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

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