Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

Metamodel-based static and dynamic optimization of composite structures with ply drop-offs

Nowadays, there is no analytical equation able to analyze the issues related to the performance of structures with ply drop-offs. In order to address this issue, a metamodel using Design of Experiments and the SunFlower Algorithm for static and dynamic optimization of composite structures with ply drop-offs was developed in this study. Through numerical simulations and experimental tests, a characterization of the static and dynamic behavior of tapered hybrid and non-hybrid tubes was proposed. Then, a metamodel was developed considering the results obtained through numerical simulations, where the best ply drop-off location that provides the best static and dynamic conditions was identified, and, posteriorly, it was applied in the manufacture of the tubes. The numerical results revealed that the hybrid tube reinforced with carbon and glass of fibers supported a high loading in buckling conditions when compared with non-hybrid tubes. Before the manufacture of the tubular structures, an experimental comparative study using honeycomb sandwich structures with different face sheets and cores was proposed to analyze the fabric characteristics. The results showed that the hybrid fabric reinforced with glass and aramid of fibers was demonstrated to be not viable for tubular structure manufacture. Then, in the manufacture of the tubular structures, the carbon, glass, and carbon/aramid hybrid fabrics were applied. The experimental results obtained with the optimized structures revealed that the hybridization provided an increase in the level of damping. The modal analyses performed on the intact and damaged structures demonstrated a smooth reduction in the first natural frequency and in the damping factor for the damaged structures. Aiming a comparative analysis between tapered and non-tapered structures, tubular structures without ply drop-offs were manufactured and experimental tests were performed. The hybrid tapered structure manufactured with carbon, aramid, and glass of fibers proved to be a promising option in compression conditions, supporting a loading of 9.489 kN, while the non-tapered structure supported a loading of 13.283 kN. In addition, this hybrid structure revealed a lower manufacturing cost when compared with the other hybrid structures, and it was considered lighter with a mass of 53 grams. The non-tapered hybrid structure had a mass of 77 grams, 30% higher than the tapered structure’s mass. Therefore, metamodel-based static and dynamic optimization was demonstrated to be feasible and advantageous for determining the optimum ply drop-off location.Atualmente não existe uma equação analítica que seja capaz de analisar questões relacionadas ao desempenho de estruturas com ply drop-offs. Com intuito de suprir essa questão, um metamodelo usando Projeto de Experimentos e o algoritmo SunFlower para otimização do comportamento estático e dinâmico de estruturas com ply drop-offs foi proposto nesse estudo. Através de simulações numéricas e testes experimentais, uma caracterização sobre o comportamento estático e dinâmico de tubos escalonados híbridos e não híbridos foi proposta. Então, um metamodelo foi desenvolvido considerando os resultados obtidos com as simulações numéricas, onde a melhor localização para os ply drop-offs foi identificada e, posteriormente, esta localização ótima foi usada na manufatura dos tubos. Os resultados numéricos revelaram que o tubo híbrido reforçado com fibras de carbono e vidro suportou um maior carregamento em condições de flambagem quando comparado aos tubos não híbridos. Antes da manufatura dos tubos, um estudo comparativo experimental envolvendo estruturas sanduíches honeycomb considerando diferentes faces e núcleos foi desenvolvido com intuito de analisar as características de cada tecido aplicado na face das estruturas. Os resultados mostraram que o tecido híbrido reforçado com fibras de vidro e aramida não era viável na manufatura dos tubos. Então, para a manufatura das estruturas tubulares foram considerados os tecidos reforçados com fibras de carbono, vidro e carbono/aramida. Os resultados experimentais obtidos com as estruturas ótimas mostraram que a hibridização proporcionou um aumento no nível de amortecimento. As análises modais executadas com as estruturas intactas e danificadas demonstraram uma suave redução na primeira frequência natural e no fator de amortecimento para as estruturas danificadas. Então, estruturas tubulares sem drop-offs foram manufaturadas e testes experimentais foram realizados. A estrutura híbrida manufaturada com fibras de carbono, aramida e vidro provou ser uma opção promissora em condições de compressão, suportando um carregamento de 9,489 kN, enquanto a estrutura não escalonada suportou um carregamento de 13,283 kN. Além disso, essa estrutura foi considerada mais leve com massa de 53 gramas e revelou um custo de manufatura reduzido, quando comparado às outras estruturas híbridas. A estrutura híbrida não escalonada apresentou massa de 77 gramas, o que corresponde a uma massa 30% maior quando comparado à estrutura escalonada. Finalmente, o metamodelo baseado na otimização provou ser viável e vantajoso

