Estimating the optimal mix design of silica fume concrete using biogeography-based programming

Abstract The use of silica fume in concrete mixtures has been dramatically increased in concrete industry, especially for achieving high strength concrete. An accurate model of estimating the compressive strength and optimal mix design of silica fume concrete can save in time and cost. In this study, the biogeography-based programming (BBP) was used as a symbolic regression method to predict the compressive strength of silica fume concrete, while the constrained biogeography-based optimization (CBBO) was used to estimate its optimal mix design. For this purpose, a comprehensive database was gathered from various published documents. From the collected data, about 75% of all data was employed to train the model, while the rest was used to verify the developed model. The amounts of cement, water, silica fume, coarse aggregate, fine aggregate, superplasticizer, as well as the maximum size of aggregate and concrete age were selected as the effective input variables of the model. The compressive strength of silica fume concrete was considered as the only output variable. The results show that the BBP model can be successfully used for the prediction of the compressive strength of silica fume concrete with acceptable accuracy. In addition, a graphical user interface was designed which allows the users to estimate the optimal mix design of silica fume concrete.

[1]  Mark Alexander,et al.  Durability performance of concrete containing condensed silica fume , 1999 .

[2]  B. Lothenbach,et al.  Supplementary cementitious materials , 2011 .

[3]  Ali Behnood,et al.  Effects of deicers on the performance of concrete pavements containing air-cooled blast furnace slag and supplementary cementitious materials , 2018, Cement and Concrete Composites.

[4]  Amir Hossein Rafiean,et al.  Compressive strength prediction of environmentally friendly concrete using artificial neural networks , 2018 .

[5]  Łukasz Sadowski,et al.  Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks , 2015 .

[6]  Mahsa Modiri Gharehveran,et al.  Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm , 2017 .

[7]  Safwan A. Khedr,et al.  Characteristics of Silica‐Fume Concrete , 1994 .

[8]  Muhammad Fauzi Mohd. Zain,et al.  Prediction of Strength and Slump of Silica Fume Incorporated High-Performance Concrete , 2015 .

[9]  Okan Karahan,et al.  Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network , 2009, Adv. Eng. Softw..

[10]  Sudarsana Rao hunchate,et al.  Mix Design of High Performance ConcreteUsing Silica Fume and Superplasticizer , 2014 .

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

[12]  C. T. Tam,et al.  EFFECT OF WATER-TO-CEMENTITIOUS MATERIALS RATIO AND SILICA FUME ON THE AUTOGENOUS SHRINKAGE OF CONCRETE , 2003 .

[13]  J. J. Brooks,et al.  Effect of silica fume on mechanical properties of high-strength concrete , 2004 .

[14]  Santanu Bhanja,et al.  Optimum Silica Fume Content and Its Mode of Action on Concrete , 2003 .

[15]  Jamaloddin Noorzaei,et al.  UPV method for strength detection of high performance concrete , 2007 .

[16]  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..

[17]  Santanu Bhanja,et al.  WATER-CEMENT RATIO LAW AND SILICA FUME CONCRETE MIX DESIGN , 2005 .

[18]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[19]  Ramazan Demirboga,et al.  Thermal conductivity and compressive strength of concrete incorporation with mineral admixtures , 2007 .

[20]  Ali Behnood,et al.  Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures , 2008 .

[21]  Dan Simon,et al.  Blended biogeography-based optimization for constrained optimization , 2011, Eng. Appl. Artif. Intell..

[22]  Emadaldin Mohammadi Golafshani,et al.  Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete , 2018 .

[23]  Xinhua Cai,et al.  Abrasion erosion characteristics of concrete made with moderate heat Portland cement, fly ash and silica fume using sandblasting test , 2016 .

[24]  B. V. Venkatarama Reddy,et al.  Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN , 2009 .

[25]  C. Poon,et al.  Compressive strength, chloride diffusivity and pore structure of high performance metakaolin and silica fume concrete , 2006 .

[26]  Mahmoud Nili,et al.  The long-term compressive strength and durability properties of silica fume fiber-reinforced concrete , 2012 .

[27]  B. W. Langan,et al.  Silica Fume in High-Strength Concrete , 1987 .

