Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK

(2020) Ahmed Ashteyat et al.   Journal of Civil Engineering and Management

Evaluating the Effect of Calcination and Grinding of Corn Stalk Ash on Pozzolanic Potential for Sustainable Cement-Based Materials

(2020) Shazim Ali Memon et al.   Advances in Materials Science and Engineering

Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill

(2020) Yuantian Sun et al.   Advances in Civil Engineering

A metaheuristic-optimized multi-output model for predicting multiple properties of pervious concrete

(2020) Junfei Zhang et al.   CONSTRUCTION AND BUILDING MATERIALS

Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models

(2020) Junfei Zhang et al.   JOURNAL OF CLEANER PRODUCTION

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

(2020) Muhammad Faisal Javed et al.   Crystals

Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model

(2020) Jiandong Huang et al.   Advances in Civil Engineering

Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression

(2019) Junbo Sun et al.   CONSTRUCTION AND BUILDING MATERIALS

Comparison of CO2 emissions from OPC and recycled cement production

(2019) Zhijun He et al.   CONSTRUCTION AND BUILDING MATERIALS

Consolidation assessment using Multi Expression Programming

(2019) Sohrab Sharifi et al.   APPLIED SOFT COMPUTING

Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer

(2019) Emadaldin Mohammadi Golafshani et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming

(2019) Muhammad Farjad Iqbal et al.   JOURNAL OF HAZARDOUS MATERIALS

Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

(2019) Yuantian Sun et al.   Advances in Civil Engineering

Evaluation of mechanical properties of Sugar Cane Bagasse Ash concrete

(2018) P. Jagadesh et al.   CONSTRUCTION AND BUILDING MATERIALS

Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill

(2018) Chongchong Qi et al.   CONSTRUCTION AND BUILDING MATERIALS

Value-added Utilization of Marine Clay as Cement Replacement for Sustainable Concrete Production

(2018) Hongjian Du et al.   JOURNAL OF CLEANER PRODUCTION

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

Multi-phase modelling of electrochemical rehabilitation for ASR and chloride affected concrete composites

(2018) Li-xuan Mao et al.   COMPOSITE STRUCTURES

Deep neural network with high-order neuron for the prediction of foamed concrete strength

(2018) Tuan Nguyen et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Characteristics and Applications of Sugar Cane Bagasse Ash Waste in Cementitious Materials

(2018) Qing Xu et al.   Materials

Properties of ultra-lightweight cement composites with nano-silica

(2018) Hongjian Du   CONSTRUCTION AND BUILDING MATERIALS

Mechanical and durability properties of mortars prepared with untreated sugarcane bagasse ash and untreated fly ash

(2016) J.C. Arenas-Piedrahita et al.   CONSTRUCTION AND BUILDING MATERIALS

Assesment of Pozzolanic Performance of Sugarcane Bagasse Ash

(2016) A. Bahurudeen et al.   JOURNAL OF MATERIALS IN CIVIL ENGINEERING

Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation

(2016) Milan Despotovic et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Assessment of artificial neural network and genetic programming as predictive tools

(2015) Amir H. Gandomi et al.   ADVANCES IN ENGINEERING SOFTWARE

Influence of different processing methods on the pozzolanic performance of sugarcane bagasse ash

(2015) A. Bahurudeen et al.   CEMENT & CONCRETE COMPOSITES

Performance evaluation of sugarcane bagasse ash blended cement in concrete

(2015) A. Bahurudeen et al.   CEMENT & CONCRETE COMPOSITES

Particle swarm optimization (PSO). A tutorial

(2015) Federico Marini et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Strength, chloride resistance, and expansion of concretes containing ground bagasse ash

(2015) Aukkadet Rerkpiboon et al.   CONSTRUCTION AND BUILDING MATERIALS

NEW DESIGN EQUATIONS FOR ELASTIC MODULUS OF CONCRETE USING MULTI EXPRESSION PROGRAMMING

(2015) Amir H. Gandomi et al.   Journal of Civil Engineering and Management

Micro-analytical studies on sugar cane bagasse ash

(2015) P JAGADESH et al.   SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES

Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS

(2013) A. Sadrmomtazi et al.   CONSTRUCTION AND BUILDING MATERIALS

Multi-output least-squares support vector regression machines

(2013) Shuo Xu et al.   PATTERN RECOGNITION LETTERS

Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares

(2013) S.M. Mousavi et al.   STRUCTURAL ENGINEERING AND MECHANICS

Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks

(2012) Adriana Trocoli Abdon Dantas et al.   CONSTRUCTION AND BUILDING MATERIALS

Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks

(2011) Mohammad Iqbal Khan   AUTOMATION IN CONSTRUCTION

Experimental characterization of binary and ternary blended-cement concretes containing ultrafine residual rice husk and sugar cane bagasse ashes

(2011) G.C. Cordeiro et al.   CONSTRUCTION AND BUILDING MATERIALS

Utilization of bagasse ash in high-strength concrete

(2011) Sumrerng Rukzon et al.   MATERIALS & DESIGN

Utilization of waste foundry sand (WFS) in concrete manufacturing

(2011) Rafat Siddique et al.   RESOURCES CONSERVATION AND RECYCLING

Use of Bagasse Ash in Concrete and Its Impact on the Strength and Chloride Resistivity

(2010) Noor-ul Amin   JOURNAL OF MATERIALS IN CIVIL ENGINEERING

Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures

(2010) Amir Hossein Gandomi et al.   JOURNAL OF MATERIALS IN CIVIL ENGINEERING

Effect of calcination temperature on the pozzolanic activity of sugar cane bagasse ash

(2009) G.C. Cordeiro et al.   CONSTRUCTION AND BUILDING MATERIALS

Utilization of bagasse ash as a pozzolanic material in concrete

(2009) Nuntachai Chusilp et al.   CONSTRUCTION AND BUILDING MATERIALS

Pozzolanic activity and filler effect of sugar cane bagasse ash in Portland cement and lime mortars

(2008) G.C. Cordeiro et al.   CEMENT & CONCRETE COMPOSITES

Ultrafine grinding of sugar cane bagasse ash for application as pozzolanic admixture in concrete

(2008) Guilherme Chagas Cordeiro et al.   CEMENT AND CONCRETE RESEARCH

Analysis of durability of high performance concrete using artificial neural networks

(2008) R. Parichatprecha et al.   CONSTRUCTION AND BUILDING MATERIALS

A review of the interference of carbon containing fly ash with air entrainment in concrete

(2007) K PEDERSEN et al.   PROGRESS IN ENERGY AND COMBUSTION SCIENCE

On Some Aspects of Variable Selection for Partial Least Squares Regression Models

(2007) Partha Pratim Roy et al.   Quantitative structure-activity relationships & combinatorial science