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration

Time-series method has become of interest in damage detection, particularly for automated and continuous structural health monitoring. In comparison to the commonly used method based on modal data, time-series method offers a straightforward application due to having no requirement for modal analysis. Sensor clustering has been proven effective in improving the ability of time-series method to detect, locate and quantify damage. However, most of the applications rely on free vibration response that can be obtained directly by impact testing, which is difficult to practice for in-service structures, or indirectly by transforming the ambient vibration response. Therefore, a reliable method that allows direct utilisation of ambient vibration response for damage detection in structures without any data transformation is proposed in this study. The implementation of the proposed response-only method involves a three-stage procedure; (i) sensor clustering, (ii) time-series modelling and (iii) damage detection. Each sensor cluster is represented by a time-series model called nonlinear autoregressive with exogenous inputs (NARX) model, which is developed via artificial neural network (ANN) using undamaged acceleration data. The model is then utilised for predicting the damaged response and the difference between prediction errors is used to extract damage sensitive feature (DSF). The existence of uncertainties is addressed through setting up a damage threshold using several sets of undamaged data. The effectiveness of the method is demonstrated through a numerical slab model and experimental structures of reinforced concrete slabs and steel arches. It is found that the proposed structural damage detection approach based on NARX neural network is superior to linear ARX model as the approach is able to detect damage under ambient vibration. The results show that the highest predicted DSF corresponds to the location of damage and its value increases relatively with the severity of damage. Better damage detection is obtained when damage threshold is integrated into the proposed approach where the precision is increased by more than 24%. Overall, the proposed method is proven applicable to identify the existence, location and relative severity of structural damage under ambient vibration

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies

Damage Detection Using a Graph-based Adaptive Threshold for Modal Strain Energy and Improved Water Strider Algorithm

Damage detection through an inverse optimization problem has been investigated by many researchers. Recently, Modal Strain Energy (MSE) has been utilized as an index (MSEBI) for damage localization that serves to guide the optimization. This guided approach considerably reduces the computational cost and increases the accuracy of optimization. Although this index mostly exhibits an acceptable performance, it fails to find some damaged elements' locations in some cases. The aim of this paper is twofold. Firstly, a Graph-based Adaptive Threshold (GAT) is proposed to identify some of those elements that are not detected by basic MSEBI. GAT relies on the concepts from graph theory and MSE working as a simple anomaly detection technique. Secondly, an Improved version of the Water Strider Algorithm (IWSA) is introduced, applied to the damage detection problems with incomplete modal data and noise-contaminated inputs. Several optimization algorithms, including the newly-established Water Strider Algorithm (WSA), are utilized to test the proposed method. The investigations on several damage detection problems demonstrate the GAT and IWSA's satisfactory performance compared to the previous methods

An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings

Structural Health Monitoring (SHM) of historical building is an emerging field of research aimed at the development of strategies for on-line assessment of structural condition and identification of damage in the earliest stage. Built heritage is weak against operational and environmental condition and preservation must guarantee minimum repair and non-intrusiveness. SHM provides a cost-effective management and maintenance allowing prevention and prioritization of the interventions. Recently, in computer science, mimicking nature to address complex problems is becoming more frequent. Nature-inspired approaches turn out to be extremely efficient in facing optimization, commonly used to analyze engineering processes in SHM, providing interesting advantages when compared with classic methods. This paper begins with an introduction to Natural Computing. Then, focusing on its applications to SHM, possible improvements in built heritage conservation are shown and discussed suggesting a general framework for safety assessment and damage identification of existing structures.This work was financed by FEDER funds through the Competitiveness Factors Operational Programme COMPETE and by national funds through FCT - Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633info:eu-repo/semantics/publishedVersio

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area

Beam-like damage detection methodology using wavelet damage ratio and additional roving mass

Early damage detection plays an essential role in the safe and satisfactory maintenance of structures. This work investigates techniques use only damaged structure responses. A Timoshenko beam was modeled in finite element method, and an additional mass was applied along their length. Thus, a frequency-shift curve is observed, and different damage identification techniques were used, such as the discrete wavelet transform and the derivatives of the frequency-shift curve. A new index called wavelet damage ratio(WDR) is defined as a metric to measure the damage levels. Damages were simulated like a mass discontinuity and a rotational spring (stiffness damage). Both models were compared to experimental tests since the mass added to the structure is a non-destructive tool. It was evaluated different damage levels and positions. Numerical results showed that all proposed techniques are efficient techniques for damage identification in Timoshenko's beams concerning low computational cost and practical application