[28]  Ersin Namli,et al.  High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform , 2013, Eng. Appl. Artif. Intell..

[29]  Okan Karahan,et al.  Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete , 2009, Adv. Eng. Softw..

[30]  Emadaldin Mohammadi Golafshani,et al.  Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete , 2018, Appl. Soft Comput..

[31]  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..

[32]  Piyushkumar Jayantilal Patel.  HEALTH ANALYSIS OF HIGH PERFORMANCE CONCRETE BY USING WASTE MATERIAL , 2014 .

[33]  R. Siddique.  Utilization of silica fume in concrete: Review of hardened properties , 2011 .

[34]  Yun Feng Li,et al.  Influence of Silica Fume on Mechanical Property of High Performance Concrete , 2014 .

[35]  Özgür Çakır,et al.  Influence of silica fume on mechanical and physical properties of recycled aggregate concrete , 2015 .

[36]  Isabel Martínez-Lage,et al.  Analytical and genetic programming model of compressive strength of eco concretes by NDT according to curing temperature , 2017 .

[37]  O. Amiri,et al.  Tri-dimensional modelling of cementitious materials permeability from polymodal pore size distribution obtained by mercury intrusion porosimetry tests , 2005 .

[38]  I-Cheng Yeh,et al.  Knowledge discovery of concrete material using Genetic Operation Trees , 2009, Expert Syst. Appl..

[39]  Ashraf F. Ashour,et al.  Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques , 2016 .

[40]  Abdelkarim Aït-Mokhtar,et al.  Water vapour desorption variability of in situ concrete and effects on drying simulations , 2011 .

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

[42]  Turan Özturan,et al.  Properties of rubberized concretes containing silica fume , 2004 .

[43]  Joseph R. Kasprzyk,et al.  Computational design optimization of concrete mixtures: A review , 2018, Cement and Concrete Research.

[44]  Ali Behnood,et al.  Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm , 2015 .

[45]  Abdelkarim Aït-Mokhtar,et al.  Study of cracking due to drying in coating mortars by digital image correlation , 2012 .

[46]  R. Hooton.  INFLUENCE OF SILICA FUME REPLACEMENT OF CEMENT ON PHYSICAL PROPERTIES AND RESISTANCE TO SULFATE ATTACK, FREEZING AND THAWING, AND ALKALI-SILICA REACTIVITY , 1993 .

[47]  Erdogan Ozbay,et al.  Transport properties based multi-objective mix proportioning optimization of high performance concretes , 2011 .

[48]  H. A. Razak,et al.  Efficiency of calcined kaolin and silica fume as cement replacement material for strength performance , 2005 .

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

[50]  Giovanni Pascale,et al.  Nondestructive assessment of the actual compressive strength of high-strength concrete , 2003 .

[51]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[52]  Ashraf Ashour,et al.  A feasibility study of BBP for predicting shear capacity of FRP reinforced concrete beams without stirrups , 2016, Adv. Eng. Softw..

[53]  R. Siddique,et al.  Use of silicon and ferrosilicon industry by-products (silica fume) in cement paste and mortar , 2011 .

[54]  Mehmet Gesoǧlu,et al.  Strength, permeability and shrinkage cracking of silica fume and metakaolin concretes , 2012 .

[55]  Chi Sun Poon,et al.  Effect of Fly Ash and Silica Fume on Compressive and Fracture Behaviors of Concrete , 1998 .

[56]  Konstantin Sobolev,et al.  The development of a new method for the proportioning of high-performance concrete mixtures , 2004 .

[57]  Magda I. Mousa.  Effect of elevated temperature on the properties of silica fume and recycled rubber-filled high strength concretes (RHSC) , 2017 .

[58]  Ali Behnood,et al.  Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength , 2015 .

[59]  A. Gandomi,et al.  New formulations for mechanical properties of recycled aggregate concrete using gene expression programming , 2017 .

[60]  Emadaldin Mohammadi Golafshani,et al.  Introduction of Biogeography-Based Programming as a new algorithm for solving problems , 2015, Appl. Math. Comput..

[61]  Emadaldin Mohammadi Golafshani,et al.  Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves , 2018, Journal of Cleaner Production.