An efficient stochastic-based coupled model for damage identification in plate structures

Abstract

Mode shape-based method has shown its dominance in failure identification of beams. However, it is a challenge for fault detection in a plate structure. It requires combined information in two directions to determine the damage location. In this study, a damage index, namely mode shape derivative based damage identification (MSDBDI), is applied to localize damage in fixed-free plate structures. Two-dimensional (2D) displacement mode shapes and their derivatives are used to identify the MSDBDI index. It is a fact that this indicator is stuck in indicating the damage severity. Hence, a coupled model between an artificial neural network (ANN) and antlion optimizer (ALO), so-called ALOANN is used to overcome this drawback. In this method, ALO instead of a backpropagation algorithm is used to look for the best initial values of learnable parameters of ANN i.e. weights and biases through mean squared error (MSE). These obtained parameters are added to ANN for damage identification. The efficiency of the proposed approach is tested with two numerical studies of the plate structures with single and multiple damage scenarios. In the first application, damage scenarios in a plate-like structure are detected. In the second application, ALO first is used to build a FE model of a composite structure based on a vibration experiment. Then the slab of the updated model is assumed to be suffered several damage scenarios. In both applications, failures are localized by using damage index. Then, the proposed approach is used to quantify the corresponding extent by means of changes in frequencies and displacement mode shapes. Values of damage index are achieved from modal properties of one or three out of the first five modes of the two considered structures. A conventional ANN also is investigated for comparison. Results of damage identification indicate that the damage indicator coupled with ALOANN show better performance compared with using ANN alone even when a noise level is assigned to modal properties.

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

“…The theory of the damage diagnosis based on the modal curvature [23] is as follows: For a plate structure, the deflection field of the plate is given by 𝑤(𝑥, 𝑦), where (𝑥, 𝑦) represents the two-dimensional position of the plate, and 𝐷(𝑥, 𝑦) represents the flexural stiffness of the plate. The modal curvature, which is the second derivative of the deflection Notably, n/Nf represents the normalized fatigue life of the GFRP, and E/E0 represents the normalized stiffness of the GFRP.…”

“…Abdel Wahab and De Roeck [22] introduced a one-dimensional modal-curvature-based DI that could enhance the accuracy of damage diagnosis, demonstrating its effectiveness with numerical data from beam models. Long Viet Ho [23] expanded this approach to two-dimensional models and defined the DI for plate structures. In conclusion, for large-scale structures, like ships, NDT methods grounded in modal parameters are the most apt.…”

A Review of Recent Developments in Friction Stir Welding for Various Industrial Applications

“…Machine learning applications have been seen in the FSW field as well. ML approaches have demonstrated interesting applications in a variety of engineering fields, including fracture mechanics [25], structural engineering [26], composite materials [27], laser cutting [28], measurement science [29], metal cutting [30], and friction stir processing [31] to name a few. Different FSW technologies have unique welding parameters and conditions, making modeling the process challenging and time intensive.…”

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages

“…Ho et al developed a coupled model between the ANN and antlion optimizer method based on the stochastic theory to locate the damage position. They used the FE model to obtain modal parameters and then generated a damage index as the main damage localization factor [ 21 ]. The ML-based surrogate model along with natural frequencies, mode shapes, and damage indexes is proposed by Lee et al [ 2 ], where-in due to limited sensors, displaced mode shapes were not available for all of the degrees of freedom.…”