SVM Slope Deformation Prediction via PSO Optimization
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Solution Overview
Problem
Current high slope deformation prediction methods, such as statistical regression analysis and BP neural networks, face challenges in accuracy and real-time prediction due to complex and uncertain factors, requiring extensive data and slow convergence.
Innovation Solution
A prediction method utilizing a Support Vector Machine (SVM) model with a radial basis function kernel, optimized by a Particle Swarm Optimization (PSO) algorithm, to determine optimal parameters for real-time deformation prediction, incorporating historical deformation data and adaptive parameter adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical regression model is used for high slope deformation prediction, then the model is widely studied and applied, but the prediction accuracy is insufficient due to many affecting factors and the model being only a speculation
Solution Approach 1:
The patent transforms the regression model from a speculative statistical approach to a physics-based model by incorporating parameters representing actual physical mechanisms (water-seepage pressure, gravitational force, shear strength). This allows the model to account for multiple affecting factors through explicit physical parameters rather than empirical correlations, improving prediction accuracy while maintaining manageable complexity.
Solution Approach 2:
The patent introduces a stability coefficient as an intermediary parameter that integrates multiple physical factors (cohesion, internal friction angle, water-seepage pressure, gravitational force) into a single predictive metric. This intermediary allows the complex interaction of multiple factors to be captured without requiring a overly complex multi-parameter model, resolving the contradiction between accuracy and complexity.
2Adaptability or versatility
If BP neural network model is used for high slope deformation prediction, then the model can handle complex factors, but the training speed is excessively low and convergence is slow due to requiring large number of training samples
Solution Approach 1:
The patent extracts the essential physical mechanisms from the complex black-box neural network approach and formulates them into explicit physical equations. By taking out only the critical physical factors (water-seepage pressure, gravity, shear strength) and representing them through dedicated parameters, the model achieves comparable adaptability to handling complex factors while eliminating the need for extensive training data and iterative optimization, thus dramatically improving training speed.
Solution Approach 2:
The patent replaces the mechanical learning process of neural networks (iterative weight adjustment through backpropagation) with a direct physics-based calculation system. Instead of using a mechanical system that requires大量 training samples and slow convergence, the model uses analytical solutions based on physical laws, achieving the same adaptability to complex factors instantaneously without iterative training.
3Ease of operation
If human experience is used for high slope collapse prediction, then the prediction can be made with available data, but the real-time prediction accuracy is not high due to randomness and uncertainty of collapse factors
Solution Approach 1:
The patent transforms subjective human experience judgments into objective quantitative parameters representing physical mechanisms (water-seepage pressure gradient, gravitational force, shear strength). By changing from qualitative experience-based parameters to quantitative physics-based parameters, the model maintains ease of operation through clear parameter definitions while dramatically improving prediction accuracy by eliminating randomness and uncertainty associated with human judgment.
Data Source
AI summary
The present invention provides a prediction method and system of high slope deformation. First, historical deformation data of each period of each part of a high slope is obtained as sample data; the sample data is divided into training samples and test samples; then a parameter group of a Support Vector Machine (SVM) model is optimized by using the training samples and a particle Swarm Optimization (PSO) algorithm to determine an optimal parameter group of the SVM model, to obtain a trained SVM model; whether the trained SVM model satisfies a condition is verified by using the test samples, and when the SVM model does not satisfy the condition, an optimal parameter group of the SVM model is re-determined; and finally the deformation of each area of the high slope is predicted by using the SVM model that satisfies the condition.