Prediction of Compressive Strength of Rice Husk Ash Concrete through Different Machine Learning Processes

Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

[1]  Amir Hossein Gandomi,et al.  Assessment of artificial neural network and genetic programming as predictive tools , 2015, Adv. Eng. Softw..

[2]  Karim Moussaceb,et al.  Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates , 2019, Construction and Building Materials.

[3]  A. P. Gursel,et al.  A life-cycle approach to environmental, mechanical, and durability properties of “green” concrete mixes with rice husk ash , 2016 .

[4]  Prinya Chindaprasirt,et al.  Sulfate resistance of blended cements containing fly ash and rice husk ash , 2007 .

[5]  M. F. Ullah,et al.  Effective use of micro-silica extracted from rice husk ash for the production of high-performance and sustainable cement mortar , 2020 .

[6]  Rayed Alyousef,et al.  Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm , 2021, Materials.

[7]  Rahali Bachir,et al.  Using Artificial Neural Networks Approach to Estimate Compressive Strength for Rubberized Concrete , 2018 .

[8]  Muhammad Ekhlasur Rahman,et al.  Self compacting concrete from uncontrolled burning of rice husk and blended fine aggregate , 2014 .

[9]  K. Sakr.  Effects of Silica Fume and Rice Husk Ash on the Properties of Heavy Weight Concrete , 2006 .

[10]  Ali Akbar Ramezanianpour,et al.  The Effect of Rice Husk Ash on Mechanical Properties and Durability of Sustainable Concretes , 2009 .

[11]  H. M. Tanarslan,et al.  An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks , 2012 .

[12]  Amir Hossein Alavi,et al.  Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing , 2011 .

[13]  Muhammad Fauzi Mohd. Zain,et al.  Production of rice husk ash for use in concrete as a supplementary cementitious material , 2011 .

[14]  Sami Ekici,et al.  An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM , 2009, Expert Syst. Appl..

[15]  P. Chindaprasirt,et al.  Performance of recycled aggregate concrete with rice husk ash as cement binder , 2020 .

[16]  C. Chen,et al.  Environmental impact of cement production: detail of the different processes and cement plant variability evaluation , 2010 .

[17]  O. Kisi,et al.  Predicting the compressive strength of steel fiber added lightweight concrete using neural network , 2008 .

[18]  Velu Saraswathy,et al.  Corrosion performance of rice husk ash blended concrete , 2007 .

[19]  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..

[20]  Muhammad Shafique,et al.  Sugarcane bagasse ash-based engineered geopolymer mortar incorporating propylene fibers , 2021 .

[21]  Mehrdad Arashpour,et al.  Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer , 2020 .

[22]  C. Hwang,et al.  Effect of rice husk ash on the strength and durability characteristics of concrete , 2011 .

[23]  Enda Crossin,et al.  The greenhouse gas implications of using ground granulated blast furnace slag as a cement substitute , 2015 .

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

[25]  Tim Grant,et al.  Comparative life cycle assessment of uses of rice husk for energy purposes , 2011 .

[26]  Saloni,et al.  Enhanced properties of high-silica rice husk ash-based geopolymer paste by incorporating basalt fibers , 2020 .

[27]  K. Rajagopal,et al.  Rice husk ash blended cement: Assessment of optimal level of replacement for strength and permeability properties of concrete , 2008 .

[28]  Zaher Mundher Yaseen,et al.  Predicting compressive strength of lightweight foamed concrete using extreme learning machine model , 2018, Adv. Eng. Softw..

[29]  Blessen Skariah Thomas,et al.  Green concrete partially comprised of rice husk ash as a supplementary cementitious material – A comprehensive review , 2018 .

[30]  Ankur Mehta,et al.  An overview of geopolymers derived from industrial by-products , 2016 .

[31]  Fangming Deng,et al.  Compressive strength prediction of recycled concrete based on deep learning , 2018, Construction and Building Materials.

[32]  Teuku Meurah Indra Mahlia,et al.  Emissions from electricity generation in Malaysia , 2002 .

[33]  Gemma Rodríguez de Sensale,et al.  Strength development of concrete with rice-husk ash , 2006 .

[34]  Luciana Paulo Gomes,et al.  Life cycle analysis (LCA) for the incorporation of rice husk ash in mortar coating , 2010 .

[35]  J. Bullard,et al.  Behavior assessment, regression analysis and support vector machine (SVM) modeling of waste tire rubberized concrete , 2020 .

[36]  A. Damgaard,et al.  Life cycle assessment of the reuse of fly ash from biomass combustion as secondary cementitious material in cement products , 2020 .

[37]  Akeem Ayinde Raheem,et al.  Development of corn cob ash blended cement , 2009 .

[38]  R. Perera,et al.  Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks , 2010 .

[39]  Yassine Djebbar,et al.  NEURAL NETWORK MODEL FOR PREFORMED-FOAM CELLULAR CONCRETE , 2001 .

[40]  F. Ameri,et al.  Optimum rice husk ash content and bacterial concentration in self-compacting concrete , 2019, Construction and Building Materials.

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

[42]  Dieu Tien Bui,et al.  Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility , 2019, CATENA.

[43]  Muhammad Izhar Shah,et al.  Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete , 2020, Crystals.

[44]  Wisal Ahmed,et al.  Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners , 2021 .

[45]  Piet Stroeven,et al.  Particle size effect on the strength of rice husk ash blended gap-graded Portland cement concrete , 2005 .