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Estimation of elastic constant of rocks using an ANFIS approach

机译：使用ANFIS方法估算岩石的弹性常数

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摘要

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.
机译：岩石的工程特性在岩石开挖和施工计划中发挥着至关重要的作用，以最佳方式利用地球资源，从而具有更高的安全性和对周围环境的破坏。结构的设计和施工受岩体的物理力学特性影响。杨氏模量提供了有关应力场变化引起的岩体变形的大小和特征的见解。在实验室中测定杨氏模量是非常耗时且昂贵的。因此，基本的岩石特性（如点荷载，密度和吸水率）已用于预测杨氏模量。点载荷，密度和吸水率可以在现场以及实验室轻松确定，并且是表征岩体的相关属性。人工神经网络（ANN），模糊推理系统（FIS）和神经模糊技术是有前途的技术，近年来已被证明非常可靠。在目前的研究中，神经模糊系统被用来预测岩石的杨氏模量，以克服神经网络和模糊逻辑的局限性。总共85个数据集用于训练网络，而10个数据集用于测试和验证网络规则。网络性能指标的相关系数，平均绝对百分比误差（MAPE），均方根误差（RMSE）和方差占（VAF）分别为0.6643、7.583、6.799和91.95，具有较高的性能。预测神经模糊系统，用于预测复杂的岩石参数。

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