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Damage Detection of Truss Bridges Using Artificial Neural Network Considering the Effect of Non-Structural Elements | ||
| Contributions of Science and Technology for Engineering | ||
| دوره 1، شماره 1، خرداد 2024، صفحه 43-49 اصل مقاله (767.06 K) | ||
| شناسه دیجیتال (DOI): 10.22080/cste.2024.5013 | ||
| نویسندگان | ||
| Hamed Shirazi؛ Fariba Shadan* ؛ Meisam Qorbani Fouladi | ||
| Department of Civil Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran | ||
| تاریخ دریافت: 22 دی 1402، تاریخ بازنگری: 25 بهمن 1402، تاریخ پذیرش: 12 اسفند 1402 | ||
| چکیده | ||
| Identifying structural damages has been a crucial research topic in civil engineering over the past few decades. Numerical modeling methods are of particular interest for damage detection because they provide more information. The accuracy of modeling results can be impacted by errors in modeling the mass of non-structural elements. This study is focused on assessing the effects of the mass of non-structural components on the detection of current damages. An integrated neural network approach was used to study a truss bridge as a widely used structure. It was possible to detect damaged members with high accuracy using the artificial neural network trained with the results of the finite element model. According to the results, the introduced method accurately detects damage despite modeling errors associated with non-structural elements' mass | ||
| کلیدواژه‌ها | ||
| Damage Detection؛ Artificial Neural Network؛ Modeling Error؛ Non-Structural Elements؛ Dynamic Data | ||
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آمار تعداد مشاهده مقاله: 97 تعداد دریافت فایل اصل مقاله: 136 |
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