چكيده به لاتين
Damage is constantly accumulating in load-bearing structures, such as bridges, buildings, and offshore platforms, during their service life, and many parts of our technical infrastructure are approaching or exceeding their lifespan. However, due to economic issues, these structures are used despite aging and the accumulation of related damage. Undetected damage may lead to structural failure and loss of human life. Therefore, it is important and necessary to detect damage in a structure and make proper repairs as soon as possible. In this research, a supervised learning method has been used to identify, locate and severity of damage using a combination of proper orthogonal modes (POM) and artificial neural network. proper orthogonal modes are a function of external loading and mechanical properties and retain the main advantage of vibration techniques, i.e. monitor global changes in the structure. In this study, the measured strains of the structural elements are used to calculate the POM. To detect the difference between healthy and damaged POMs, an artificial neural network regression is trained by a group of POMs from different loads to identify the damage. To test the proposed method, a simulated test is performed to a truss structure to identify damage. A 25-element truss is modeled using the finite element method in MATLAB software and subjected to different loads, then the loads are grouped according to the strain RMS. The neural network recognizes the severity of the damage well in the groups 1, 2 and 3, but in the group 4 and members 21 and 22 of the truss, it is observed that the neural network can not properly detect damage in all cases. The results also show that this method detects the location and severity of the damage for damages with severity higher than 50% and the error in intact elements reduced and for low damages such as the amount of misdiagnosis in the observed elements between 10 and 15% can be said that this method is suitable for damages greater than 20%. Finally, it can be concluded that the proposed method can successfully identify the damage at different levels.
