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.
Prediction of Settlements of Buildings around Excavations Based on I AO-LSSVM Model:Taking a Subway Foundation Pit in Shenzhen Huaqiangnan as an Example
Aiming at the shortcomings of the current building settlement prediction model triggered by pit excavation,such as insuf-ficient 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 ker-nel 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 accura-cy,faster convergence and stronger ability to jump out of the local optimization domain than the traditional prediction model.The predic-ted 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.
building settlement predictionTent chaotic mappingadaptive weightsimproved aquila optimizationleast squares support vector machine
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维普
