Non-linear and mixed regression models in predicting sustainable concrete strength

Abstract Most previous research adopting the regression analysis to capture the relationship between concrete properties and mixture-design-related variables was based on the linear approach with limited accuracy. This study applies non-linear and mixed regression analyses to model properties of environmentally friendly concrete based on a comprehensive set of variables containing alternative or waste materials. It was found that best-fit non-linear and mixed models achieved similar accuracies and superior R2 values compared to the linear approach, with both the numerical and relative input methods. Individual materials’ effects on concrete strength were statistically quantified at different curing ages using the best-fit models.

[1]  Ali Akbar Ramezanianpour,et al.  Engineering Properties of Alkali-Activated Natural Pozzolan Concrete , 2011 .

[2]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[3]  A. Marí,et al.  Influence of Amount of Recycled Coarse Aggregates and Production Process on Properties of Recycled Aggregate Concrete , 2007 .

[4]  Hanifi Binici,et al.  Effect of crushed ceramic and basaltic pumice as fine aggregates on concrete mortars properties , 2007 .

[5]  Umit Atici,et al.  Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network , 2011, Expert Syst. Appl..

[6]  N R St-Pierre,et al.  Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. , 2001, Journal of dairy science.

[7]  Young-Moon Leem,et al.  Effect of oyster shell substituted for fine aggregate on concrete characteristics: Part I. Fundamental properties , 2005 .

[8]  A. Öztas,et al.  Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks , 2007 .

[9]  Qian Chen,et al.  Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete , 2016, J. Comput. Civ. Eng..

[10]  Ruoyu Jin,et al.  Survey of the current status of sustainable concrete production in the U.S. , 2015 .

[11]  Kenneth A. Snyder,et al.  Concrete Mixture Optimization Using Statistical Mixture Design Methods. , 1997 .

[12]  Vernon R. Schaefer,et al.  Mixture Proportion Development and Performance Evaluation of Pervious Concrete for Overlay Applications , 2011 .

[13]  İlker Bekir Topçu,et al.  Prediction of properties of waste AAC aggregate concrete using artificial neural network , 2007 .

[14]  J. Koenderink Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[15]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[16]  Ruoyu Jin,et al.  A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties , 2013 .

[17]  C. O. Orangun,et al.  Optimal water/cement ratios and strength characteristics of some local clay soils stabilized with cement , 1985 .

[18]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[19]  Gokmen Tayfur,et al.  FUZZY LOGIC MODEL FOR THE PREDICTION OF CEMENT COMPRESSIVE STRENGTH , 2004 .

[20]  Jerry Stephens,et al.  Performance of 100% Fly Ash Concrete with 100% Recycled Glass Aggregate , 2011 .

[21]  Abhijit Mukherjee,et al.  Artificial neural networks in prediction of mechanical behavior of concrete at high temperature , 1997 .

[22]  F. Demir A new way of prediction elastic modulus of normal and high strength concrete—fuzzy logic , 2005 .

[23]  J. Streibig,et al.  Nonlinear Mixed-Model Regression to Analyze Herbicide Dose–Response Relationships1 , 2004, Weed Technology.

[24]  İlker Bekir Topçu,et al.  Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic , 2009 .

[25]  S. Chithra,et al.  A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks , 2016 .

[26]  Ahmet Raif Boğa,et al.  Effect of boron waste on the properties of mortar and concrete , 2010, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[27]  Mauro Serra,et al.  Concrete strength prediction by means of neural network , 1997 .

[28]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[29]  Jui-Sheng Chou,et al.  Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques , 2011, J. Comput. Civ. Eng..

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

[31]  Fa-Liang Gao,et al.  A new way of predicting cement strength—Fuzzy logic , 1997 .

[32]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[33]  Mukesh Limbachiya,et al.  Performance of portland/silica fume cement concrete produced with recycled concrete aggregate , 2012 .

[34]  Wps Dias,et al.  NEURAL NETWORKS FOR PREDICTING PROPERTIES OF CONCRETES WITH ADMIXTURES , 2001 .

[35]  Muhammad Fauzi Mohd. Zain,et al.  Concrete using waste oil palm shells as aggregate , 1999 .