Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Predictive modelling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners

(2021) Furqan Farooq et al.   JOURNAL OF CLEANER PRODUCTION

Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm

(2021) Jiandong Huang et al.   ENGINEERING WITH COMPUTERS

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

(2021) Ayaz Ahmad et al.   Materials

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

(2021) Ayaz Ahmad et al.   Materials

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach

(2021) Hongwei Song et al.   Materials

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

(2021) Ayaz Ahmad et al.   Materials

An Experimental and Empirical Study on the Use of Waste Marble Powder in Construction Material

(2021) Muhammad Sufian et al.   Materials

Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms

(2021) Hongwei Song et al.   CONSTRUCTION AND BUILDING MATERIALS

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

(2021) Waqas Ahmad et al.   Materials

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

(2021) Ayaz Ahmad et al.   Polymers

A systematic review of waste materials in cement-based composites for construction applications

(2021) Xuemei Li et al.   Journal of Building Engineering

A scientometric review of waste material utilization in concrete for sustainable construction

(2021) Waqas Ahmad et al.   Case Studies in Construction Materials

Sustainable approach of using sugarcane bagasse ash in cement-based composites: A systematic review

(2021) Waqas Ahmad et al.   Case Studies in Construction Materials

Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques

(2021) Ayaz Ahmad et al.   Case Studies in Construction Materials

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

(2020) Dong Van Dao et al.   Sustainability

An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete

(2020) Taihao Han et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction Models for the Mechanical Properties of Self-Compacting Concrete with Recycled Rubber and Silica Fume

(2020) Robert Bušić et al.   Materials

Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm

(2020) De-Cheng Feng et al.   ADVANCED ENGINEERING INFORMATICS

Post-fire compressive strength of recycled PET aggregate concrete reinforced with steel fibers: Optimization and prediction via RSM and GEP

(2020) Mahdi Nematzadeh et al.   CONSTRUCTION AND BUILDING MATERIALS

Combined use of recycled powder and recycled coarse aggregate derived from construction and demolition waste in self-compacting concrete

(2020) Zhenhua Duan et al.   CONSTRUCTION AND BUILDING MATERIALS

A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)

(2020) Furqan Farooq et al.   Applied Sciences-Basel

New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach

(2020) Muhammad Faisal Javed et al.   Crystals

Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete

(2020) Muhammad Faisal Javed et al.   Crystals

Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete

(2020) Fahid Aslam et al.   Advances in Civil Engineering

Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression

(2019) Junfei Zhang et al.   CONSTRUCTION AND BUILDING MATERIALS

Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns

(2019) Payam Sarir et al.   ENGINEERING WITH COMPUTERS

Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach

(2019) Prasenjit Saha et al.   NEURAL COMPUTING & APPLICATIONS

Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete

(2019) Mohammad Azimi-Pour et al.   CONSTRUCTION AND BUILDING MATERIALS

Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm

(2019) Amir Ali Shahmansouri et al.   CONSTRUCTION AND BUILDING MATERIALS

Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach

(2019) De-Cheng Feng et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification

(2018) Sundari Selvaraj et al.   NEURAL COMPUTING & APPLICATIONS

Prediction of compressive strength of self-compacting concrete by ANFIS models

(2018) Behnam Vakhshouri et al.   NEUROCOMPUTING

Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm

(2018) Dhanya Sathyan et al.   International Journal of Concrete Structures and Materials

Self-compacting concrete strength prediction using surrogate models

(2017) Panagiotis G. Asteris et al.   NEURAL COMPUTING & APPLICATIONS

M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

(2017) Ali Kaveh   Periodica Polytechnica-Civil Engineering

Stochastic Nonlinear Behavior of Reinforced Concrete Frames. II: Numerical Simulation

(2016) Decheng Feng et al.   JOURNAL OF STRUCTURAL ENGINEERING

Prediction of properties of self-compacting concrete containing fly ash using artificial neural network

(2016) Omar Belalia Douma et al.   NEURAL COMPUTING & APPLICATIONS

Stochastic Nonlinear Behavior of Reinforced Concrete Frames. II: Numerical Simulation

(2016) Decheng Feng et al.   JOURNAL OF STRUCTURAL ENGINEERING

Recycled aggregate from C&D waste & its use in concrete – A breakthrough towards sustainability in construction sector: A review

(2014) Monalisa Behera et al.   CONSTRUCTION AND BUILDING MATERIALS

Experimental analysis of properties of recycled coarse aggregate (RCA) concrete with mineral additives

(2014) Ö. Çakır   CONSTRUCTION AND BUILDING MATERIALS

Strength prediction model and methods for improving recycled aggregate concrete

(2013) Khaleel H. Younis et al.   CONSTRUCTION AND BUILDING MATERIALS

Carbon dioxide equivalent (CO2-e) emissions: A comparison between geopolymer and OPC cement concrete

(2013) Louise K. Turner et al.   CONSTRUCTION AND BUILDING MATERIALS

Are geopolymers more suitable than Portland cement to produce high volume recycled aggregates HPC?

(2012) F. Pacheco-Torgal et al.   CONSTRUCTION AND BUILDING MATERIALS

Closed-loop recycling of recycled concrete aggregates

(2012) Iqbal Marie et al.   JOURNAL OF CLEANER PRODUCTION

Microstructure-linked strength properties and impact response of conventional and recycled concrete reinforced with steel and synthetic macro fibres

(2011) Savaş Erdem et al.   CONSTRUCTION AND BUILDING MATERIALS

Comparative environmental assessment of natural and recycled aggregate concrete

(2010) S. Marinković et al.   WASTE MANAGEMENT

Estimating carbon dioxide emissions for aggregate use

(2009) A. Thomas et al.   Proceedings of the Institution of Civil Engineers-Engineering Sustainability