CAJ | 학술논문

深基坑变形预测是进行施工参数调整和确保深基坑施工安全的重要手段，而如何对其变形进行有效、准确的预测是一个有待解决的技术难题。采用粒子群优化算法对神经网络模型的初始权值和阈值进行优化，并将已有的变形监测数据作为神经网络的输入参数，建立了基于粒子群优化神经网络算法的深基坑变形预测方法。将形成的方法应用于长春市火车站北广场深基坑开挖监测工程中。结果表明：8号水平位移测点预测结果的均方根误差为3．78％，平均百分比误差为5．48％；9号地面沉降点预测结果的均方根误差为5．62％，平均百分比误差为3．23％。经验证，本文方法预测深基坑开挖过程中的变形具有较高的可信度。
심기갱변형예측시진행시공삼수조정화학보심기갱시공안전적중요수단，이여하대기변형진행유효、준학적예측시일개유대해결적기술난제。채용입자군우화산법대신경망락모형적초시권치화역치진행우화，병장이유적변형감측수거작위신경망락적수입삼수，건립료기우입자군우화신경망락산법적심기갱변형예측방법。장형성적방법응용우장춘시화차참북엄장심기갱개알감측공정중。결과표명：8호수평위이측점예측결과적균방근오차위3．78％，평균백분비오차위5．48％；9호지면침강점예측결과적균방근오차위5．62％，평균백분비오차위3．23％。경험증，본문방법예측심기갱개알과정중적변형구유교고적가신도。
Prediction of the deformation is one of the most important methods for the construction parameter adjustment for deep foundation pit.However,it is still a chilling task to effectively predict accurate deformation in engineering application.We proposed deformation prediction model,which is based on the neural network optimized by particle swarm optimization ,for the deformation of the deep foundation pit based on filed data.The proposed model is established by using the existing monitoring data as input parameters of neural network.The initial weights and threshold values of neural network model are optimized by using particle swarm optimization to improve the prediction accuracy and prediction efficiency of the neural network algorithm.The proposed method is used for the foundation pit located in north plaza of Changchun railway station comprehensive traffic transfer center.The results show that for the No.8 point measuring horizontal displacement,the root mean square error (RMSE)of the horizontal displacement of No.8 points is 3.78%,the mean absolute percentage error (MAPE)is 5.48%;for the No.9 point measuring ground settlement,the number respectively are 5.62% and 3.23%.Results show that the proposed method can be reliably used to predict the deformation of the deep foundation pit.