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CLC number: TU433

On-line Access: 2020-06-10

Received: 2019-10-08

Revision Accepted: 2020-03-09

Crosschecked: 2020-05-23

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Hong-wei Huang


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Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.6 P.430-444


Machine learning-based prediction of soil compression modulus with application of 1D settlement

Author(s):  Dong-ming Zhang, Jin-zhang Zhang, Hong-wei Huang, Chong-chong Qi, Chen-yu Chang

Affiliation(s):  Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; more

Corresponding email(s):   huanghw@tongji.edu.cn, chongchong.qi@csu.edu.cn

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

Dong-ming Zhang, Jin-zhang Zhang, Hong-wei Huang, Chong-chong Qi, Chen-yu Chang. Machine learning-based prediction of soil compression modulus with application of 1D settlement[J]. Journal of Zhejiang University Science A, 2020, 21(6): 430-444.

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DOI - 10.1631/jzus.A1900515

The compression modulus (Es) 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 Es 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 Es 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 Es 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.


目的:土体压缩模量是影响岩土体结构变形的重要参数之一. 本文旨在通过机器学习的方法实现对压缩模量的预测,并通过构建一个机器学习模型,得到塑限、液限、塑性指数、液性指数、比贯入阻力以及埋深这6个输入参数与压缩模量预测值之间的关系.
创新点:1. 构建一个机器学习算法框架以实现对土体压缩模量的预测; 2. 此框架包括梯度提升回归树(GBRT)和遗传算法(GA),并采用GA对GBRT超参数进行获取.
方法:1. 通过收集整理工程报告获取本次预测的数据集(样本211个); 输入参数有6个,分别为塑限、液限、塑性指数、液性指数、比贯入阻力以及埋深; 输出参数为压缩模量. 2. 采用GBRT算法识别输入变量与目标响应之间的非线性规律,并采用GA调整GBRT模型的超参数. 3. 模型训练完成后,对压缩模量进行预测. 4. 将测试集上的预测结果和传统方法进行对比分析并应用到一维基础沉降中.
结论:1. 本文提出的GA-GBRT模型可以较好地实现对土体压缩模量的预测; GA可以对GBRT算法的超参数进行有效标定. 2. 训练后的GA-GBRT模型在训练集和测试集上都表现良好; 在训练集和测试集上的相关系数R值分别为0.82和0.91,说明模型可以对压缩模量进行准确预测. 3. 对输入变量相对重要性的研究发现,液性指标是本研究中最重要的变量,其重要性得分为0.313(总数为1); 其他指标的重要性排序依次为:液限、塑限、塑性指数、比贯入阻力和埋深. 4. 对于地基沉降的预测,本文提出的模型在相关系数R值和Mann-Whitney检验结果上均优于经验公式. 5. 本文提出的GA-GBRT模型可以更经济、更快速地预测土壤压缩模量.

关键词:压缩模量预测; 机器学习; 梯度提升回归算法; 遗传算法(GA); 基础沉降

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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