Predicting the compressive strength of concrete with fly ash admixture using 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

An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements

(2021) Sławomir Czarnecki et al.   MEASUREMENT

Granite Powder vs. Fly Ash for the Sustainable Production of Air-Cured Cementitious Mortars

(2021) Adrian Chajec   Materials

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

(2021) Ayaz Ahmad et al.   Materials

Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

(2021) Jesika Rahman et al.   ENGINEERING STRUCTURES

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

(2021) Ayaz Ahmad et al.   Materials

A comparative investigation using machine learning methods for concrete compressive strength estimation

(2021) Kadir Güçlüer et al.   Materials Today Communications

Experimental Investigation of Hybrid Carbon Nanotubes and Graphite Nanoplatelets on Rheology, Shrinkage, Mechanical, and Microstructure of SCCM

(2020) Furqan Farooq et al.   Materials

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

(2020) Robert Bušić et al.   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

Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete

(2020) Miao Su et al.   CONSTRUCTION AND BUILDING MATERIALS

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

(2019) Jie Dou et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Modelling the Influence of Waste Rubber on Compressive Strength of Concrete by Artificial Neural Networks

(2019) Marijana Hadzima-Nyarko et al.   Materials

Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment

(2019) Hao Ling et al.   CONSTRUCTION AND BUILDING MATERIALS

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

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

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

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

CO2 emission patterns in shrinking and growing cities: A case study of Northeast China and the Yangtze River Delta

(2019) Huijuan Xiao et al.   APPLIED ENERGY

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

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

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

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

(2018) Zaher Mundher Yaseen et al.   ADVANCES IN ENGINEERING SOFTWARE

Specimen shape and size effects on the concrete compressive strength under static and dynamic tests

(2018) Mei Li et al.   CONSTRUCTION AND BUILDING MATERIALS

Emerging artificial intelligence methods in structural engineering

(2018) Hadi Salehi et al.   ENGINEERING STRUCTURES

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

The development of a low carbon binder produced from the ternary blending of cement, ground granulated blast furnace slag and high calcium fly ash: An experimental and statistical approach

(2018) Ali Abdulhussein Shubbar et al.   CONSTRUCTION AND BUILDING MATERIALS

Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes

(2018) Mulusew Aderaw Getahun et al.   CONSTRUCTION AND BUILDING MATERIALS

Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves

(2018) Ali Behnood et al.   JOURNAL OF CLEANER PRODUCTION

Self-compacting concrete strength prediction using surrogate models

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

Prediction of self-compacting concrete strength using artificial neural networks

(2016) P.G. Asteris et al.   European Journal of Environmental and Civil Engineering

Cement industry greenhouse gas emissions – management options and abatement cost

(2016) Raili Kajaste et al.   JOURNAL OF CLEANER PRODUCTION

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

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

Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength

(2013) Jui-Sheng Chou et al.   CONSTRUCTION AND BUILDING MATERIALS

A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler

(2013) You Lv et al.   ENERGY

Cement and carbon emissions

(2013) Laurent Barcelo et al.   MATERIALS AND STRUCTURES

State of the art in building modelling and energy performances prediction: A review

(2013) Aurélie Foucquier et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Global strategies and potentials to curb CO2 emissions in cement industry

(2012) Emad Benhelal et al.   JOURNAL OF CLEANER PRODUCTION

Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks

(2011) Rafat Siddique et al.   ADVANCES IN ENGINEERING SOFTWARE

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

(2011) M. Galar et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

Greenhouse gas emissions in building construction: A case study of One Peking in Hong Kong

(2009) Hui Yan et al.   BUILDING AND ENVIRONMENT

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

(2008) B.K. Raghu Prasad et al.   CONSTRUCTION AND BUILDING MATERIALS

Mechanical properties of concrete containing a high volume of tire–rubber particles

(2008) Ali R. Khaloo et al.   WASTE MANAGEMENT

Numerical investigation of concrete subjected to compressive impact loading. Part 2: Parametric investigation of factors affecting behaviour at high loading rates

(2007) D.M. Cotsovos et al.   COMPUTERS & STRUCTURES