Inverse Analysis of Structural Damage Based on the Modal Kinetic and Strain Energies with the Novel Oppositional Unified Particle Swarm Gradient-Based Optimizer

Abstract

Structural damage inspection is a key structural engineering technique that strives for ensuring structural safety. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using evolutionary computation. By considering the recent advances in this field, an efficient hybrid objective function that combines the global modal kinetic and modal strain energies is introduced. The newly developed objective function aims to extract maximum dynamic information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the oppositional learning (OL) for population initialization and convergence acceleration is first adopted. Thereafter, the unified particle swarm algorithm (UPSO) mechanism is combined with another newly developed algorithm, the gradient-based optimizer (GBO). The new algorithm, called the oppositional unified particle swarm gradient-based optimizer (OL-UPSGBO), with the convergence acceleration feature of (OL), enhances balanced exploration-exploitation of UPSO, and the local escaping operator of GBO is designed to specifically deal with the complex inverse analysis of structural damage problems. To authenticate the performance of the OL-UPSGBO, the complex benchmark set of CEC 2017 is adopted to compare the OL-UPSGBO with several original metaheuristics. Furthermore, the developed approach for structural damage identification is tested using several damage scenarios in a multi-story frame structure. Results show that the developed approach shows superior performance and robust behavior when tackling the inverse analysis of structural damage.

Application and planning of an energy-oriented stochastic disassembly line balancing problem

“…Although this paper has successfully investigated the minimization of energy consumption under uncertain disassembly operations, there are still many directions that can be investigated in the future, such as the use of more distributional forms to express the stochastic disassembly time (Alkayem et al 2022a , b ; Tian et al 2011a , b ; Wang et al 2021 ) and classify different disassembly fuzzy representations according to the actual situation. The integration of big data and the Internet of Things with disassembly operations is also a direction that can be explored in depth.…”

“…In addition, Mojtahedi et al applied SEO with memory storage to a vehicle routing problem for solid waste transport and proved that their proposed new variant of SEO leads to better solutions. Alkayem et al ( 2022a , b ) combined SEO and particle swarm algorithms to propose a new hybrid algorithm (SEPSO) and applied it to the structural health monitoring problem, and the proposed algorithm was shown to have good local and global search capabilities. Our main contributions can be seen by comparing the findings of our research to those of other studies: This paper proposes a SEDLBP and a mathematical model to describe it in order to minimize the amount of energy consumption during the disassembly process, taking into account the variable disassembly time and energy consumption as well as the specific difficulty of the disassembly tasks.…”

Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks

“…The OBL is structured by comparing the current population with its opposite, as the latter could be closer to the global optimum. Furthermore, the quasi-opposite number has been shown to be even closer to the global optimum than the opposite number [31]. Thus, the quasi-opposite population is calculated by a random probabilistic value and compared to the current population, then the best candidate between them is selected, as shown in Figure 2.…”

A Novel Adaptive Manta-Ray Foraging Optimization for Stochastic ORPD Considering Uncertainties of Wind Power and Load Demand

“…The second modification is the QOBL, which is an efficient method that can be applied for performance enhancement and searching abilities of several optimization algorithms [34]. The QOBL is a combination of the oppositional-based learning (OBL) mechanism along with the Quasi-based method that was applied with several optimization algorithms [52][53][54][55]. In the OLB, these populations update their placement to the opposite number or the mirror location of the population as follows:…”