深度置信网络算法在公路边坡工程安全评价中的运用  

Application of deep belief network algorithm in safety evaluation of highway slope engineering

作　　者：蒙国往 程懿 吴波[1,3] 叶华政 刘家乐 MENG Guowang;CHENG Yi;WU Bo;YE Huazheng;LIU Jiale(School of Civil Engineering and Architecture,Guangxi University,Nanning 530004,China;Guangxi Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education,Nanning 530004,China;School of Civil Engineering and Architectural,East China University of Technology,Nanchang 330013,China)

机构地区：[1]广西大学土木建筑工程学院,南宁530004 [2]特色金属材料与组合结构全寿命安全国家重点实验室,南宁530004 [3]东华理工大学土木与建筑工程学院,南昌330013

出　　处：《安全与环境学报》2024年第2期470-478,共9页Journal of Safety and Environment

基　　金：国家自然科学基金项目(52208389)。

摘　　要：针对传统边坡工程安全评价方法无法对边坡稳定性进行符合实际情况下工程安全评价的问题,提出一种基于深度置信网络(Deep Belief Networks,DBN)算法的公路边坡工程安全评价方法。首先,使用MATLAB建立模型;然后,对基于DBN算法的边坡稳定性安全评价方法进行研究;最后,使用DBN算法在莆炎高速公路项目中进行公路边坡工程安全评价。结果显示:(1)与反向传播(Back Propagation,BP)神经网络相比,DBN模型得到的预测值误差更小,精度更高,计算复杂度更低、可扩展性更强;(2)在实际工程中,DBN算法能够有效地对众多非线性因素共同作用下的公路边坡进行符合实际的安全评价。Aiming at the problem that the traditional slope engineering safety evaluation method cannot carry out the engineering safety evaluation of slope stability in line with the actual situation,In this paper,a safety evaluation method for highway slope engineering based on Deep Belief Networks(DBN)algorithm is proposed.In this paper,first,using MATLAB established a DBN neural network model.Then,using the collected middle and outer slope stability dataset,the bulk density,cohesion,internal friction angle,slope angle,slope height,and pore pressure ratio are input to the DBN algorithm as the characteristic values,and testing the DBN model,and the test results are compared with the slope stability safety evaluation method based on BP neural network algorithm.Finally,to test the versatility of the proposed DBN algorithm and its application in practical engineering,this paper uses the slope engineering safety influencing factors collected in Puyan project and different from the benchmark data set to carry out the safety evaluation of highway slope engineering.The results show that:(1)the accuracy of the DBN algorithm in predicting the slope state of the collected dataset is 100%;(2)When analyzing the example of arc failure slope,The average absolute error of the DBN network prediction safety factor arithmetic is 0.09,and the average relative error of the arithmetic is 0.07;(3)when analyzing the wedge failure slope example,the average absolute error of the DBN network prediction safety factor arithmetic is 0.01 and the average relative error of the arithmetic is 0.01;(4)the accuracy of the predicted slope dataset collected by the DBN algorithm is higher than that of the BP neural network algorithm;(5)when applied to the Puyan project,the accuracy of the DBN algorithm in predicting the slope risk level is 99.19%;(6)When applied to the Puyan project,the safety evaluation of the actual project by the DBN algorithm is closer to the reality than the index method.

关 键 词：安全工程 边坡稳定 深度置信网络(DBN) 

分 类 号：X974[环境科学与工程—安全科学]