Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Alkayem, Nizar Faisal; Cao, Maosen; Ragulskis, Minvydas

doi:10.1155/2018/3541676

Cited by 16 publications

(5 citation statements)

References 55 publications

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“…Many real-world scenarios involve simultaneously optimization of several objectives in order to solve a certain problem [26,27,28]. Formally, a general multiobjective optimization problem (MOP) can be formulated as [29] minimize…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

Multiobjective Genetic Algorithms for Reinforcing Equal Population in Congressional Districts

Lara-Caballero

Silva

Mora-Gutiérrez

et al. 2019

Mathematical Problems in Engineering

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Redistricting is the process of partitioning a set of basic units into a given number of larger groups for electoral purposes. These groups must follow federal and state requirements to enhance fairness and minimize the impact of manipulating boundaries for political gain. In redistricting tasks, one of the most important criteria is equal population. As a matter of fact, redistricting plans can be rejected when the population deviation exceeds predefined limits. In the literature, there are several methods to balance population among districts. However, further discussion is needed to assess the effectiveness of these strategies. In this paper, we considered two different strategies, mean deviation and overall range. Additionally, a compactness measure is included to design well-shaped districts. In order to provide a wide set of redistricting plans that achieve good trade-offs between mean deviation, overall range, and compactness, we propose four multiobjective metaheuristic algorithms based on NSGA-II and SPEA-II. The proposed strategies were applied in California, Texas, and New York. Numerical results show that the proposed multiobjective approach can be a very valuable tool in any real redistricting process.

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Section: Multiobjective Optimizationmentioning

confidence: 99%

Multiobjective Genetic Algorithms for Reinforcing Equal Population in Congressional Districts

Lara-Caballero

Silva

Mora-Gutiérrez

et al. 2019

Mathematical Problems in Engineering

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“…Next, the DM applies MCDM techniques on the set of Pareto front solutions to pick one best solution. Various researchers have previously implemented this process in [13][14][15]. An optimizer combining both of these approaches was previously developed, which was called Multi-Criteria Decision Making-Pareto Front (M-PF) optimizer [16].…”

Section: Introductionmentioning

confidence: 99%

Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization

Kesireddy,

Medrano

2024

Algorithms

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Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used.

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“…With the increasing complexity and size of modern structures, the effectiveness of local methods has diminished [3]. The advent of sensors and transducers has spurred the development of global methods, which have overcome the limitations of local approaches [4].…”

Section: Introductionmentioning

confidence: 99%

Model Updating for Damage Identification: Leveraging Response Surface Methodology and Frequency Response Function Curvature

Nur Raihana Sukri,

Nurulakmar Abu Husain,

Syarifah Zyurina Nordin

et al. 2024

ARAM

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The limitations of the classical finite element (FE) model updating-based damage identification method, described as having convergence problems and a high computing cost, served as a catalyst for this investigation. Unlike traditional FE models, response surface methodology (RSM) employs explicit mathematical functions to describe the input-response relationship, offering a clear and concise representation. However, the current application of RSM is restricted to a few responses and updating parameters, and unresolved issues persist, including symmetrical damage location and false damage detection. In this study, a new RSM approach is introduced, using FRF curvature as the response. The effectiveness of the proposed method is demonstrated through an experimental modal analysis of a free-free aluminium beam, employing four different Design of Experiment (DOE) techniques: a minimum-run resolution V (CCDmrv) design and a half-fractional (CCDhalf) design using Central Composite Design (CCD), Box-Behnken design (BBD), and D-optimal design. The research systematically evaluates and compares the performance of these DOE techniques in identifying damage. Overall, the results highlight the success of the RSM method, particularly CCDhalf, D-optimal and BBD, in effectively identifying damage.

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Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Cited by 16 publications

References 55 publications

Multiobjective Genetic Algorithms for Reinforcing Equal Population in Congressional Districts

Multiobjective Genetic Algorithms for Reinforcing Equal Population in Congressional Districts

Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization

Model Updating for Damage Identification: Leveraging Response Surface Methodology and Frequency Response Function Curvature

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