投影寻踪是一种解决复杂围岩稳定性评价问题较为有效的不确定性分析方法,但其结果受投影方向的影响和控制。为寻求最优的投影方向向量和体现寻优过程的不确定性,探讨了寻求最佳投影方向向量的反向学习果蝇优化算法,进而利用该算法改进围岩稳定性投影寻踪评价模型。针对投影寻踪方法常出现分级阈值难以划分情形,利用实测样本的最佳投影值,通过逆向云算法构建了不同等级围岩的正态云模型,进而有效解决了阈值难以划分问题。为判定模型评价结果可靠度和评价过程模糊性,引入模糊熵E作为辅助参评量和等级评价结果L共同构成二维评价模式(L, E)。围岩稳定性评价指标多种多样且评价体系不唯一,选取2个采用不同评价指标体系的实例予以应用,并与其他方法作对比分析。结果表明,该模型应用于围岩稳定性评价有效可行,且评价结果客观准确,为围岩稳定性分级提供了新参考。
Abstract
Projection pursuit is an efficient uncertainty analysis method in solving complex stability problems for surrounding rock. Its result is affected and controlled by the projection direction. To find an optimum projection direction vector and reflect the uncertainty of the process, we adopted an opposition-based learning fruit fly optimization algorithm (OBLFOA) to modify the projection pursuit evaluation model for surrounding rock stability. The classification threshold of projection pursuit is difficult to be divided in some special cases. In view of this, we constructed the normal cloud models for different grades of surrounding rock by using the optimal projection value according to the optimum projection value of measured samples. To determine the reliability of the model evaluation results and the fuzziness of the evaluation process, we introduced the fuzzy entropy E as the auxiliary parameter with the evaluation result L forming a two-dimensional evaluation mode (L, E). Since the evaluation indexes of surrounding rock stability are varied and the evaluation system is not unique, we selected two cases with different evaluation index systems for application and compared with other methods. Results indicated that the proposed model is feasibile and effective and the evaluation results are objective and accurate.
关键词
围岩稳定性 /
反向学习果蝇算法(OBLFOA) /
投影寻踪 /
逆向云 /
模糊熵
Key words
surrounding rock stability /
opposition-based learning fruit fly optimization algorithm /
projection pursuit /
backward cloud model /
fuzzy entropy
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基金
国家重点研发计划项目(2017YFC1502405);国家自然科学基金项目(41172274)
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