【作者】 熊孝波； 桂国庆； 郑明新； 许建聪； 马淑芝；

【Author】 XIONG Xiao-bo~(1,2) GUI Guo-qing~1 ZHENG Ming-xin~2 XU Jian-cong~3 MA Shu-zhi~4 (1. Institute of Technology Jinggangsan University, Ji’an 343009, China; 2. School of Civil Engineering, East China Jiaotong University, Nanchang 330013, China; 3. College of Civil Engineering, Tongji University, Shanghai 200092, China; 4. Engineering Faculty, China University of Geosciences, Wuhan 430074, China)

【机构】 井冈山大学工学院； 华东交通大学土建学院； 同济大学土木工程学院； 中国地质大学工程学院；

【摘要】 基坑工程由于受多种因素的影响,目前已成为岩土工程中的重点和难点。在基坑工程施工中,需要根据现场实际情况、周围环境、建筑安全等级等对变形进行严格控制。通过现场量测的深基坑围护结构变形信息资料,对实测数据进行整理和分析,利用神经网络对支护结构的变形作出预测,以保证基坑安全施工。研究了一种基于免疫识别原理的径向基函数神经网络学习算法,该算法将所识别的数据作为抗原,抗体为抗原的压缩映射并作为神经网络模型的隐层中心,采用最小二乘法确定权值,提高了RBF神经网络收敛速度和精度,将人工免疫RBF神经网络应用于时间序列预测中,工程实例计算证明了算法的有效性和可行性,为时间序列预测提供了一种新途径。更多还原

【Abstract】 Deep foundation pit is affected by several factors,and becomes the key problem in geotechnical engineering.The deformation must be controlled rigorously according to the actual situation surrounding environment and building safety grade.After analyzing the measured data of deformation,the prediction of deformation of supporting structure is made,and the safety of deep foundation pit is guranteed.To insure the construction quality of the deep foundation pit,deformation observation of the supporting structure is carried out in many deep pit in order to predict the development of deformation accurately during construction.A radial basis function (RBF) neural network learning algorithm based on immune recognition principle is studied.In the algorithm,the input data are regarded as antigens,and antibodies are regarded as the compression mapping of antigenas,i.e.the hidden layer centers.The weights of the output layer are determined by adopting the least square method,which can improve convergence speed and precision of the RBF neural network.A new time series prediction method based on artificial immune RBF neural network is also presented, and its application is discussed.The simulation experiment indicates that this method is effective in time series prediction.更多还原