Application of neural network for prediction of unmeasured mode shape in damage detection
The major problem in the vibration-based damage detection field is still a limited number of sensors and the existence of uncertainties. In this paper, a new approach combines a multi-stage ANN model and statistical method to detect damage based on the limited number of sensors with consideration of...
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my.utm.506692018-11-09T08:11:26Z http://eprints.utm.my/id/eprint/50669/ Application of neural network for prediction of unmeasured mode shape in damage detection Goh, Lyn Dee Bakhary, Norhisyam Abdul Rahman, Azlan Ahmad, Baderul Hisham TA Engineering (General). Civil engineering (General) The major problem in the vibration-based damage detection field is still a limited number of sensors and the existence of uncertainties. In this paper, a new approach combines a multi-stage ANN model and statistical method to detect damage based on the limited number of sensors with consideration of uncertainties. The first stage of the ANN is used to predict the unmeasured mode shapes data based on limited measured modal data. The second stage ANN is devoted to predicting the damage location and severity using the complete modal data from the first-stage ANN. To incorporate the uncertainties in modal data, Gaussian noise is applied to the input variables and the probability of damage existence is calculated using Rosenblueth's point estimate method. The feasibility of the proposed method is demonstrated using an analytical model of a continuous two-span reinforced concrete slab. The application of a multi-stage ANN showed results having a high potential of overcoming the issue of using a limited number of sensors in structural health monitoring. Sage Journals 2013 Article PeerReviewed Goh, Lyn Dee and Bakhary, Norhisyam and Abdul Rahman, Azlan and Ahmad, Baderul Hisham (2013) Application of neural network for prediction of unmeasured mode shape in damage detection. Advances In Structural Engineering, 16 (1). pp. 99-113. ISSN 1369-4332 http://journals.sagepub.com/doi/10.1260/1369-4332.16.1.99 |
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TA Engineering (General). Civil engineering (General) Goh, Lyn Dee Bakhary, Norhisyam Abdul Rahman, Azlan Ahmad, Baderul Hisham Application of neural network for prediction of unmeasured mode shape in damage detection |
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The major problem in the vibration-based damage detection field is still a limited number of sensors and the existence of uncertainties. In this paper, a new approach combines a multi-stage ANN model and statistical method to detect damage based on the limited number of sensors with consideration of uncertainties. The first stage of the ANN is used to predict the unmeasured mode shapes data based on limited measured modal data. The second stage ANN is devoted to predicting the damage location and severity using the complete modal data from the first-stage ANN. To incorporate the uncertainties in modal data, Gaussian noise is applied to the input variables and the probability of damage existence is calculated using Rosenblueth's point estimate method. The feasibility of the proposed method is demonstrated using an analytical model of a continuous two-span reinforced concrete slab. The application of a multi-stage ANN showed results having a high potential of overcoming the issue of using a limited number of sensors in structural health monitoring. |
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Article |
author |
Goh, Lyn Dee Bakhary, Norhisyam Abdul Rahman, Azlan Ahmad, Baderul Hisham |
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Goh, Lyn Dee Bakhary, Norhisyam Abdul Rahman, Azlan Ahmad, Baderul Hisham |
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Goh, Lyn Dee |
title |
Application of neural network for prediction of unmeasured mode shape in damage detection |
title_short |
Application of neural network for prediction of unmeasured mode shape in damage detection |
title_full |
Application of neural network for prediction of unmeasured mode shape in damage detection |
title_fullStr |
Application of neural network for prediction of unmeasured mode shape in damage detection |
title_full_unstemmed |
Application of neural network for prediction of unmeasured mode shape in damage detection |
title_sort |
application of neural network for prediction of unmeasured mode shape in damage detection |
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Sage Journals |
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2013 |
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http://eprints.utm.my/id/eprint/50669/ http://journals.sagepub.com/doi/10.1260/1369-4332.16.1.99 |
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