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Shirazi, Hamed, Shadan, Fariba, Qorbani Fouladi, Meisam. (1402). Damage Detection of Truss Bridges Using Artificial Neural Network Considering the Effect of Non-Structural Elements. پژوهشنامه مدیریت اجرایی, 1(1), 43-49. doi: 10.22080/cste.2024.5013
Hamed Shirazi; Fariba Shadan; Meisam Qorbani Fouladi. "Damage Detection of Truss Bridges Using Artificial Neural Network Considering the Effect of Non-Structural Elements". پژوهشنامه مدیریت اجرایی, 1, 1, 1402, 43-49. doi: 10.22080/cste.2024.5013
Shirazi, Hamed, Shadan, Fariba, Qorbani Fouladi, Meisam. (1402). 'Damage Detection of Truss Bridges Using Artificial Neural Network Considering the Effect of Non-Structural Elements', پژوهشنامه مدیریت اجرایی, 1(1), pp. 43-49. doi: 10.22080/cste.2024.5013
Shirazi, Hamed, Shadan, Fariba, Qorbani Fouladi, Meisam. Damage Detection of Truss Bridges Using Artificial Neural Network Considering the Effect of Non-Structural Elements. پژوهشنامه مدیریت اجرایی, 1402; 1(1): 43-49. doi: 10.22080/cste.2024.5013

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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