Tarbiat Modares University Modares Civil Engineering journal 2476-6763 10 3 2010 11 22 Health Monitoring of Cracked Cantilever Beams Using Artificial Neural Networks Considering Nonlinear Crack Behavior Health Monitoring of Cracked Cantilever Beams Using Artificial Neural Networks Considering Nonlinear Crack Behavior 11645 FA ئ. Joharzadeh N. Khaji A Bahreininejad 3 Journal Article Abstract In this paper, using Artificial Neural Networks (ANNs) and Finite Element Method (FEM), health monitoring of damaged cantilever beams having longitudinal cracks is discussed. The main focus is devoted to the nonlinear behavior (breathing) of crack, which, to our knowledge, is taken into account in the crack detection of structures using ANNs, for the first time. Thus nonlinear behavior of crack is modeled using FEM.The changes in the natural frequencies (due to crack) of various vibration modes were implemented as input for training and testing of ANNs. By producing various scenarios for sound and damaged beams (with different damage location and severity), two specific classes of ANNs were trained to predict the location and length of longitudinal cracks. The Results showed a promising prediction for the length of cracks by the proposed methodology. Also a considerable approximation observed in the prediction of cracks location. Abstract In this paper, using Artificial Neural Networks (ANNs) and Finite Element Method (FEM), health monitoring of damaged cantilever beams having longitudinal cracks is discussed. The main focus is devoted to the nonlinear behavior (breathing) of crack, which, to our knowledge, is taken into account in the crack detection of structures using ANNs, for the first time. Thus nonlinear behavior of crack is modeled using FEM.The changes in the natural frequencies (due to crack) of various vibration modes were implemented as input for training and testing of ANNs. By producing various scenarios for sound and damaged beams (with different damage location and severity), two specific classes of ANNs were trained to predict the location and length of longitudinal cracks. The Results showed a promising prediction for the length of cracks by the proposed methodology. Also a considerable approximation observed in the prediction of cracks location. https://mcej.modares.ac.ir/article_11645_cfd382c5eb817d52c7faf45a96f20b81.pdf