Compressive strength of natural hydraulic lime mortars using soft computing techniques

被引：97
|
作者：
Apostolopoulou, Maria [1 ]
Armaghani, Danial J. [2 ]
Bakolas, Asterios [1 ]
Douvika, Maria G. [3 ]
Moropoulou, Antonia [1 ]
Asteris, Panagiotis G. [3 ]
机构：
[1] Natl Tech Univ Athens, Sch Chem Engn, Lab Mat Sci & Engn, Athens 15780, Greece
[2] Univ Teknol Malaysia, Fac Engn, Sch Civil Engn, Ctr Trop Geoengn GEOTROPIK, Johor Baharu 81310, Malaysia
[3] Sch Pedag & Technol Educ, Computat Mech Lab, GR-14121 Athens, Greece
来源：
3RD INTERNATIONAL CONFERENCE ON STRUCTURAL INTEGRITY (ICSI 2019) | 2019年 / 17卷
关键词：
natural hydraulic lime; compressive strength; mortar mix; artificial neural networks; heuristic algorithms; monument protection; soft computing techniques; ARTIFICIAL NEURAL-NETWORK; MECHANICAL-PROPERTIES; PHYSICAL-PROPERTIES; CEMENT; PREDICTION; PERFORMANCE; DURABILITY; SURFACE; MODELS; AREA;
D O I：
10.1016/j.prostr.2019.08.122
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
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. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码：914 / 923
页数：10
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