Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique

被引：35

作者：

Behnia, Arash ^{[1
]}

Ranjbar, Navid ^{[2
]}

Chai, Hwa Kian ^{[3
]}

Masaeli, Mahyar ^{[4
]}

机构：

[1] Monash Univ, Sch Engn, Discipline Civil Engn, Subang Jaya, Malaysia

[2] Shiraz Univ, Fac Engn, Dept Civil Engn, Shiraz, Iran

[3] Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur, Malaysia

[4] Griffith Univ, Fac Engn & Informat Technol, Gold Coast, Australia

来源：

CONSTRUCTION AND BUILDING MATERIALS | 2016年 / 122卷

关键词：

Acoustic emission; Bathtub curve; Damage detection; Reliability analysis; Ferrocement slabs; Machine learning; SUPPORT VECTOR MACHINE; CONCRETE BEAMS; REINFORCED-CONCRETE; PERFORMANCE; PARAMETERS; FLEXURE;

D O I：

10.1016/j.conbuildmat.2016.06.130

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This paper introduces suitable features and methods to define hazard rate function by acoustic emission (AE) parametric analysis to develop robust damage statement index and reliability analysis. AE signal energy was first examined to find out the relation between damage progress and AE signal energy so that a damage index based on AE signal energy could be proposed to quantify progressive damage imposed to ferrocement composite slabs. Moreover, by using AE signal strength, historic index could be computed and utilized to develop a modified hazard rate function through integration of bathtub curve and Weibull function. Furthermore, to provide a practical scheme for real condition monitoring, support vector regression was utilized to produce a robust tools for failure prediction considering uncertainties exist in real structures. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：823 / 832

页数：10

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