PSI - Issue 17

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000 – 000

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Procedia Structural Integrity 17 (2019) 924–933

ICSI 2019 The 3rd International Conference on Structural Integrity Soft computing-based techniques for concrete beams shear strength Danial J. Armaghani a , George D. Hatzigeorgiou b , Chrysoula Karamani c , Athanasia Skentou c , Ioanna Zoumpoulaki c and Panagiotis G. Asteris c,1 a Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia b Hellenic Open University, School of Science and Technology, Parodos Aristotelous 18, GR-26335, Patras, Greece c Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Despite the abundance of research works, both experimental and theoretical, conducted since the middle of the previous century up to today, the determination of the shear stress value is still remains an open issue of great interest in structural engineering. The need for further research is indicated by the fact that the majority of available proposals, whether proposed by regulatory agencies or various individuals researchers, lead to the estimation of different shear stress values; moreover, the comparison of estimated values with experimental values demonstrates that the available proposals lead to an overestimation or to an underestimation of the “true” shear stress. In this research study, the artificial neural networks approach is used to estima te the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, artificial neural network models have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the consequent results with the corresponding experimental ones as well as with available formulas from previous research studies or code provisions makes obvious the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, the preliminary results presented in this work reveal the crucial parameters that affect the value of the shear strength of reinforced concrete beams with or without transverse reinforcement. ICSI 2019 The 3rd International Conference on Structural Integrity Soft computing-based techniques for concrete beams shear strength Danial J. Armaghani a , George D. Hatzigeorgiou b , Chrysoula Karamani c , Athanasia Skentou c , Ioanna Zoumpoulaki c and Panagiotis G. Asteris c,1 a Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia b Hellenic Open University, School of Science nd Technology, Parodos Aristotelous 18, GR-26335, Patras, Gre ce c Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Abstract Despite the abundance of research works, both experimental and theoretical, conducted since the middle of the previous century up to today, the determination of the shear stress value is still remains an open issue of great interest in structural engineering. The need for further research is indicated by the fact that the majority of available proposals, whether proposed by regulatory agencies or various individuals researchers, lead to the estimation of different shear stress values; moreover, the comparison of estimated values with experimental values demonstrates that the available proposals lead to an overestimation or to an underestimation of the “true” shear stress. In this research study, the artificial neural networks approach is used to estima te the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, artificial neural network models have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the consequent results with the corresponding experimental ones as well as with available formulas from previous research studies or code provisions makes obvious the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, the preliminary results presented in this work reveal the crucial parameters that affect the value of the shear strength of reinforced concrete beams with or without transverse reinforcement. Abstract

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.

Keywords:concrete beam shear strength, artificial neural networks,heuristic algorithms, stirrups, soft computing techniques Keywords:concrete beam shear strength, artificial neural networks,heuristic algorithms, stirrups, soft computing techniques

* Corresponding author. Tel.: +30 210 2896922 E-mail address: panagiotisasteris@gmail.com ; asteris@aspete.gr * Corresponding author. Tel.: +30 210 2896922 E-mail address: panagiotisasteris@gmail.com ; asteris@aspete.gr

2452-3216© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 2452-3216© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.

2452-3216  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.123