随机森林
堆
人工神经网络
极限学习机
梯度升压
Boosting(机器学习)
人工智能
计算机科学
均方误差
预测建模
机器学习
回归
灵敏度(控制系统)
统计
数学
工程类
算法
电子工程
作者
Manish Kumar,Pijush Samui,Divesh Ranjan Kumar,Panagiotis G. Asteris
标识
DOI:10.1080/17486025.2024.2337702
摘要
Machine learning (ML) has made significant advancements in predictive modelling across many engineering sectors. However, predicting the bearing capacity of pre-bored grouted planted nodular (PGPN) piles remains a relatively unexplored area due to the complexity of the load-bearing mechanism, pile-soil interactions, and multiple variables involved. The study utilises state-of-the-art ML techniques such as extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machines (GBMs), and deep learning-based simulation models. The dataset fed into the model comprises 81 case histories of static pile load tests conducted in various regions of Vietnam. The data was validated using descriptive statistics, sensitivity analysis, correlation matrix displays, SHAP plot analysis, and regression curves, with predictive performance validated through k-fold cross-validation. Among all the models tested, XGBoost (R2 = 0.91, RMSE = 0.09) and RF (R2 = 0.82, RMSE = 0.09) performed the best, while the deep neural network also yielded satisfactory results. However, GBM was found not to be a robust model for this analysis. The performance of the models was visually analysed using Violin plot comparisons and Taylor diagrams. The outcome of this study facilitates the safe and economical designs of the eco-friendly pile.
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