Intelligent prediction of potential sliding surface of soil slopes based on slope displacement

Abstract

[Objective] In light of the current challenges in predicting potential sliding surfaces of slopes, such as high difficulty and low accuracy, this study attempts to quantitatively predict the potential sliding surface of soil slopes using neural networks, based on real-time slope displacement monitoring data. [Methods] Based on the principle of stochastic potential sliding surface generation, this study employs the discrete element program 3DEC to simulate the entire process of slope instability under randomly generated slip surfaces. Subsequently, a neural network model capable of predicting potential sliding surfaces is developed by integrating sample data and a cascading algorithm, which is then validated through both laboratory tests and real-world engineering case studies. Finally, a dynamic slope safety early-warning system is established. [Results] The indoor test result show that both the established cascade correlation neural network(referred to as the “CC” neural network) model and the backpropagation feedforward neural network(referred to as the “BP” neural network) model can, to some extent, map the implicit relationship between slope displacement and the potential sliding surface. Compared to the BP neural network, the average consistency of the real-time sliding surface curve predicted by the CC neural network is 0.973, by comparison increase of 0.07. Moreover, the average relative error of the predicted cohesive force of the sliding surface soil is 14.99%, by comparison decrease of 9.93%, and the average relative error of the internal friction angle is 10.12%, by comparison decrease of 10.25%. The application of the CC neural network model in practical engineering projects shows that the consistency of the predicted real-time sliding surface curves is greater than 0.99, while the relative error of the real-time predicted values of soil shear strength is less than 14%, with the overall prediction accuracy exhibiting an upward trend. [Conclusion] The result indicate that the formation and stability state of the potential sliding surface in a slope can be further reflected by the distribution and evolutionary trend of surface displacement. Analysis of laboratory slope model tests shows that the CC neural network, owing to its unique self-adaptive architecture, achieves higher accuracy and stronger adaptability in predicting slope sliding surfaces. By integrating slope surface displacement with AI-powered neural network prediction technology, it is possible to rapidly and accurately intelligently predict potential sliding surfaces and their mechanical properties. This approach enables convenient, efficient, and reliable assessment of potential instability hazards in slopes.

Keywords

real-time slope displacement / slope slip surface prediction / discrete element method(DEM) program / neural network model / laboratory test / safety early warning / shear strength / influencing factors