Applying deep learning and wavelet transform for predicting the vibration behavior in variable thickness skew composite plates with intermediate elastic support

et al. 2022

Sensors

Self Cite

Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

Section: Step 4: Generation Of New Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification

Mohebian

Aval

et al. 2022

Sensors

Self Cite

“…Due to the extensive computational cost associated with the FE method (FEM), and especially for model updating, FEM is not an efficient and sufficient approach. A fuzzy method-based approach combined with neural network concepts, [50][51][52][53][54] such as an adaptive neuro-fuzzy inference system (ANFIS), can be more useful for analyzing this problem. Several research studies for predicting the strength and displacement of RC structures have been completed by Demir 55 ; Sobhani et al 56 ; Nikoo et al 57 ; Padimi et al 58 ; Khademi et al 59 The effectiveness of ANFIS compared with the artificial neural network (ANN) approach and its capability to solve more complex systems have been demonstrated by a few researchers.…”

Section: Introductionmentioning

confidence: 99%

Reliability of reinforced concrete beams in serviceability limit state via microprestress-solidification theory, a structural health monitoring strategy

Ghabdian

Aval

Proceedings of the Institution of Mechanical Engineers, Part L:

et al. 2022

An important and critical area within the broad domain of structural health monitoring, as related to reinforced civil and mechanical structures, is the assessment of creep, shrinkage, and high-temperature effects on reliability and serviceability. Unfortunately, the monitoring and impact of these inherent mechanical characteristics and behaviors, and subsequent impact on serviceability, have rarely been considered in the literature in structural health monitoring. In this paper, the microprestress-solidification creep theory for beams is generalized for the simultaneous effect of linear/nonlinear creep, shrinkage, and high temperature in a reliability framework. This study conducts a systematic time-dependent procedure for the reliability analysis of structures using a powerful nanoscale method. It must be noted that this paper aims to extend the previously developed microprestress-solidification method in a health monitoring reliability-based framework with a close look at a nonlinear creep, parameters affecting creep, and long-time high temperature. A finite element approach is proposed where creep, shrinkage, temperature, and cracking are considered using strain splitting theory. First, the model performance was evaluated by comparing the results with the experimental test available in the literature in the case of creep and shrinkage. Then, the simultaneous effect of creep, shrinkage, and temperature was compared with experimental results obtained by the authors. Reliability analysis was applied to reinforced concrete beams subjected to sustained gravity loading and uniform temperature history in order to calculate exceedance probability in the serviceability limit state. It was found that the exceedance probability of reinforced concrete beams was dependent on the shear span-to-depth ratio. In the serviceability limit state, exceedance probabilities of 0.012 and 0.157 were calculated for the span-to-depth ratios of 1 and 5, respectively. In addition, it was shown that temperature plays an important role in the reliability of reinforced concrete beams. A 4.27-fold increase was observed in the case of moderate to high temperature. Finally, for three different load levels of 40%, 70%, and 80%, the exceedance probabilities were 0.156, 0.328, and 0.527, respectively, suggesting that load level is another key parameter affecting the reliability of reinforced concrete beams. It is thus concluded these fundamental phenomenological studies should be further considered as part of the broad field of structural health monitoring.

“…46–54 In structural dynamics analysis, Prandtl–Ishlinskii model and its modified form has been integrated with artificial neural networks and utilized for structural response prediction. 55–66 However, most of the work reported in the literature do not include the structural dynamic analysis of structures with asymmetric hysteresis.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification and dynamic response analysis of a modified Prandtl–Ishlinskii asymmetric hysteresis model via least-mean square algorithm and particle swarm optimization

Wang

Proceedings of the Institution of Mechanical Engineers, Part L:

Altabey

et al. 2021

Hysteresis is a nonlinear phenomenon observed in the dynamic response behavior of numerous structural systems under high intensity cyclic or random loading, as well as in numerous mechanical and electromagnetic systems. For several decades, hysteretic response analysis of structural systems has been widely studied and numerous hysteresis models have been proposed and utilized in order to reproduce and better understand the complex hysteretically degrading behavior of structural systems. An important area of research in this regard has been the parameter identification of hysteretic systems. In this paper, we propose a modified Prandtl–Ishlinskii model to simulate the asymmetric hysteresis, which is the complex behavior in structural systems. In addition, a new approach based on particle swarm optimization and least-mean square algorithm is utilized for parameter identification of this hysteresis model. Finally, the model is applied in structural dynamic response analysis of a base isolated structural model under seismic load.