Khajehzadeh Mohammad, Keawsawasvong Suraparb, Kamchoom Viroon, Shi Chao, Khajehzadeh Alimorad
Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand.
Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar, Iran.
Heliyon. 2024 Aug 25;10(17):e36714. doi: 10.1016/j.heliyon.2024.e36714. eCollection 2024 Sep 15.
The precise assessment of shallow foundation settlement on cohesionless soils is a challenging geotechnical issue, primarily due to the significant uncertainties related to the factors influencing the settlement. This study aims to create an advanced hybrid machine learning methodology for accurately estimating shallow foundations' settlement (Sm). The initial contribution of the current research is developing and validating a robust hybrid optimization methodology based on an artificial electric field and single candidate optimizer (AEFSCO). This approach is thoroughly tested using various benchmark functions. AEFSCO will also be used to optimize three useful machine learning methods: long short-term memory (LSTM), support vector regression (SVR), and multilayer perceptron neural network (MLPNN) by adjusting their hyperparameters for predicting the settlement of shallow foundations. A database consisting of 189 individual case histories, conducted through various investigations, was used for training and testing the models. The database includes five input parameters and one output. These factors encompassed both the geometric characteristics of the foundation and the properties of the sandy soil. The results demonstrate that employing effective optimization strategies to adjust the ML models' hyperparameters can significantly improve the accuracy of predicted results. The AEFSCO has increased the coefficient of determination (R) value of the MLPNN model by 9.3 %, the SVR model by 8 %, and the LSTM model by 22 %. Also, the LSTM-AEFSCO model is more accurate than the SVR-AEFSCO and MLPNN-AEFSCO models. This is shown by the fact that R went from 0.9494 to 0.9290 to 0.9903, which is an increase of 4.5 % and 6 %.
精确评估无粘性土上浅基础的沉降是一个具有挑战性的岩土工程问题，主要是因为影响沉降的因素存在很大的不确定性。本研究旨在创建一种先进的混合机器学习方法，以准确估计浅基础的沉降（Sm）。当前研究的首要贡献是开发并验证一种基于人工电场和单候选优化器（AEFSCO）的强大混合优化方法。该方法使用各种基准函数进行了全面测试。AEFSCO还将用于优化三种有用的机器学习方法：长短期记忆（LSTM）、支持向量回归（SVR）和多层感知器神经网络（MLPNN），通过调整它们的超参数来预测浅基础的沉降。一个由189个通过各种调查得出的单独案例历史组成的数据库被用于训练和测试模型。该数据库包括五个输入参数和一个输出。这些因素既包括基础的几何特征，也包括砂土的特性。结果表明，采用有效的优化策略来调整机器学习模型的超参数可以显著提高预测结果的准确性。AEFSCO使MLPNN模型的决定系数（R）值提高了9.3%，SVR模型提高了8%，LSTM模型提高了22%。此外，LSTM-AEFSCO模型比SVR-AEFSCO和MLPNN-AEFSCO模型更准确。这体现在R值从0.9494变为0.9290再变为0.9903，分别提高了4.5%和6%。
