Implementing fuzzy-based artificial intelligence approach for location of damage in structures - Hakim, S. J. S.; Ibrahim, M. H. W.; Mohammadhassani, M.; Yeoh, D.; M. Jaini, Z.; T. Chik, T.N. (Horizon Research Publishing Corporation, 2022)
Modal parameters are functions of the physical characteristics of a structure and they are very sensitive to damage. Therefore, any alterations in the physical features can change the vibration parameters of a structure. Modal data such as natural frequencies and mode shapes are easy to acquire from the measurements of structural behavior. One method of structural damage identification is to apply natural frequency. Natural frequencies represent the global behaviors of a structure and are not too sensitive when detecting the damage in structures and cannot offer spatial information about structural changes, and thus, their application is considered as challenging. On the other hand, a mode shape is a vibrational deformation of a system and it represents the relative displacement of all parts of a structure and can provide spatial information as well as give a significant indication of the damage occurring in a structure. In this present research, an intelligent hybrid approach, namely adaptive neuro-fuzzy inference system (ANFIS), as a fuzzy-based artificial intelligence approach was developed and applied due to its ability to recognize patterns, strong computational features, and capability of locating defects in a scaled girder bridge using direct modal parameters. The experimental analysis and numerical simulations of a steel girder bridge provided mode shape parameter datasets under different positions and sizes of faults in the structure. The results demonstrated the effectiveness of this method and provided acceptable precision even when the input datasets contained errors or were corrupted with a certain level of noise.