基于粗糙集和人工神经网络的洞室岩体质量评价

Evaluation of Tunnel Rock Quality with Routh Sets Theory and Artificial Neural Networks

针对洞室岩体质量问题,从洞室工程的角度选取能够反映岩体综合工程特性的6个参数,用可拓评判和专家审定的方法构建了决策样本集;再利用粗糙集理论对原始决策样本集进行约简操作,并分析各指标对决策的相对重要性;最后将约简结果生成的规则作为人工神经网络的输入,建立了洞室岩体质量评价模型。通过工程实例分析对比,该模型有效地简化神经网络的网络结构,减少网络的训练步数,提高网络的学习效率,能够较准确地反映洞室岩体的工程特性。

To evaluate the tunnel rock quality, six parameters reflecting the general properties of rock engineering was selected to build the decision table, which was evaluated by extenics theory and expert examination, and rough sets theory was applied to reduce the original decision table and to analyze the relative importance of every parameter. Finally, the reduction results are transformed into rules, which are used as input of the BP neural networks. Combining rough sets theory with artificial neural networks, then the evaluation model of tunnel rock quality was established. Through the case study, the model can efficiently simplifies the networks structure, reduces the networks training period and has better study efficiency and can more precisely reflect the engineering characteristics of tunnel rock.