| 基于支持向量机的膨胀土胀缩等级预测 |
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| 引用本文： | 周苏华,周帅康,张运强,聂志红,雷瑜.基于支持向量机的膨胀土胀缩等级预测[J].中国地质灾害与防治学报,2021,32(1):117-126. |
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| 作者姓名： | 周苏华 周帅康 张运强 聂志红 雷瑜 |
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| 作者单位： | 1.湖南大学土木工程学院，湖南 长沙 410082 |
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| 基金项目： | 国家自然科学基金青年基金(51708199)；中央高校基本科研业务费专项基金(531107050969)；贵州省科技支撑计划（2020-4Y047）；贵州省交通运输厅科技项目 (2017-143-054)；北京市科技计划 (Z181100003918005) |
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| 摘 要： | 膨胀土的胀缩等级判定对膨胀土地区工程建设具有重要的意义。为此，本文提出了一种基于支持向量回归机（SVR）模型的膨胀土胀缩等级预测方法。基于肯尼亚“蒙内铁路”沿线膨胀土的土工试验数据，以土体自由膨胀率作为预测目标，构建了包含两种不同预测指标体系的膨胀土胀缩等级预测模型。模型I以液限、塑限、塑性指数、3种不同粒径的颗粒含量（< 0.075、0.075～0.25、0.25～0.5）、土的类型为输入参数，模型II以液限、塑限、塑性指数、粒径< 0.075的颗粒含量、土的类型为预测参数。两个模型在预测时采用Linear、Polynomial、RBF和Sigmoid核函数进行训练。结果表明:（1）当预测采样次数达到1000次时，训练模型均趋于稳定；（2）整体而言，模型I的预测精度要优于模型II，模型I中采用RBF核函数建立的模型给出了最高准确率86.6%，其次为Linear核函数（准确率82.9%）和Sigmoid和函数（准确率75.1%）。模型II中采用RBF核函数建立的模型给出了最高准确率77.4%，其次为Linear核函数（准确率74.3%）和Sigmoid和函数（准确率72.9%）；（3）采用Linear函数、Sigmoid函数和RBF函数作为核函数模型对44组未知胀缩等级的土样预测时，模型I中三者预测结果相同的数量占比为73%，其余组土样的预测胀缩等级相同或相邻，不存在“越级”现象，模型II中三者预测结果相同的数量占比为68%，不存在“越级”现象。最后，通过与模糊层次分析法评价结果对比，进一步证明了本文研究结果可为肯尼亚等类似地区工程建设中膨胀土的胀缩等级预测和处理提供依据。
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| 关 键 词： | 膨胀土 胀缩等级 支持向量回归机 自由膨胀率 预测准确率 |
| 收稿时间： | 2020-04-06 |
Predicting of swelling-shrinking level of expansive soil using support vector regression |
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| Institution: | 1.College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China2.Key Laboratory of Building Safety and Energy Efficiency, Changsha, Hunan 410082, China3.College of Civil Engineering, Central South University, Changsha, Hunan 410075, China4.China Road & Bridge Corporation, Beijing 100010, China |
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| Abstract: | Level scaling of expansive soil is of significant importance in practical engineering. In this study, a support vector regression (SVR)-based predicting model of expansive soil level was proposed. Using the laboratory test dataset of expansive soil in Kenya, two training models with different predictors were built. Seven parameters, including liquid limit, plastic limit, plastic index, particle percentage of three different particle sizes (< 0.075、0.075～0.25、0.25～0.5), and soil type, were used in model I, while five parameters, including liquid limit, plastic limit, plastic index, percentage of particles with size < 0.075 and soil type, were used in model II. For each model, four kernels namely, Linear, Polynomial, RBF and Sigmoid, were used. The result had shown that all training models become stable when the sampling number reached 1000.The results show that with the increase of the number of randomly selected training samples, when the number of predictions reaches 1000, the prediction accuracy obtained by the SVR models with 4 different kernel functions basically stabilizes. The prediction effect is better when the RBF function and Linear function are used, followed by the Sigmoid kernel function. The prediction accuracy of the above three is more than 70% in scheme one, and prediction accuracy of the above three is more than 70% in scheme two. When Linear function, Sigmoid function, and RBF function are used as kernel function models to predict 44 groups of unknown expansive scale soil samples, the number of identical prediction results accounts for 73% in scheme one. The prediction scale of the remaining sets of soil samples is the same or adjacent, there is no "crossing" phenomenon. The number of identical prediction results accounts for 73% in scheme two, and there is no "crossing" phenomenon. The results of the study can provide a basis for the prediction and treatment of the scale of the expansion soil in the construction of Kenya and other regions. |
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