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Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage

机译：基于遗传算法和支持向量机的矿山建筑破坏智能预测模型

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摘要

Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model’s generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems.
机译：影响采矿引起的建筑物破坏的因素的特征是多样的，非线性的和多重线性的。为了更好地描述这些因素，基于支持向量机（SVM）开发了用于地下采矿引起的建筑物破坏的智能预测模型。在综合考虑地质，采矿和建筑因素的基础上，精心选择了10个因素。尤其是，将砖混结构的采矿引起的破坏等级用作建议模型中的主要输入变量。在该模型中，以砖混结构的破坏等级和最大裂缝宽度为输出变量。在中国，总共收集了32起采矿引起的建筑破坏的典型案例，并将其用作培训数据。径向基函数（RBF）用于SVM分类和最大裂缝宽度回归模型的应用。为了提高模型的通用性和预测能力，采用遗传算法（GA）为SVM模型选择有效参数，然后对六组样本进行相应的识别。分类和回归结果表明，所提出的基于GA-SVM的预测模型可以预测采矿对砖混结构房屋的破坏，评估结果与监测数据吻合良好。这表明所提出的模型在广泛的工程问题中是实用的。

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