Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique
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 energ...
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my.um.eprints.184942017-12-07T07:41:25Z http://eprints.um.edu.my/18494/ Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique Behnia, A. Ranjbar, N. Chai, H.K. Masaeli, M. TA Engineering (General). Civil engineering (General) 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. Elsevier 2016 Article PeerReviewed Behnia, A. and Ranjbar, N. and Chai, H.K. and Masaeli, M. (2016) Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique. Construction and Building Materials, 122. pp. 823-832. ISSN 0950-0618 http://dx.doi.org/10.1016/j.conbuildmat.2016.06.130 doi:10.1016/j.conbuildmat.2016.06.130 |
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TA Engineering (General). Civil engineering (General) Behnia, A. Ranjbar, N. Chai, H.K. Masaeli, M. Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
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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. |
format |
Article |
author |
Behnia, A. Ranjbar, N. Chai, H.K. Masaeli, M. |
author_facet |
Behnia, A. Ranjbar, N. Chai, H.K. Masaeli, M. |
author_sort |
Behnia, A. |
title |
Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
title_short |
Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
title_full |
Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
title_fullStr |
Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
title_full_unstemmed |
Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
title_sort |
failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique |
publisher |
Elsevier |
publishDate |
2016 |
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http://eprints.um.edu.my/18494/ http://dx.doi.org/10.1016/j.conbuildmat.2016.06.130 |
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1643690721043021824 |
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13.159267 |