基于改进最小二乘支持向量机组合模型的深基坑沉降变形预测

Deep Foundation Pit Settlement Deformation Prediction Based on Improved Least Square Support Vector Machine Combined Model

为了提高深基坑沉降变形预测精度,及时为深基坑支护施工提供指导,提出一种改进最小二乘支持向量机组合模型;通过引入自适应噪声完备集合经验模态分解方法分解原始深基坑沉降变形数据,并结合粒子群优化算法和遗传算法对最小二乘支持向量机进行参数寻优,对分解的数据分别训练、预测后再叠加,得到最终预测结果;应用所提出模型对济南市某深基坑的累积沉降量进行预测,同时与其他模型对比,验证所提出模型的实用性和优越性。结果表明:所提出模型预测深基坑累积沉降量的平均相对误差为0.035%,均方误差为0.0809 mm^(2),均方根误差为0.2838 mm,所提出模型的准确性远优于其他模型的;自适应噪声完备集合经验模态分解方法的引入更有利于在深基坑沉降变形预测方面发挥最小二乘支持向量机的优势。

To improve prediction accuracy of deep foundation pit settlement deformation and provide timely guidance for deep foundation pit supporting construction,an improved least square support vector machine combined model was proposed.Complete ensemble empirical mode decomposition with adaptive noise was introduced to decompose original deep foundation pit settlement deformation data.Particle swarm optimization algorithm and genetic algorithm were combined to optimize parameters of least square support vector machine.The decomposed data were trained,predicted,and then superposed to obtain final prediction results.The proposed model was used to predict the cumulative settlement of a deep foundation pit in Jinan city,and was compared with other models to verify practicability and superiority of the proposed model.The results show that the mean relative error,mean square error,and root-mean-square error of the proposed model for predicting the cumulative settlement are 0.035%,0.0809 mm^(2),and 0.2838 mm.The accuracy of the proposed model is far better than that of other models.The introduction of complete ensemble empirical mode decomposition with adaptive noise is more conducive to take advantage of least square support vector machine in deep foundation pit settlement deformation prediction.