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首页> 外文期刊>Engineering with Computers >A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam)
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A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam)

机译：基于群体智能的机器学习方法来预测道路建设的土壤抗剪强度：以龙岗国家高速公路项目（越南）为例

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摘要

Determining the shear strength of soil is an important task in the design phase of construction project. This study puts forward an artificial intelligence (AI) solution to estimate this parameter of soil. The proposed approach is a hybrid AI model that integrates the least squares support vector machine (LSSVM) and the cuckoo search optimization (CSO). A dataset of 332 soil samples collected from the Trung Luong National Expressway Project in Viet Nam have been used for constructing and validating the AI model. The sample depth, sand percentage, loam percentage, clay percentage, moisture content, wet density of soil, specific gravity, liquid limit, plastic limit, plastic index, and liquid index are used as input variables to predict the output variable of shear strength. In the hybrid AI framework, LSSVM is employed to generalize the functional mapping that estimates the shear strength from the information provided by the aforementioned input variables. Since the model establishment of LSSVM requires a proper setting of the regularization and the kernel function parameters, the CSO algorithm is utilized to automatically determine these parameters. Experimental results show that the prediction accuracy of the hybrid method of LSSVM and CSO (RMSE=0.082, MAPE=14.841, and R-2=0.885) is better than those of the benchmark approaches including the standard LSSVM, the artificial neural network, and the regression tree. Therefore, the proposed method is a promising alternative for assisting construction engineers in the task of soil shear strength estimation.
机译：确定土的抗剪强度是建设项目设计阶段的重要任务。这项研究提出了一种人工智能（AI）解决方案来估算该土壤参数。所提出的方法是一种集成了最小二乘支持向量机（LSSVM）和布谷鸟搜索优化（CSO）的混合AI模型。从越南Trung Luong国家高速公路项目收集的332个土壤样品的数据集已用于构建和验证AI模型。样品深度，砂百分比，壤土百分比，粘土百分比，水分含量，土壤的湿密度，比重，液体极限，塑性极限，塑性指数和液体指数用作输入变量，以预测剪切强度的输出变量。在混合AI框架中，LSSVM用于概括功能映射，该功能映射根据上述输入变量提供的信息来估算抗剪强度。由于LSSVM的模型建立需要对正则化和内核函数参数进行适当的设置，因此使用CSO算法自动确定这些参数。实验结果表明，LSSVM和CSO混合方法（RMSE = 0.082，MAPE = 14.841和R-2 = 0.885）的预测精度优于基准方法，包括标准LSSVM，人工神经网络和回归树。因此，所提出的方法是协助建筑工程师进行土壤抗剪强度估算任务的一种有希望的替代方法。

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