基于M5P-SVR故障预测的地下洞室施工仿真

地下洞室群施工仿真是分析地下洞室群施工过程的重要手段。针对传统仿真模型难以实现对出渣运输时间的高精度仿真计算，而且在量化运输机械故障对施工进度的影响时存在主观性强、误差大等不足，本研究提出了基于M5P-SVR故障预测的地下洞室群施工仿真模型，模型的建立包括以下两个方面：（1）对传统CYCLONE模型中的出渣模块进行了改进，建立了交通运输仿真回路来计算出渣运输时间，提高了这一关键工艺的仿真精度；（2）科学地考虑地质等外在因素的影响，结合M5P模型树训练规则的简单有效的优点以及支持向量机回归（SVR）可以有效解决小样本、非线性预测问题的优势，提出了基于M5P-SVR的运输机械故障预测方法，交叉验证结果表明该方法有效地提高了预测精度。最后采用该模型对某实际工程进行仿真模拟并与传统方法计算结果进行对比分析，分析结果验证了M5P-SVR机械故障预测方法的有效性及该仿真模型的准确性和优越性。

Simulations of underground cavern construction based on M5P-SVR failure prediction

Simulations of underground cavern group construction are important to analysis of the construction process. It is difficult to achieve high-accuracy simulation of slag transportation time by using traditional simulation models that suffer from problems such as large subjective errors in quantifying the influence of transportation machinery failure on construction progress. This study develops a new simulation model of underground cavern group construction to predict this failure using a M5P-SVR model. This model improves the accuracy in two aspects. Firstly, it improves the slag module adopted in the traditional CYCLONE model and uses a transportation simulation circuit to calculate slag transportation time with an improved accuracy. Secondly, it considers certain external factors more rationally, adopts the simple, effective training rules of the M5P model tree, and is equipped with the support vector machine regression (SVR) that can effectively solve those nonlinear prediction problems that are based on small sample dataset. Combining these two advantages, we develop the M5P-SVR model based on a prediction method of transportation machinery faults, and verify its significant improvement on prediction accuracy using a cross validation method. Finally, our new model is applied to the simulations of a real project and compared with the traditional method, verifying its accuracy and superiority and the effectiveness of M5P-SVR.