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A new approach for estimation of rock brittleness based on non-destructive tests
Nondestructive Testing and Evaluation ( IF 2.6 ) Pub Date : 2019-06-04 , DOI: 10.1080/10589759.2019.1623214
Mohammadreza Koopialipoor 1 , Amin Noorbakhsh 2 , Ebrahim Noroozi Ghaleini 3 , Danial Jahed Armaghani 4 , Saffet Yagiz 5
Affiliation  

ABSTRACT This paper aims to propose predictive equations for estimation of rock brittleness as a function of intact rock properties including rock density (r), Schmidt hammer (Rn) and wave velocity (Vp) using two optimization techniques, artificial neural network (ANN) and FA-ANN (Firefly Algorithm and ANN). Using ANN and FA-ANN techniques, 10 different models were developed and compared to find the optimum one implementing some performance indices such as coefficient of determination (R2) and root mean square error (RMSE). In addition, a ranking system was performed to select the best models. It was found that in developing ANN models, the Model number 1 is superior to other 4 models (models 2-5). Likewise, in developing hybrid FA-ANN technique, model number 9 was better than other 4 models (models 6-10). Further, the best models obtained with these two intelligent techniques were compared to show that hybrid model is better than a simple ANN model. It was found that R2, RMSE, and total ranking are obtained as 0.826, 0.1481, and 19 for ANN while those are 0.896, 0.0812 and 36 for FA-ANN, respectively. It was also concluded that the model 9 of FA-ANN technique indicates the best performance among all developed hybrid models.

中文翻译:

基于无损检测的岩石脆性估算新方法

摘要 本文旨在提出预测方程,用于估计岩石脆性作为完整岩石特性的函数,包括岩石密度 (r)、施密特锤 (Rn) 和波速 (Vp),使用两种优化技术,人工神经网络 (ANN) 和FA-ANN(萤火虫算法和人工神经网络)。使用 ANN 和 FA-ANN 技术,开发并比较了 10 种不同的模型,以找到实现某些性能指标的最佳模型,例如确定系数 (R2) 和均方根误差 (RMSE)。此外,还执行了排名系统以选择最佳模型。发现在开发 ANN 模型时,模型 1 优于其他 4 个模型(模型 2-5)。同样,在开发混合 FA-ANN 技术时,模型编号 9 优于其他 4 个模型(模型 6-10)。更多,比较使用这两种智能技术获得的最佳模型,表明混合模型优于简单的 ANN 模型。发现 ANN 的 R2、RMSE 和总排名分别为 0.826、0.1481 和 19,而 FA-ANN 分别为 0.896、0.0812 和 36。还得出结论,FA-ANN 技术的模型 9 在所有开发的混合模型中表现出最佳性能。
更新日期:2019-06-04
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