砂岩试样间接抗拉强度预测新方法

IF 1.1 Q3 MINING & MINERAL PROCESSING
H. Fattahi
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引用次数: 1

摘要

岩石的抗拉强度(σt)在几种土木结构的可靠施工中起着重要作用,如坝基、隧道和开挖类型。对于某些项目来说,在实验室中测定σt可能是昂贵、困难和耗时的。由于与实验程序相关的困难,通常优选以间接方式评估σt。基于这些原因,本工作使用自适应网络模糊推理系统(ANFIS)建立了一个预测模型,用于从砂岩样品的物理性质间接预测其σt。实现了两个ANFIS模型,即ANFIS减法聚类法(SCM)和ANFIS模糊c-均值聚类法(FCM)。ANFIS模型应用于开源文献中可用的数据。在这些模型中,孔隙率、比重、干容重和饱和容重被用作输入参数,而测量的σt是输出参数。根据两个性能指标,即均方误差(MSE)和决定系数(R2),检查所提出的预测模型的性能。这项工作的结果表明,ANFIS-CM是一种高精度预测σt的可靠方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Method for Predicting Indirect Tensile Strength of Sandstone Rock Samples
The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive network-based fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFIS-subtractive clustering method (SCM) and ANFIS-fuzzy c-means clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFIS-SCM is a reliable method to predict σt with a high degree of accuracy.
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
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0
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