Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Azimian, Abdolazim

doi:10.1007/s11440-016-0467-3

Cited by 41 publications

(6 citation statements)

References 56 publications

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“…(2013) Sandstone Indonesia (Tutupan) 10 - 26 6.53–23.2 [26] Mudstone Indonesia (Tutupan) 10 - 28 6.53–25.6 Selçuk and Yabalak (2014) Calcareous marl Turkey (Van) 27 - 29.7 38.6–41.3 [27] Marlstone Turkey (Van) 20 - 26 4.5–9.5 Claystone / Argillaceous marl Turkey (Van) 10 - 22 2.5–4.5 Kesimal and Kesimal (2015) Limestone Turkey (Trabzon) 37.6 - 39.5 75–120 [28] Sandy limestone Turkey (Trabzon) 30.6 - 31 22.5 Biomicritic limestone Turkey (Trabzon) 13.5 - 17.5 7.7–18.9 Jobli et al. (2016) Marlstone Sungai Buloh 25.85 20–25 [29] Calcareous marl Sungai Buloh 37.38 30–35 Calcareous marl Sungai Buloh 59.51 38–43 Limestone Sungai Buloh 59.2 42–46 Azimian (2017) Limestone Iran (Shiraz) 59 - 22 28.7–118.4 [30] Rajabi et al. (2017) Limestone Iran (Saveh) 21.3 - 29.6 33.5–42.6 [31] Török (2018) Oolitic limestone Hungary (Budapest), Austria (Vienna) 37–17 18.7–35 …”

Section: Methods Detailsmentioning

confidence: 99%

An empirical classification method for South Pars marls by Schmidt hammer rebound index

et al. 2021

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The presented article provides an experimental classification for South Pars marls (SPM), southwest of Iran, using the Schmidt hammer rebound index, marl geological classes, and SPM geo-engineering characteristics. In this regard, 45 samples of marls (rock) are selected on the studied site and tested by geotechnical in-situ and laboratory tests such as Schmidt hammer, uniaxial compressive strength (UCS), laboratory direct-shear (LDS) to estimate the geo-engineering characteristics of SPM. These specimens are categorised by Pettijohn's marl classification in 3 main groups (concluded argillaceous lime, calcareous marl, and marlstone) and established the geologic class and geo-engineering properties as well as Schmidt hammer rebound index. In the meantime, the geologic classes and the Schmidt index show the logic classification. Thus, this work attempted to prepare the experimental classification based on Pettijohn's marl classification and Schmidt rebound index for SPM. According to geotechnical experiments results, the Schmidt index shows 3 main group variations like Pettijohn's marl classification. • This method can be used to prepare the geologic status based on the Schmidt rebound index. • This method can be useful for detailed decryption of geo-engineering characteristics of different type of marls in the studied area. • This method can be used as a quick link for marl geologic status and geo-engineering features.

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Section: Methods Detailsmentioning

confidence: 99%

An empirical classification method for South Pars marls by Schmidt hammer rebound index

et al. 2021

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“…Son and Kim [17] used the sound signal obtained by hammering a rock to calculate the total energy of the sound, and then used the total energy calculation to calculate the strength of the rock. Azimian [18] developed a model for predicting UCS with P-wave and Schmidt hammer rebound. However, the equipment used in this experiment was laboratory-based and had limited application in the field.…”

Section: Non-destructive Methods For Measuring Rock Surface Strengthsmentioning

confidence: 99%

A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

Han

et al. 2019

Applied Sciences

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Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [−5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.

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“…Determining the UCS of a rock sample in the laboratory requires specialist equipment and intact rock samples free of fissures and veins which are generally not easy to obtain. A viable alternative is to associate the rock's UCS with various physical and mechanical test indexes such as the pulse velocity (V p ), Schmidt hammer rebound number (R n ), effective porosity (n e ), total porosity (n t ), dry density (γ d ), point load index (Is 50 ), shear wave velocity (V s ), Brazilian tensile strength (BTS), slake durability index (SDI), and using simple and multiple regression analyses (Sachpazis 1990;Tuğrul and Zarif 1999;Katz et al 2000;Kahraman 2001;Yılmaz and Sendır 2002;Yaşar and Erdoğan 2004;Dinçer et al 2004;Fener et al 2005;Aydin and Basu 2005;Sousa et al 2005;Shalabi et al 2007;Çobanoğlu and Çelik 2008;Vasconcelos et al 2008;Sharma and Singh 2008;Kılıç and Teymen 2008;Diamantis et al 2009;Yilmaz and Yuksek 2009;Yagiz, 2009;Moradian and Behnia 2009;Khandelwal and Singh 2009;Altindag 2012;Kurtulus et al 2012;Bruno et al 2013;Mishra and Basu 2013;Khandelwal 2013;Tandon and Gupta 2015;Karaman and Kesimal 2015;Ng et al 2015;Armaghani et al 2016a, b;Azimian 2017;Heidari et al 2018;Çelik and Çobanoğlu 2019;Barham et al 2020;Ebdali et al 2020;Teymen and Mengüç 2020;…”

Section: Introductionmentioning

confidence: 99%

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

et al. 2022

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The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.

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Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Cited by 41 publications

References 56 publications

An empirical classification method for South Pars marls by Schmidt hammer rebound index

An empirical classification method for South Pars marls by Schmidt hammer rebound index

A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

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