| In recent years,with the increase in the number and scale of various geotechnical engineering projects,under the influence of natural factors such as rainstorms and earthquakes,there have been more and more slope failure accidents,and the reliability of slope safety is particularly important.The traditional primary reliability solution method has limited accuracy,and the slope function is highly nonlinear and difficult to express explicitly.Therefore,this thesis proposes a combination of BP neural network and Laplace asymptotic method for slope reliability analysis.The research has important theoretical reference and practical value.Firstly,taking the bedding slope as the research object,the limit state equation is deduced,and the coordinates of the check point and the primary reliability result are obtained by using the mapping transformation method.On this basis,the Laplace asymptotic method is used to correct the primary result,and the quadratic reliability result of the slope is obtained.Calculate the error value between the two reliability results and the Monte Carlo method respectively to verify the feasibility and accuracy of the Laplace asymptotic method.Then this method is extended to general slope reliability analysis.The random variables in the slope are sampled using the Latin hypercube sampling method.The corresponding safety factor of slope is calculated based on finite element method.The sample data is brought into the BP neural network for training,and after testing its test fitting effect,the explicit function expression of the slope is derived based on the functional relationship between neurons in each layer.Then,the failure probability of slope is calculated by Laplace asymptotic method.Finally,the method is used to analyze the influence of slope parameters.The main analysis conclusions of this thesis are as follows:(1)The slope failure probability calculated by the Monte Carlo method is regarded as a relatively accurate solution.The calculation accuracy of the Laplace asymptotic method is higher than that of the first-order second moment method,and the convergence speed is fast.(2)The BP neural network has a good fitting effect on the highly nonlinear and implicit slope function,and the explicit function expression of the slope can be derived based on the functional relationship between neurons in each layer.(3)The slope safety factor prediction model established based on BP neural network,for the same type of slope,the corresponding slope safety factor value can be directly obtained by directly bringing in the corresponding parameter value,without the need for complicated finite element modeling,and the calculation process has high efficiency and small error.(4)Combining BP neural network and Laplace asymptotic method for slope reliability analysis,it can not only obtain high-precision reliability calculation results,but also avoid a lot of repeated calculation work,and improve the efficiency of slope reliability calculation and analysis. |
