Compressive strength of natural hydraulic lime mortars using soft computing techniques

被引：102

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

页码：914 / 923

页数：10

共 61 条

[11] Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition [J].

Armaghani, Danial Jahed ;

Mohamad, Edy Tonnizam ;

Narayanasamy, Mogana Sundaram ;

Narita, Nobuya ;

Yagiz, Saffet .

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2017, 63 :29-43

[12] Prediction of self-compacting concrete strength using artificial neural networks [J].

Asteris, P. G. ;

Kolovos, K. G. ;

Douvika, M. G. ;

Roinos, K. .

EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING, 2016, 20 :s102-s122

[13] Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures [J].

Asteris, Panagiotis G. ;

Nikoo, Mehdi .

NEURAL COMPUTING & APPLICATIONS, 2019, 31 (09) :4837-4847

[14] Krill herd algorithm-based neural network in structural seismic reliability evaluation [J].

Asteris, Panagiotis G. ;

Nozhati, Saeed ;

Nikoo, Mehdi ;

Cavaleri, Liborio ;

Nikoo, Mohammad .

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, 2019, 26 (13) :1146-1153

[15] Self-compacting concrete strength prediction using surrogate models [J].

Asteris, Panagiotis G. ;

Kolovos, Konstantinos G. .

NEURAL COMPUTING & APPLICATIONS, 2019, 31 (Suppl 1) :409-424

[16] Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials [J].

Asteris, Panagiotis G. ;

Roussis, Panayiotis C. ;

Douvika, Maria G. .

SENSORS, 2017, 17 (06)

[17] Anisotropic masonry failure criterion using artificial neural networks [J].

Asteris, Panagiotis G. ;

Plevris, Vagelis .

NEURAL COMPUTING & APPLICATIONS, 2017, 28 (08) :2207-2229

[18] Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks [J].

Asteris, Panagiotis G. ;

Tsaris, Athanasios K. ;

Cavaleri, Liborio ;

Repapis, Constantinos C. ;

Papalou, Angeliki ;

Di Trapani, Fabio ;

Karypidis, Dimitrios F. .

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016

[19] Bond-strength performance of hydraulic lime and natural cement mortared sandstone masonry [J].

Barr, S. ;

McCarter, W. J. ;

Suryanto, B. .

CONSTRUCTION AND BUILDING MATERIALS, 2015, 84 :128-135

[20] Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network [J].

Bilim, Cahit ;

Atis, Cengiz D. ;

Tanyildizi, Harun ;

Karahan, Okan .

ADVANCES IN ENGINEERING SOFTWARE, 2009, 40 (05) :334-340

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