基于IAO-LSSVM模型的基坑周围建筑物沉降预测:以深圳华强南站地铁基坑为例  

Prediction of Settlements of Buildings around Excavations Based on I AO-LSSVM Model:Taking a Subway Foundation Pit in Shenzhen Huaqiangnan as an Example

作　　者：贾磊 贾世济 高帅 JIA Lei;JIA Shi-ji;GAO Shuai(School of Urban Geology and Engineering,Hebei GEO University,Shijiazhuang 050030,China)

机构地区：[1]河北地质大学城市地质与工程学院,石家庄050030

出　　处：《科学技术与工程》2024年第7期2885-2892,共8页Science Technology and Engineering

基　　金：河北省教育厅高等学校自然科学研究重点项目(ZD2019026)。

摘　　要：针对当前基坑开挖引发建筑物沉降预测模型存在精度不足、收敛速度慢、易陷入局部最优等缺点,提出了一种基于改进天鹰算法(improved aquila optimizer, IAO)优化最小二乘支持向量机(least squares support vector machine, LSSVM)的建筑物沉降预测模型。利用Tent混沌映射提高天鹰算法的种群多样性水平,再通过自适应权重强化算法的全阶段寻优能力;引入IAO算法优化LSSVM的正则化参数和核函数宽度,构建基于IAO-LSSVM的建筑物沉降预测模型,并将该预测模型在深圳华强南某地铁基坑工程中进行了验证。结果表明:该沉降预测模型相比于传统预测模型精度更高、收敛更快、跳出局部最优域的能力强;该模型预测值与实际沉降监测值吻合度较高,其误差在5%左右,更适合预测城市中地铁基坑开挖引起的周围建筑物沉降。Aiming at the shortcomings of the current building settlement prediction model triggered by pit excavation,such as insufficient accuracy,slow convergence speed,and easy to fall into local optimization,a building settlement prediction model based on the improved aquila optimizer(IAO)optimized least squares support vector machine(LSSVM)was proposed.Tent chaotic mapping was utilized to improve the population diversity level of the aquila optimizer,and then adaptive weighting was used to strengthen the algorithm's full-stage optimization search capability.The IAO algorithm was introduced to optimize the regularization parameter and kernel function width of LSSVM to construct a building settlement prediction model based on IAO-LSSVM,and the prediction model was verified in a subway pit project in Huaqiang South,Shenzhen.The results show that the settlement prediction model has higher accuracy,faster convergence and stronger ability to jump out of the local optimization domain than the traditional prediction model.The predicted values of the model are in good agreement with the actual settlement monitoring values,and its error is around 5%,which is more suitable for predicting the settlement of the surrounding buildings caused by the excavation of the subway foundation pit in the city.

关 键 词：建筑物沉降预测 Tent混沌映射 自适应权重 改进天鹰算法 最小二乘支持向量机 

分 类 号：TU753[建筑科学—建筑技术科学] TP18[自动化与计算机技术—控制理论与控制工程]