Prediction of Compressive Strength of Concrete with Manufactured Sand by Ensemble Classification and Regression Tree Method

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

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

Experimental investigation and modelling the deformation properties of demolition wastes subjected to freeze–thaw cycles using ANN and SVR

(2020) Behnam Ghorbani et al.   CONSTRUCTION AND BUILDING MATERIALS

Role of Limestone Powder in Early-Age Cement Paste Considering Fineness Effects

(2020) Qiang Ren et al.   JOURNAL OF MATERIALS IN CIVIL ENGINEERING

Multi-level diffusion model for manufactured sand mortar considering particle shape and limestone powder effects

(2019) Qiang Ren et al.   CONSTRUCTION AND BUILDING MATERIALS

Modeling the compressive strength of high-strength concrete: An extreme learning approach

(2019) Abobakr Khalil Al-Shamiri et al.   CONSTRUCTION AND BUILDING MATERIALS

Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models

(2019) Jui-Sheng Chou et al.   SOFT COMPUTING

Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN)

(2019) Jong Yil Park et al.   Applied Sciences-Basel

Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques

(2019) Vinay Vakharia et al.   CONSTRUCTION AND BUILDING MATERIALS

A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm

(2019) Qinghua Han et al.   CONSTRUCTION AND BUILDING MATERIALS

Utilization of low-quality desulfurized ash from semi-dry flue gas desulfurization by mixing with hemihydrate gypsum

(2019) Haoxin Li et al.   FUEL

Impact of different lithological manufactured sands on high-speed railway box girder concrete

(2019) Huajian Li 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

A novel optimised self-learning method for compressive strength prediction of high performance concrete

(2018) Yang Yu et al.   CONSTRUCTION AND BUILDING MATERIALS

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

(2018) Palika Chopra et al.   Advances in Civil Engineering

Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods

(2018) Benjamin A. Young et al.   CEMENT AND CONCRETE RESEARCH

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

(2017) Faezehossadat Khademi et al.   Frontiers of Structural and Civil Engineering

Random forest for ordinal responses: Prediction and variable selection

(2016) Silke Janitza et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Comparative study of effect of raw and densified silica fume in the paste, mortar and concrete

(2016) Zengqi Zhang et al.   CONSTRUCTION AND BUILDING MATERIALS

Experimental study on long-term compressive strength of concrete with manufactured sand

(2016) Xinxin Ding et al.   CONSTRUCTION AND BUILDING MATERIALS

Kernel-based models for prediction of cement compressive strength

(2016) Mohit Verma et al.   NEURAL COMPUTING & APPLICATIONS

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

(2016) Nhat-Duc Hoang et al.   Advances in Civil Engineering

Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

(2015) Yaşar Ayaz et al.   CONSTRUCTION AND BUILDING MATERIALS

Machine learning in concrete strength simulations: Multi-nation data analytics

(2014) Jui-Sheng Chou et al.   CONSTRUCTION AND BUILDING MATERIALS

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

(2014) Naresh Kumar Nagwani et al.   TheScientificWorldJOURNAL

Effect of partial and total replacement of siliceous river sand with limestone crushed sand on the durability of mortars exposed to chemical solutions

(2013) M. Bederina et al.   CONSTRUCTION AND BUILDING MATERIALS

High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT)

(2013) Min-Yuan Cheng et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Properties of self-compacting mortar made with various types of sand

(2012) Benchaa Benabed et al.   CEMENT & CONCRETE COMPOSITES

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

(2012) Halil Ibrahim Erdal et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Prediction of Elastic Modulus of Concrete Using Support Vector Committee Method

(2012) Javad Sadoghi Yazdi et al.   JOURNAL OF MATERIALS IN CIVIL ENGINEERING

Inclusion of aggregate angularity, texture, and form in estimating dynamic modulus of asphalt mixes

(2012) Dharamveer Singh et al.   Road Materials and Pavement Design

Study on the Fluidity and Strength Properties of High Performance Concrete Utilizing Crushed Sand

(2012) Sangjun Park   International Journal of Concrete Structures and Materials

A new predictive model for compressive strength of HPC using gene expression programming

(2011) Seyyed Mohammad Mousavi et al.   ADVANCES IN ENGINEERING SOFTWARE

Influence of manufactured sand characteristics on strength and abrasion resistance of pavement cement concrete

(2011) Beixing Li et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

(2011) U. Atici   EXPERT SYSTEMS WITH APPLICATIONS

Utilization of silica fume in concrete: Review of hardened properties

(2011) Rafat Siddique   RESOURCES CONSERVATION AND RECYCLING

Prediction of elastic modulus of normal and high strength concrete by support vector machine

(2010) Kezhen Yan et al.   CONSTRUCTION AND BUILDING MATERIALS

Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques

(2010) Jui-Sheng Chou et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models

(2009) Jafar Sobhani et al.   CONSTRUCTION AND BUILDING MATERIALS

Rheological and mechanical properties of mortars prepared with natural and manufactured sands

(2008) D.D. Cortes et al.   CEMENT AND CONCRETE RESEARCH

Strength and durability of concrete incorporating crushed limestone sand

(2008) B. Menadi et al.   CONSTRUCTION AND BUILDING MATERIALS

Fuzzy polynomial neural networks for approximation of the compressive strength of concrete

(2007) M.H. Fazel Zarandi et al.   APPLIED SOFT COMPUTING

Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic

(2007) İlker Bekir Topçu et al.   COMPUTATIONAL MATERIALS SCIENCE