Assessment of flexural strength of concrete with marble powder applying soft computing techniques

[1]  M. S. Thakur,et al.  Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder , 2022, Materials.

[2]  Pijush Samui,et al.  Efficient soft computing techniques for the prediction of compressive strength of geopolymer concrete , 2021 .

[3]  J. Revathy,et al.  Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites , 2021 .

[4]  Parveen Sihag,et al.  Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques , 2021, Arabian Journal for Science and Engineering.

[5]  Z. Duan,et al.  Combined use of recycled powder and recycled coarse aggregate derived from construction and demolition waste in self-compacting concrete , 2020 .

[6]  B. Pang,et al.  A model to characterize the effect of particle size of fly ash on the mechanical properties of concrete by the grey multiple linear regression , 2020 .

[7]  Hoang Nguyen,et al.  Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms , 2020, ISPRS Int. J. Geo Inf..

[8]  Arvind Kumar,et al.  Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR , 2020 .

[9]  Atefeh Gholamzadeh-Chitgar,et al.  Elman ANNs along with two different sets of inputs for predicting the properties of SCCs , 2019 .

[10]  Hasan Ahmadi,et al.  Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR) , 2019, ISH Journal of Hydraulic Engineering.

[11]  Jiayuan Wang,et al.  Considering life-cycle environmental impacts and society's willingness for optimizing construction and demolition waste management fee: An empirical study of China , 2019, Journal of Cleaner Production.

[12]  Rajendra Kumar Sharma,et al.  Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete , 2018 .

[13]  H. S. Chore,et al.  Prediction of unconfined compressive and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes using multiple linear regression and artificial neural network , 2017 .

[14]  Anshuman Srivastava,et al.  An investigation on effect of partial replacement of cement by waste marble slurry , 2017 .

[15]  Nhat-Duc Hoang,et al.  Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model , 2016 .

[16]  José Luis Rojo-Álvarez,et al.  Support vector machines in engineering: an overview , 2014, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[17]  Dolores Eliche-Quesada,et al.  Recycling of sawdust, spent earth from oil filtration, compost and marble residues for brick manufacturing , 2012 .

[18]  Said Kenai,et al.  Effect of natural pozzolana and marble powder on the properties of self-compacting concrete , 2012 .

[19]  K. Sathiyakumari,et al.  Prediction of the Compressive Strength of High Performance Concrete Mix using Tree Based Modeling , 2010 .

[20]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[21]  A. Goh,et al.  Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data , 2007 .

[22]  S. Agarwal,et al.  Utilization of industrial wastes and unprocessed micro-fillers for making cost effective mortars , 2006 .

[23]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[24]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[25]  Neeraj Sharma,et al.  A review: sustainable compressive strength properties of concrete mix with replacement by marble powder , 2020 .

[26]  Monica C. Dhoka.  “Green Concrete: Using Industrial Waste of Marble Powder, Quarry Dust , 2013 .

[27]  N. Soliman.  Effect of using Marble Powder in Concrete Mixes on the Behavior and Strength of R.C. Slabs , 2013 .

[28]  K. Mahmood,et al.  Effect of micro sized marble sludge on physical properties of natural rubber composites , 2013 .

[29]  J. H. Bungey,et al.  Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models , 2012 .

[30]  Test Methods for Time of Setting of Hydraulic Cement by Vicat Needle , 2008 .