| Pub. Date | : Oct, 2019 |
|---|---|
| Product Name | : The IUP Journal of Structural Engineering |
| Product Type | : Article |
| Product Code | : IJSE31910 |
| Author Name | : Miguel Abambres, Marilia Marcy, Graciela Doz |
| Availability | : YES |
| Subject/Domain | : Science and Technology |
| Download Format | : PDF Format |
| No. of Pages | : 34 |
Fabrication technology and structural engineering states-of-the-art have led to a growing use of slender structures, making them more susceptible to static and dynamic actions that may lead to some sort of damage. In this context, regular inspections and evaluations are necessary to detect and predict structural damage and establish maintenance actions able to guarantee structural safety and durability with minimal cost. However, these procedures are traditionally quite time-consuming and costly, and techniques allowing a more effective damage detection are necessary. This paper assesses the potential of Artificial Neural Network (ANN) models in the prediction of damage localization in structural members; as function of their dynamic properties- the three first natural frequencies are used. Based on 64 numerical examples from damaged (mostly) and undamaged steel channel beams, an ANN-based analytical model is proposed as a highly accurate and efficient damage localization estimator. The proposed model yielded maximum errors of 0.2 and 0.7% concerning 64 numerical and three experimental data points, respectively. Due to the high-quality of results, the authors' next step is the application of similar approaches to entire structures based on much larger datasets.
Fabrication technology and structural engineering states-of-the-art have led to a growing use of slender structures in construction industry. Those structures (or structural members) are more susceptible to static and dynamic actions that may lead to damage and/or excessive vibration. In this context, regular inspections and evaluations are necessary to detect and predict structural damage and establish maintenance actions able to guarantee structural safety and durability with minimal cost. However, these procedures are traditionally quite time-consuming and costly. Thus, techniques allowing a more efficient and less resource-dependent damage detection are in high demand and will contribute to a more sustainable built environment.
Structural health monitoring, Damage localization, Steel beams, Dynamic properties, Natural frequencies, Artificial Neural Networks (ANNs)