Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature.

[1]  Panagiotis G. Asteris,et al.  Properties of sandcrete mixtures modified with metakaolin , 2016 .

[2]  Nain Y. Sheen,et al.  Predicting strength development of RMSM using ultrasonic pulse velocity and artificial neural network , 2013 .

[3]  M. Forde,et al.  Review of NDT methods in the assessment of concrete and masonry structures , 2001 .

[4]  Αλέξανδρος Π. Αλεξανδρίδης Evolving RBF neural networks for adaptive soft-sensor design , 2015 .

[5]  K. Tharmaratnam,et al.  Attenuation of ultrasonic pulse in cement mortar , 1990 .

[6]  P. G. Asteris,et al.  Modeling of masonry failure surface under biaxial compressive stress using Neural Networks , 2014 .

[7]  Özgür Kisi,et al.  Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques , 2018, Neural Computing and Applications.

[8]  Kyriacos Neocleous,et al.  Predicting the Shear Strength of RC Beams without Stirrups Using Bayesian Neural Network , 2010 .

[9]  Leonard Ziemiański,et al.  Neural networks in mechanics of structures and materials – new results and prospects of applications , 2001 .

[10]  Panagiotis G. Asteris,et al.  Self-compacting concrete strength prediction using surrogate models , 2017, Neural Computing and Applications.

[11]  Panagiotis G. Asteris,et al.  Investigation of the mechanical behaviour of metakaolin-based sandcrete mixtures , 2019 .

[12]  Junkyeong Kim,et al.  Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network , 2017 .

[13]  Mehdi Nikoo,et al.  Determination of compressive strength of concrete using Self Organization Feature Map (SOFM) , 2013, Engineering with Computers.

[14]  Philippe Gotteland,et al.  Physical and mechanical properties of soilcrete mixtures: Soil clay content and formulation parameters , 2017 .

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

[16]  Hamid Eskandari-Naddaf,et al.  ANN prediction of cement mortar compressive strength, influence of cement strength class , 2017 .

[17]  Said Kenai,et al.  Performance of compacted cement-stabilised soil , 2004 .

[18]  J. H. Bungey,et al.  Reliability of partially-destructive tests to assess the strength of concrete on site , 2001 .

[19]  Panagiotis G. Asteris,et al.  Anisotropic masonry failure criterion using artificial neural networks , 2017, Neural Computing and Applications.

[20]  P. G. Asteris,et al.  Mechanical properties of soilcrete mixtures modified with metakaolin , 2013 .

[21]  Francisco Arriaga,et al.  Comparison of modelling using regression techniques and an artificial neural network for obtaining the static modulus of elasticity of Pinus radiata D. Don. timber by ultrasound , 2016 .

[22]  Saman Soleimani Kutanaei,et al.  Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network , 2016 .

[23]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[24]  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..

[25]  A. V. Olgac,et al.  Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks , 2011 .

[26]  Sohichi Hirose,et al.  Artificial neural network model using ultrasonic test results to predict compressive stress in concrete , 2017 .

[27]  G. Trtnik,et al.  Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. , 2009, Ultrasonics.

[28]  P. Asteris,et al.  Neural Network Approximation of the Masonry Failure under Biaxial Compressive Stress , 2013 .

[29]  Martin Doerr,et al.  INSTITUTE OF COMPUTER SCIENCE FOUNDATION FOR RESEARCH AND TECHNOLOGY - HELLAS , 2005 .

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

[31]  Panagiotis G. Asteris,et al.  ANISOTROPIC FAILURE CRITERION FOR BRITLE MATERIALS USING ARTIFICIAL NEURAL NETWORKS , 2015 .

[32]  O. Kisi,et al.  Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches , 2015 .

[33]  Panagiotis G. Asteris,et al.  Prediction of self-compacting concrete strength using artificial neural networks , 2016 .

[34]  Ramesh Sharda,et al.  Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. , 2006, Accident; analysis and prevention.

[35]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[36]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[37]  Mustafa Ulaş,et al.  Using an Artificial Neural Network to Predict Mix Compositions of Steel Fiber-Reinforced Concrete , 2014, Arabian Journal for Science and Engineering.

[38]  Panagiotis G. Asteris,et al.  Data on the physical and mechanical properties of soilcrete materials modified with metakaolin , 2017, Data in brief.

[39]  Mehdi Nikoo,et al.  Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete , 2017, Frontiers of Structural and Civil Engineering.

[40]  Türkay Dereli,et al.  Prediction of cement strength using soft computing techniques , 2004 .

[41]  Dimitris G. Giovanis,et al.  Spectral representation-based neural network assisted stochastic structural mechanics , 2015 .

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

[43]  Bakhta Boukhatem,et al.  Prediction of properties of self-compacting concrete containing fly ash using artificial neural network , 2017, Neural Computing and Applications.

[44]  İlker Bekir Topçu,et al.  Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic , 2008 .

[45]  Bumjoo Kim,et al.  Strength characteristics of cemented sand–bentonite mixtures with fiber and metakaolin additions , 2017 .

[46]  Md. Safiuddin,et al.  Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash , 2016, Materials.

[47]  Ahmet Tortum,et al.  Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models , 2017 .