Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network
Document Type
Article
Publication Date
1-1-2017
Abstract
Structural failure can be prevented if the damage in the structure is detected at its onset and appropriate retrofitting carried out. Towards this end, this paper presents a vibration-based technique, using only the first vibration mode, for predicting damage and its location and severity in steel beams that are important structural components in buildings and bridges. For single damage scenarios, the modal strain energy based damage index β was capable of detecting, locating and quantifying damage. For multiple damage scenarios, Artificial Neural Network incorporating β as the input layer was used. This research used computer simulations supported by limited experiments. Damage intensity was specified as a percentage reduction in stiffness compared to that at first yield. The procedure is illustrated through several numerical examples and the results confirm the feasibility of the method and its application in preventing structural failure.
Keywords
Damage prediction, Failure prevention, Vibration based technique, Modal strain energy, Artificial Neural Network, Damage location, Damage severity, Damage index, Damage scenarios
Divisions
sch_civ
Funders
Ministry of Higher Education (MOHE), Malaysia [grant number UM.C/625/1/HIR/MOHE/ENG/55]
Publication Title
Engineering Failure Analysis
Volume
79
Publisher
Elsevier