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Volume 1, Number 1, June 2020 , pages 063-99 DOI: https://doi.org/10.12989/mca.2020.1.1.063 |
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On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength |
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P.G. Asteris, M. Apostolopoulou, D.J. Armaghani, L. Cavaleri, A.T. Chountalas, D. Guney, M. Hajihassani, M. Hasanipanah, M. Khandelwal, C. Karamani, M. Koopialipoor, E. Kotsonis, T.-T. Le, P.B. Lourenço, H.-B. Ly, A.Moropoulou, H. Nguyen, B.T. P
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| Abstract | ||
| In recent years, metakaolin, as a highly reactive pozzolan, has been in the center of research concerning mortar-based materials. Metakaolin is used as an addition in cement-mortars, substituting the cement fraction to a certain extent, in order to enhance sustainability of cement mortars, both in terms of environmental impact of raw materials production, as well as in terms of improving cement-based mortars durability under environmental actions. However, as metakaolin affects the mechanical performance of cement-based mortars, it is important to know the compressive strength that these blended mortars achieve at 28-days, in terms of structural design. Toward this direction, metaheuristic models such as ANN and Genetic Programming (GP) models have been developed and trained through the use of a database, compiled by available, in the literature, experimental works related to cement and blended cement-metakaolin mortars. In the model development phase, the most important parameters affecting the strength of concrete-based mortars, were investigated and selected. In addition, the effect of the selected transfer functions, as well as the initial values of weights and biases on the performance of ANN models, were also investigated. Based on this analysis, it was shown that ANNs with selected transfer functions (such as the RadialBasis transfer function, the Soft-Max transfer function, and the Normalized Radial Basis transfer function) were, able to reliably simulate the 28-days compressive strength of the cement-based mortars. In addition, it was shown that parameters such as the cement grade and the maximum diameter of aggregates, are very important in determining compressive strength of the cement-based mortars; this is an important finding, because these parameters are usually not taken into account in the research studies concerned in the prediction of compressive strength through computational models. | ||
| Key Words | ||
| artificial neural networks (ANNs); cement; compressive strength; Genetic Programming (GP); metakaolin; mortar; metaheuristic algorithms; surrogate models | ||
| Address | ||
| Panagiotis G. Asteris:Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Maria Apostolopoulou:School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15780, Athens, Greece Danial J. Armaghani:Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia Liborio Cavaleri:Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo, Palermo, Italy Athanasios T. Chountalas:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,Heraklion, GR 14121, Athens, Greece Deniz Guney:1omputational Mechanics Laboratory, School of Pedagogical and Technological Education,Heraklion, GR 14121, Athens, Greece Mohsen Hajihassani:Construction Research Alliance, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor, Malaysia Mahdi Hasanipanah:Department of Mining Engineering, University of Kashan, Kashan, Iran Manoj Khandelwal:Federation University Australia, PO Box 663, Ballarat, VIC 3353, Australia Chrysoula Karamani:Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Mohammadreza Koopialipoor:Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran Evgenios Kotsonis:Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Tien-Thinh Le:Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam Paulo B. Lourenço:ISISE, Department of Civil Engineering, University of Minho, Azurém, 4800-058 Guimarães, Portugal Hai-Bang Ly:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,Heraklion, GR 14121, Athens, Greece Antonia Moropoulou:School of Chemical Engineering, National Technical University of Athens, Zografou Campus,15780, Athens, Greece Hoang Nguyen:Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam Binh Thai Pham:University of Transport Technology, Hanoi 100000, Vietnam Pijush Samui:Department of Civil Engineering, NIT Patna, Patna – 800005, Bihar, India Jian Zhou:School of Resources and Safety Engineering, Central South University, Changsha 410083, China | ||
