Hybrid ELM and MARS-Based Prediction Model for Bearing Capacity of Shallow Foundation
Hybrid ELM and MARS-Based Prediction Model for Bearing Capacity of Shallow Foundation
复制标题
基于ELM和MARS的混合浅基础承载力预测模型
作者：
Manish Kumar;Vinay Kumar;Rahul Biswas;P. Samui;M. Kaloop;M. Alzara;A. Yosri
由于土壤的形成过程，土壤的性质在水平方向和垂直方向上都是不同的。因此，确保土工结构的安全设计一直是一项重大挑战。在浅层地基中，进行现场测试既昂贵又耗时，而且往往是在大幅缩小的模型上进行的。经验模型也被发现是文献中最不可靠的。该研究提出了基于人工智能的技术来预测浅基础的承载力，使用文献中不同实验室进行的实验中获得的数据集进行模拟。将ELM- eo和ELM- pso混合模型与ELM和MARS模型的结果进行了比较。利用各种性能参数对模型的性能进行了分析和比较。使用秩分析对模型进行分级，并使用误差矩阵和REC曲线提供可视化解释。结果表明，ELM- eo模型在测试阶段表现最佳（R2为0.995，RMSE为0.01），其次是ELM- pso、MARS和ELM模型。MARS的性能优于ELM（测试阶段R2分别为0.97和0.5）；然而，杂交极大地提高了ELM的性能，并且混合模型的性能优于MARS模型。本文认为，基于人工智能的模型具有较强的鲁棒性，在进一步的研究中应鼓励将回归模型与优化技术相结合。灵敏度分析表明，各输入参数对输出均有显著影响，其中摩擦角最大。
The nature of soil varies horizontally as well as vertically, owing to the process of the formation of soil. Thus, ensuring the safe design of geotechnical structures has been a major challenge. In shallow foundations, conducting field tests is expensive and time-consuming and often conducted on significantly scaled-down models. Empirical models, too, have been found to be the least reliable in the literature. The study proposes AI-based techniques to predict the bearing capacity of a shallow foundation, simulated using the datasets obtained in experiments conducted in different laboratories in the literature. The results of the ELM-EO and ELM-PSO hybrid models are compared with that of the ELM and MARS models. The performance of the models is analyzed and compared with each other using various performance parameters. The models are graded to each other using rank analysis and the visual interpretations are provided using error matrices and REC curves. ELM-EO is concluded to be the best performing model (R2 and RMSE equal to 0.995 and 0.01, respectively, in the testing phase), closely followed by ELM-PSO, MARS, and ELM. The performance of MARS is better than ELM (R2 equals 0.97 and 0.5, respectively, in the testing phase); however, hybridization greatly enhances the performance of the ELM and the hybrid models perform better than MARS. The paper concludes that AI-based models are robust and hybridization of regression models with optimization techniques should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with friction angle being the highest.