Long-term Prediction Model of Rockburst in Underground Openings Using Heuristic Algorithms and Support Vector Machines
Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference etc., this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. Based on the theory of statistical learning algorithms, the SVMS is used into classification technique with introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks maximum tangential stress 06, rocks uniaxial compressive strength σc, rocks uniaxial tensile strength σt, stress coefficient σθ/σc, rock brittleness coefficient σc/σt and elastic energy index Wet. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GASVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines(GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.
Rockburst Classification Genetic algorithm(GA) Particle swarm optimization algorithm (PSO) Support vector machines (SVMs)
Zhou Jian Li Xibing Shi Xiuzhi
School of Resources and Safety Engineering, Central South University, Changsha 410083, China School of Resources and Safety Engineering, Central South University, Changsha 410083, China Postdoc
国际会议
The First International Symposium on Mine Safety Science and Engineering (首届矿山安全科学与工程学术会议)
北京
英文
131-151
2011-10-27（万方平台首次上网日期，不代表论文的发表时间）