✦ LIBER ✦

Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

✍ Scribed by U. Atici

Book ID: 108130731
Publisher: Elsevier Science
Year: 2011
Tongue: English
Weight: 448 KB
Volume: 38
Category: Article
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2011.01.156

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Prediction of shrimp growth using an art

Prediction of shrimp growth using an artificial neural network and regression models

✍ Abdoulkarim Esmaeili; Mohammad Hassan Tarazkar 📂 Article 📅 2010 🏛 Springer Netherlands 🌐 English ⚖ 480 KB

Predicting the core compressive strength

Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network

✍ Mucteba Uysal; Harun Tanyildizi 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 651 KB

Predicting the compressive strength of g

Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

✍ Cahit Bilim; Cengiz D. Atiş; Harun Tanyildizi; Okan Karahan 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 336 KB

Simultaneous prediction of coal rank par

Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network

✍ S. Chehreh Chelgani; Sh. Mesroghli; James C. Hower 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 353 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

Prediction of the compressive strength o

Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models

✍ Jafar Sobhani; Meysam Najimi; Ali Reza Pourkhorshidi; Tayebeh Parhizkar 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 716 KB