CAJ | 학술논문

传统的固体力学方法在描述岩石的各种地质因素与其力学性态之间的复杂非线性关系时存在困难.引入粒子群算法(PSO)对支持向量机(SVM)进行优化,提出岩石力学性态预测的粒子群优化支持向量机模型(PSO-SVM).该模型利用SVM来建立岩石地质因素与力学性态之间的非线性关系;同时利用PSO对SVM参数进行全局寻优,避免人为选择参数的盲目性,从而提高模型的预测精度.将PSO-SVM应用到岩石压缩系数的预测中,并与传统的BP神经网络(BP-NN)进行对比分析.结果显示,PSO-SVM的预测精度较BP-NN有较大的提高,从而表明PSO-SVM在岩石力学性态预测中的可行性和有效性.
전통적고체역학방법재묘술암석적각충지질인소여기역학성태지간적복잡비선성관계시존재곤난.인입입자군산법(PSO)대지지향량궤(SVM)진행우화,제출암석역학성태예측적입자군우화지지향량궤모형(PSO-SVM).해모형이용SVM래건립암석지질인소여역학성태지간적비선성관계;동시이용PSO대SVM삼수진행전국심우,피면인위선택삼수적맹목성,종이제고모형적예측정도.장PSO-SVM응용도암석압축계수적예측중,병여전통적BP신경망락(BP-NN)진행대비분석.결과현시,PSO-SVM적예측정도교BP-NN유교대적제고,종이표명PSO-SVM재암석역학성태예측중적가행성화유효성.
It is difficult to describe the complex nonlinear relationship between all kinds of geological factors of rock and their mechanical behaviors. A new model for forecasting the mechanical behaviors of rock is proposed by combining the particle swarm optimization(PSO) and the support vector machines(SVM),which is support vector machine based on particle swarm optimization(PSO-SVM). The model,on one hand,uses the nonlinear characteristics of SVM to establish the nonlinear relationship between geological factors of rock and their mechanical behaviors. On the other hand,the penalty factor and kernel function parameter of SVM are optimized by PSO,by which the accuracy of the parameters used in the model is ensured as well as the precision of forecasting result. The model is applied to forecast the coefficient of compressibility of rock and the result is compared with that of back propagation neural network(BP-NN). It is shown that the forecasting precision of PSO-SVM is higher than that of BP-NN,which indicates that the model here is feasible and effective.