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首页> 外文期刊>International Journal of Rock Mechanics and Mining Sciences >Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass
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Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass

机译：各种优化技术的应用及其性能预测在岩体中TBM渗透率的预测

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The aim of this study is to develop prediction models for estimating tunnel boring machine (TBM) performance using various optimization techniques including Differential Evolution (DE), Hybrid Harmony Search (HS-BFGS) and Grey Wolf Optimizer (GWO), and then to compare the results obtained from introduced models and also in literature. For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. From each modeling technique, seven different models, (M1-M7) were developed using the assortment of datasets having various percentage of rock type. In order to find out the optimal values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. Further, the developed models were compared according to the coefficient of correlations (R-2), computer process unit (CPU) and number of function evaluation (NFE) values to choice the best accurate and most efficient model. It is found that there is no salient difference between the models according to the R-2 values; however, it is concluded that the M7 generated via HS-BFGS algorithm consistently converges faster than both the DE and GWO. Also, total CPU time required by HS-BFGS for M7 was the shortest one. As a result, Model 7 developed using the HS-BFGS is considered to be better, especially for simulations in which computational time and efficiency is a critical factor. (C) 2015 Elsevier Ltd. All rights reserved.
机译：这项研究的目的是使用包括差分进化（DE），混合和声搜索（HS-BFGS）和灰狼优化器（GWO）在内的各种优化技术来开发用于估算隧道掘进机（TBM）性能的预测模型，然后进行比较从介绍的模型以及文献中获得的结果。为此，选择了纽约市的皇后区水隧道作为案例研究来测试所提出的模型。从每种建模技术中，使用各种岩石类型百分比的数据集，开发了七个不同的模型（M1-M7）。为了找出参数的最佳值并防止过度拟合，随机选择了总数据的80％作为训练集，其余的用于测试模型。此外，根据相关系数（R-2），计算机处理单元（CPU）和功能评估（NFE）值的数量对开发的模型进行比较，以选择最准确，最有效的模型。发现根据R-2值，模型之间没有显着差异。然而，可以得出结论，通过HS-BFGS算法生成的M7始终比DE和GWO收敛更快。同样，HS-BFGS为M7所需的总CPU时间最短。结果，使用HS-BFGS开发的Model 7被认为是更好的模型，特别是对于其中计算时间和效率是关键因素的仿真而言。 （C）2015 Elsevier Ltd.保留所有权利。

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