PREDICTION OF MECHANICAL CHARACTERISTICS OF SOILCRETE MATERIALS USING ARTIFICIAL NEURAL NETWORKS

被引：0

作者：

Asteris, P. G. ^{[1
]}

Alexandridis, A. ^{[2
]}

Kolovos, K. G. ^{[3
]}

Anesti, E. G. ^{[1
]}

Douvika, M. G. ^{[1
]}

Karamani, C. A. ^{[1
]}

Kassolis, M. G. ^{[1
]}

Skentou, A. D. ^{[1
]}

机构：

[1] Sch Pedag & Technol Educ, Computat Mech Lab, Athens, Greece

[2] Technol Educ Inst Athens, Dept Elect Engn, Aigaleo, Greece

[3] Hellen Army Acad, Dept Phys Sci & Applicat, Vari, Greece

来源：

PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON MECHANICS AND MATERIALS IN DESIGN (M2D2017) | 2017年

关键词：

artificial neural networks; back propagation neural networks; compressive strength; normalization techniques; soilcrete materials; ultrasonic pulse velocity; METAKAOLIN; MIXTURES; STRENGTH; CEMENT; PARAMETERS; SOILS; FIBER;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, the application of soft computing techniques such as surrogate models for the prediction of the soilcrete materials' properties has been investigated. Specifically, the application of Artificial Neural Networks (ANNs) models for the prediction of mechanical properties such as the 28-day compressive strength has been studied. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of ANNs to predict with pinpoint accuracy the compressive strength. Furthermore, the proposed normalization technique has been proven effective and robust compared to the available ones.

引用

页码：973 / 984

页数：12

共 50 条

[1] Prediction of Polluted Insulators Characteristics using Artificial Neural Networks
Teguar, M.
Mekhaldi, A.
Boubakeur, A.
[J]. 2012 ANNUAL REPORT CONFERENCE ON ELECTRICAL INSULATION AND DIELECTRIC PHENOMENA (CEIDP), 2012, : 767 - 770
[2] Prediction of mechanical properties of elastomeric materials using neural networks
Fomin, S. V.
Rostovtsev, V. S.
Meltsov, V. U.
Shirokova, E. S.
[J]. MATERIALS PHYSICS AND MECHANICS, 2023, 51 (05): : 66 - 78
[3] Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks
Merayo, D.
Rodriguez-Prieto, A.
Camacho, A. M.
[J]. IEEE ACCESS, 2020, 8 : 13444 - 13456
[4] PREDICTION OF MECHANICAL PROPERTIES OF CUMIN SEED USING ARTIFICIAL NEURAL NETWORKS
Saiedirad, M. H.
Mirsalehi, M.
[J]. JOURNAL OF TEXTURE STUDIES, 2010, 41 (01) : 34 - 48
[5] Semantic Characteristics Prediction of Pulmonary Nodule Using Artificial Neural Networks
Li, Guangxu
Kim, Hyoungseop
Tan, Joo Kooi
Ishikawa, Seiji
Hirano, Yasushi
Kido, Shoji
Tachibana, Rie
[J]. 2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2013, : 5465 - 5468
[6] Prediction on friction characteristics of industrial brakes using artificial neural networks
Grzegorzek, Wojciech
Scieszka, Stanislaw F.
[J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY, 2014, 228 (10) : 1025 - 1035
[7] Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks
Correa, Ramon Santos
Sampaio, Patricia Teixeira
Braga, Rafael Utsch
Lambertucci, Victor Alberto
Almeida, Gustavo Matheus
Braga, Antonio Padua
[J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2020, 19 (04)
[8] Prediction of brake friction materials recovery performance using artificial neural networks
Aleksendric, Dragan
Barton, David C.
Vasic, Branko
[J]. TRIBOLOGY INTERNATIONAL, 2010, 43 (11) : 2092 - 2099
[9] Prediction of breakdown strength of cellulosic insulating materials using artificial neural networks
Singh, Sakshi
Mohsin, M. M.
Masood, Aejaz
[J]. JOURNAL OF ADVANCED DIELECTRICS, 2018, 8 (01)
[10] Prediction of mechanical properties of superplastic lnconel*718 using artificial neural networks
Huang, Y
Blackwell, PL
[J]. MATERIALS SCIENCE AND TECHNOLOGY, 2002, 18 (10) : 1104 - 1108

← 1 2 3 4 5 →