岩体失稳预测是保障采矿安全生产的重要手段，由于岩体在力学性质上存在非线性、非均匀性、非连续性、各向异性等特点，导致岩体内部状态难以描述，建立在传统单一物理量监测基础上的岩体稳定性评价方法难以满足要求。根据声发射现象的不可逆性，通过采集到岩体失稳所释放的声发射信号就可以获得其岩体内部结构变化的实时状态。因此，基于D-S证据理论，提出一种基于加权D-S证据理论融合多域特征的岩体失稳预测方法。在实验室条件下，以红砂岩为实验对象采用RMT-150C 型岩石力学试验系统进行单轴压缩试验，采集岩体失稳声发射信号，提取岩体失稳声发射信号的多域特征参数，通过敏感特征评价方法，筛选出岩体失稳各阶段的敏感特征参数，将多域敏感特征参数作为证据源，分别经遗传算法优化的BP神经网络预测模型进行初步预测，并将输出结果采用后验概率建模方法计算出各证据的基本概率分配函数，再引入相似性测度优化基本概率分配函数，最后利用加权D-S证据理论建立多域特征融合预测模型。实验结果表明:通过加权D-S证据理论将多域特征信息进行决策级融合，能够有效提升岩体失稳预测模型的准确率;采用多域特征参数取代单一特征参数的方法，为预测模型提供更可信的信号来源;随着融合的特征参数越多，预测模型的准确率越高;采用加权思想引入相似性测度对基本概率分配函数进行改进，能够有效消除高冲突证据导致传统D-S证据融合算法融合失效的问题。

Rock mass instability prediction plays an important role in ensuring the safe production of mines. Due to the features of rock mass like nonlinearity,non-uniformity,discontinuity and anisotropy,it is difficult to describe the inter- nal state of rock mass. The method of rock mass stability evaluation based on single physical quantity monitoring is hard to meet the requirements. The real-time state of internal rock structure could be obtained in the acoustic emission signal from the rock mass instability based on the irreversibility of acoustic emission phenomenon. Therefore,originated from the D-S evidence theory,a rock mass instability prediction method based on the weighted D-S evidence theory and fusion multi-domain features is proposed. Under laboratory conditions,red sandstone,as the experimental object,is tested in uniaxial compression with type RMT-150C rock mechanics test system and the rock mass instability acoustic emission signal is collected. Subsequently,the multi-domain features in the acoustic emission signal of unstable rock mass are firstly extracted. The sensitive features of unstable rock mass are selected in each stage by the sensitive fea- ture evaluation method. Evidenced from the multi-domain sen-sitive features,an optimized BP neural network predic- tion model is used for preliminary prediction. Secondly,the basic probability distribution function of each evidence with the model outputs is calculated by the posterior probability modeling method. Then,the similarity measurement optimi- zation is applied on the basic probability distribution function. Lastly,a prediction model based on weighed D-S evi- dence theory and multi-domain features fusion is built. The experimental results show that the accuracy of prediction model of rock mass instability has been improved manifestly through the multi-domain features fusion based on D-S evidence theory in the decision level. The replacement of single feature parameter with multi-domain feature fusion in the data extraction provides more credible inputs for the prediction model. The more features fused in the model,the more accurate outputs will be obtained. Modifying the basic probability distribution function by weighed thinking and similarity measurement optimization could effectively eliminate the high-conflict evidences,which might lead to tradi- tional D-S evidence fusion algorithm failure.