چكيده به لاتين
Identifying the location and the severity of potential damages is the main part of the Structural Health Monitoring (SHM) program which can return valuable information to design rehabilitation plans. Early damage detection in civil structures not only prevents unexpected failures, but also it can dramatically decrease maintenance costs. Consequently, one of the most significant subjects related to the maintenance of structures in the field of structural engineering is the structural health monitoring.
In this study an efficient vibration-based damage detection method is proposed by defining the problem as an inverse model updating problem. Using frequency-wise weighted mode shapes, a new damage-sensitive objective function is proposed and a novel optimization algorithm, which is a hybrid version of the Grey Wolf Optimizer and Tabu Search algorithms –called GWO-TS algorithm, is introduced and used to solve the damage detection problem. Several numerical examples, containing a 2D simple beam, a planar truss, a spatial frame, and a cantilever beam, are studied to evaluate the performance of the proposed method under different conditions. Moreover, the precision, accuracy and convergence rate of the proposed GWO-TS algorithm is compared with three other optimization algorithms (e.g., GWO, GA, and PSO) to assess its performance in dealing with highly ill-posed problems. The obtained results demonstrate that the proposed method is capable of damage localization and quantification in various civil structures either ideal or noisy input data is fed.
