✦ LIBER ✦

Bayesian prediction of elastic modulus of intact rocks using their uniaxial compressive strength

✍ Scribed by Feng, Xianda; Jimenez, Rafael

Book ID: 123343804
Publisher: Elsevier Science
Year: 2014
Tongue: English
Weight: 944 KB
Volume: 173
Category: Article
ISSN: 0013-7952
DOI: 10.1016/j.enggeo.2014.02.005

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Predicting uniaxial compressive strength

Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer

✍ Saffet Yagiz 📂 Article 📅 2008 🏛 Springer 🌐 English ⚖ 529 KB

Predicting the Uniaxial Compressive Stre

Predicting the Uniaxial Compressive Strength and Static Young’s Modulus of Intact Sedimentary Rocks Using the Ultrasonic Test

✍ Moradian, Z. A.; Behnia, M. 📂 Article 📅 2009 🏛 American Society of Civil Engineers 🌐 English ⚖ 436 KB

On the Ratios between Elastic Modulus an

On the Ratios between Elastic Modulus and Uniaxial Compressive Strength of Heterogeneous Carbonate Rocks

✍ V. Palchik 📂 Article 📅 2010 🏛 Springer 🌐 English ⚖ 675 KB

Application of genetic programming to pr

Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks

✍ Beiki, Morteza; Majdi, Abbas; Givshad, Ali Dadi 📂 Article 📅 2013 🏛 Elsevier Science 🌐 English ⚖ 761 KB

Estimating uniaxial compressive strength

Estimating uniaxial compressive strength of rocks using genetic expression programming

✍ Ozbek, Ahmet; Unsal, Mehmet; Dikec, Aydin 📂 Article 📅 2013 🏛 Elsevier 🌐 English ⚖ 790 KB

Prediction of uniaxial compressive stren

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

✍ S DEHGHAN; Gh SATTARI; S CHEHREH CHELGANI; MA ALIABADI 📂 Article 📅 2010 🏛 Elsevier 🌐 English ⚖ 635 KB

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were use