Geomechanical parameters identification by particle swarm optimization and support vector machine

Abstract

a b s t r a c tBack analysis is commonly used in identifying geomechanical parameters based on the monitored displacements. Conventional back analysis method is not capable of recognizing non-linear relationship involving displacements and mechanical parameters effectively. The new intelligent displacement back analysis method proposed in this paper is the combination of support vector machine, particle swarm optimization, and numerical analysis techniques. The non-linear relationship is efficiently represented by support vector machine. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, a global optimum search on the obtained SVMs by particle swarm optimization can lead to the geomechanical parameters identification effectively.

Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater

“…The SVR would be would be used as an objective function for the PSO optimization process, to generate the outputs from the inputs. Some studies integrating PSO and SVM have been presented in the literatures [16][17][18][19][20][21][22]. However, there is still no research showing whether the hybrid model based on PSO and SVR can be applied in algal blooms problem that occurs in drinking water resources, which affects health of public.…”

Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine

“…Motivated by this problem, many researchers have drawn ideas from the field of biology. A number of such biology-inspired evolutionary techniques have been developed, such as genetic algorithm (GA) [15][16][17][18] and particle swarm optimization (PSO) [19,20], which are widely used for solving optimization problems. PSO is originally attributed to Kennedy and Eberhart, who were inspired by the behavior of bird swarms in 1995 [21].…”

A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

“…For instance, Huang and Dun [18] proposed a PSO-SVM model to improve classification accuracies with an appropriate feature subset. Zhao and Yin [20] take the global optimal search through PSO to obtain the SVM to effectively identify the geomechanical parameters. Selakova et al [26] proposed a practical new hybrid model for short term electrical load forecasting based on PSO-SVM.…”

“…However, there is still a problem that the proper selection of kernel function and its parameters has great influence on the final prediction accuracy. In order to choose the optimal parameters when building the model, some researchers have linked Particle Swarm Optimization algorithms to classical SVM algorithms [18][19][20][21][22][23]. The Particle Swarm Optimization (PSO) algorithm was developed by Eberhart and Kennedy and is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling [24].…”