced sandy soil foundations (RSSFs). The potential of the MARS model is validated comparatively with four other robust artificial intelligence/machine learning regression models, namely extreme learning machines (ELM), support vector regression (SVR), Gaussian process regression (GPR), and stochastic gradient boosting trees (SGBT). The pertinent data retrieved from previously published well-established scientific studies have been used to calibrate and validate the data-driven intelligent machine learning models. The predictive strength of all the modelling tools mentioned above were assessed via several statistical indices. Moreover, the predictive ability and reliability of the developed models were also corroborated with ranking criteria and external validation analysis. The results as obtained have shown that the MARS modelling technique attains the superior veracity in predicting the settlement of reinforced foundations.

中文翻译：

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加筋土基础多元自适应回归样条模型

在这项研究中，开发了一种多元自适应回归样条 (MARS) 模型来预测浅层加筋砂土基础 (RSSF) 的沉降。MARS 模型的潜力与其他四种强大的人工智能/机器学习回归模型进行了比较验证，即极限学习机 (ELM)、支持向量回归 (SVR)、高斯过程回归 (GPR) 和随机梯度提升树 (SGBT) ）。从先前发表的完善的科学研究中检索到的相关数据已被用于校准和验证数据驱动的智能机器学习模型。上面提到的所有建模工具的预测强度都是通过几个统计指标进行评估的。此外，所开发模型的预测能力和可靠性也得到了排名标准和外部验证分析的证实。所得结果表明，MARS建模技术在预测加筋基础沉降方面具有较高的准确性。