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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ScienceDirect

Procedia Structural Integrity 17 (2019) 914–923

ICSI 2019 The 3rd International Conference on Structural Integrity Compressive strength of natural hydraulic lime mortars using soft computing techniques Maria Apostolopoulou a , Danial J. Armaghani b , Asterios Bakolas a , Maria G. Douvika c , Antonia Moropoulou a and Panagiotis G. Asteris c,1 a Laboratory of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece b Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia c Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece In recent years, natural hydraulic lime (NHL) mortars have gained increased attention from researchers, not only as restoration materials for monuments and historical buildings, but also as an eco-friendly material which can be used as binder to formulate mortars for contemporary structures. In the present study, an extended database related to NHL mortars is compiled, related to all three NHL grades (NHL5, NHL3.5, NHL2) and soft computing techniques namely artificial neural networks (ANN) are utilized to reveal the influence of the mortar’s mix design on mechanical strength, as well as to predict the compressive strength of NHL mortar mixes. Influence of the binder to aggregate, water to binder and maximum aggregate size on the compressive strength of a mortar at different mortar ages is revealed, for the three grades of natural hydraulic lime, further highlighting aspects of this “new” material, which has been used as a binder since antiquity. ICSI 2019 The 3rd International Conference on Structural Integrity Compressive strength of natural hydraulic lime mortars using soft computing techniques Maria Apostolopoulou a , Danial J. Armaghani b , Asterios Bakolas a , Maria G. Douvika c , Antonia Moropoulou a and Panagiotis G. Asteris c,1 a Laboratory of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greec b Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia c Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece Abstract In recent years, natural hydraulic lime (NHL) mortars have gained increased attention from researchers, not only as restoration aterials for monuments and historical buildings, but also as an eco-friendly material which can be used as binder to formulate mortars for contemporary structures. In the present study, an extended database related to NHL mortars is compiled, related to all three NHL grades (NHL5, NHL3.5, NHL2) and soft computing techniques namely artificial neural networks (ANN) are utilized to reveal the influence of the mortar’s mix design on mechanical strength, as well as to predict the compressive strength of NHL ortar mixes. Influence of the binder to aggregate, water to binder and maximum aggregate size on the compressive strength of a mortar at different mortar ages is revealed, for the three grades of natural hydraulic lime, further highlighting aspects of this “new” material, which has been used as a binder since antiquity. 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: natural hydraulic lime, compressive strength, mortar mix, artificial neural networks,heuristic algorithms, monument protection, soft computing techniques Keywords: natural hydraulic lime, compressive strength, mortar mix, artificial neural networks,heuristic algorithms, monument protection, 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.2019.08.122