Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

Ceryan, Nurcihan; Okkan, Umut; Kesimal, Ayhan

doi:10.1007/s12665-012-1783-z

Cited by 151 publications

(23 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These techniques become more attractive because of their capability of information processing, such as non-linearity, high parallelism, robustness, fault and failure tolerance and their ability to generalize. Besides, these techniques have been successfully employed to solve problems in civil engineering field [19][20][21][22][23][24][25][26][27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses

Tajeri

Sadrossadat

Bazaz

2015

International Journal of Rock Mechanics and Mining Sciences

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses

Tajeri

Sadrossadat

Bazaz

2015

International Journal of Rock Mechanics and Mining Sciences

View full text Add to dashboard Cite

“…Yapay sinir ağları modellemesinde MATLAB bilgisayar yazılımından yararlanılmış olup, bu amaçla kurulan YSA modelleri kapsamında çok katmanlı (neural network fitting işlemidir (Ceryan et al 2013). Ayrıca, yöntem esas olarak kendisine tanıtılan pek çok değişken arasındaki ilişkileri ve bilgileri öğrenerek edindiği tecrübelerden farklı yaklaşımların ortaya konması üzerine kuruludur (Yurdakul 2009).…”

Section: Yapay Sinir Ağlarıunclassified

Untitled

Sakız¹,

Yaralı²,

Aydın³

2017

KSEJ

View full text Add to dashboard Cite

Bu çalışmanın amacı; kayaçların mekanik, indeks ve petrografik özelliklerinden yararlanarak pratik bir delinebilirlik indeks modeli (DRI) geliştirmektir. DRI'nın tahmin edilmesi için geliştirilen modellerde yapay sinir ağları (YSA) kullanılmıştır. Bu amaçla, laboratuvarda farklı kayaçlar üzerinde yapılan delinebilirlik deney verileri kullanılmıştır. Ayrıca kayaçların; mekanik (tek eksenli basınç ve Brezilian çekme dayanımı) ve indeks (Schmidt sertlik ve nokta yükü dayanımı) özelliklerinin yanı sıra petrografik analiz (ortalama tane boyu, eş değer kuvars içeriği (%), çimentolanma derecesi) sonuçları da YSA modellemesi için kullanılmıştır. DRI ve kayaç özellikleri arasındaki korelasyon incelenmiş ve yüksek korelasyona sahip değişkenler YSA'na girdi parametresi olarak seçilmiştir. Buna göre 14 veri ağın eğitimi için, 7 veri ise test için kullanılmış ve en anlamlı sonucu veren model belirlenmiştir. Testler sonucunda tahmin edilen DRI sonuçları ile deneylerden elde edilen DRI sonuçlarının karşılaştırması yapılmıştır. Sonuç olarak YSA ile modellenen DRI sonuçlarının deneysel verilerden elde edilen sonuçlara oldukça yakın olduğu görülmüştür.

show abstract

“…The uniaxial compressive strength (UCS) of an intact rock is an important geotechnical parameter for engineering applications. This parameter has a great deal of importance within rock mechanic applications such as tunnel and dam design, rock blasting and drilling, mechanical rock excavation and slope stabilization [12]. The method for measuring UCS on a core sample has been standardized by both ASTM and ISRM.…”

Section: Introductionmentioning

confidence: 99%

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

Azimian

2016

Acta Geotech.

View full text Add to dashboard Cite

Uniaxial compressive strength (UCS) of an intact rock is an important geotechnical parameter for engineering applications. Using standard laboratory tests to determine UCS is a difficult, expensive and time-consuming task. The main purpose of this study is to develop a general model for predicting UCS of limestone samples and to investigate the relationships among UCS, Schmidt hammer rebound and P-wave velocity (V P ). For this reason, some samples of limestone rocks were collected from the southwestern Iran. In order to evaluate a correlation, the measured and predicted values were examined utilizing simple and multivariate regression techniques. In order to check the performance of the proposed equation, coefficient of determination (R 2 ), root-mean-square error, mean absolute percentage error, variance accounts for (VAF %), Akaike Information Criterion and performance index were determined. The results showed that the proposed equation by multivariate regression could be applied effectively to predict UCS from its combinations, i.e., ultrasonic pulse velocity and Schmidt hammer hardness. The results also showed that considering high prediction performance of the models developed, they can be used to perform preliminary stages of rock engineering assessments. It was evident that such prediction studies not only provide some practical tools but also contribute to better understanding of the main controlling index parameters of UCS of rocks.Keywords Limestone Á P-wave velocity (V P ) Á Schmidt hammer rebound (SHR) Á Simple and multivariate regression Á Uniaxial compressive strength

show abstract

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

Cited by 151 publications

References 47 publications

Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses

Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses

Untitled

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

Contact Info

Product

Resources

About