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A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests
Journal of Nondestructive Evaluation ( IF 2.8 ) Pub Date : 2020-10-28 , DOI: 10.1007/s10921-020-00725-x
Diyuan Li , Danial Jahed Armaghani , Jian Zhou , Sai Hin Lai , Mahdi Hasanipanah

The uniaxial compressive strength (UCS) is considered as a significant parameter related to rock material in design of geotechnical structures connected to the rock mass. Determining UCS values in laboratory is costly and time consuming, hence, its indirect determination through use of rock index tests is of a great interest and advantage. This study presents a prediction process of the UCS values through the use of three non-destructive tests i.e., p-wave velocity, Schmidt hammer and density. This process was done by developing an intelligent predictive technique namely the group method of data handling (GMDH). Before constructing intelligence system, a series of experimental equations were proposed using three non-destructive tests. The results showed that there is a need to propose new model with taking advantages of all three non-destructive tests results. Then, several GMDH models were built through the use of various parametric studies on the most effective GMDH factors. For comparison purposes, an artificial neural network (ANN) was also modelled to predict rock strength. The obtained results of the ANN and GMDH were assessed based on system error and coefficient of determination values. The results confirmed that the proposed GMDH model is an applicable, powerful, and practical intelligence system that is able to provide an acceptable accuracy level for predicting rock strength.

中文翻译:

使用三种无损测试预测岩石材料强度的 GMDH 预测模型

在与岩体相连的岩土结构设计中,单轴抗压强度 (UCS) 被认为是与岩石材料相关的重要参数。在实验室确定 UCS 值既昂贵又费时,因此,通过使用岩石指数测试间接确定其具有很大的兴趣和优势。本研究通过使用三种无损测试,即 p 波速度、施密特锤和密度,介绍了 UCS 值的预测过程。这个过程是通过开发一种智能预测技术来完成的,即数据处理的组方法(GMDH)。在构建智能系统之前,通过三个无损测试提出了一系列实验方程。结果表明,有必要提出新的模型,以利用所有三种无损检测结果。然后,通过对最有效的 GMDH 因素进行各种参数研究,建立了几个 GMDH 模型。为了进行比较,还模拟了人工神经网络 (ANN) 来预测岩石强度。ANN 和 GMDH 获得的结果基于系统误差和决定系数值进行评估。结果证实,所提出的 GMDH 模型是一种适用的、强大的、实用的智能系统,能够为预测岩石强度提供可接受的精度水平。还模拟了人工神经网络 (ANN) 来预测岩石强度。ANN 和 GMDH 获得的结果基于系统误差和决定系数值进行评估。结果证实,所提出的 GMDH 模型是一种适用的、强大的、实用的智能系统,能够为预测岩石强度提供可接受的精度水平。还模拟了人工神经网络 (ANN) 来预测岩石强度。ANN 和 GMDH 获得的结果基于系统误差和决定系数值进行评估。结果证实,所提出的 GMDH 模型是一种适用的、强大的、实用的智能系统,能够为预测岩石强度提供可接受的精度水平。
更新日期:2020-10-28
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