Prediction and validation of alternative fillers used in micro surfacing mix-design using machine learning techniques
Authors:
- Rajesh GujarRGRajesh Gujar
- Vinay VakhariaVVVinay Vakharia
- 1 May 2019
- journal article
- research article
- Published by Elsevier in Construction and Building Materials
- Vol. 207, 519-527
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Production of sodium silicate powder from waste glass cullet for alkali activation of alternative bindersCement and Concrete Research, 2019
- Stockwell-common spatial pattern technique for motor imagery-based Brain Computer Interface designComputers and Electrical Engineering, 2018
- Kernel machines and firefly algorithm based dynamic modulus prediction model for asphalt mixes considering aggregate morphologyConstruction and Building Materials, 2018
- Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithmConstruction and Building Materials, 2017
- Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifierJournal of the Brazilian Society of Mechanical Sciences and Engineering, 2017
- A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural NetworksConstruction and Building Materials, 2016
- Modelling the fresh properties of self-compacting concrete using support vector machine approachConstruction and Building Materials, 2016
- Predicting the moment capacity of RC beams exposed to fire using ANNsConstruction and Building Materials, 2015
- Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteinsComputational Biology and Chemistry, 2013
- Economic effects of microsurfacing on Thermally-Cracked pavementsKSCE Journal of Civil Engineering, 2008