Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach
- 1 November 2024
- journal article
- research article
- Published by Springer Nature in Innovative Infrastructure Solutions
- Vol. 9 (11) , 1-20
Abstract
No abstract availableKeywords
This publication has 47 references indexed in Scilit:
- Enhancing Sustainable Concrete Production by Utilizing Fly Ash and Recycled Concrete Aggregate with Experimental Investigation and Machine Learning ModelingJournal of Building Pathology and Rehabilitation, 2024
- Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strengthNeural Computing and Applications, 2021
- Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environmentEnvironmental Science and Pollution Research, 2021
- Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beamsEngineering with Computers, 2021
- Mechanical properties of lime-treated clay reinforced with different types of randomly distributed fibersArabian Journal of Geosciences, 2018
- Strength reduction factors for structural rubbercreteFrontiers of Structural and Civil Engineering, 2014
- Influence of free water content on the compressive mechanical behaviour of cement mortar under high strain rateSādhanā, 2011
- Effect of the addition of synthetic fibers to concrete thin slabs on plastic shrinkage crackingConstruction and Building Materials, 2010
- Self compactability of high volume hybrid fiber reinforced concreteConstruction and Building Materials, 2007
- An experimental study on strength development of concrete containing fly ash and optimum usage of fly ash in concreteCement and Concrete Research, 2005