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

Examination timetabling at the University of Cape Town: a tabu search approach to automation

With the rise of schedules and scheduling problems, solutions proposed in literature have expanded yet the disconnect between research and reality remains. The University of Cape Town's (UCT) Examinations Office currently produces their schedules manually with software relegated to error-checking status. While they have requested automation, this study is the first attempt to integrate optimisation techniques into the examination timetabling process. Tabu search and Nelder-Mead methodologies were tested on the UCT November 2014 examination timetabling data with tabu search proving to be more effective, capable of producing feasible solutions from randomised initial solutions. To make this research more accessible, a user-friendly app was developed which showcased the optimisation techniques in a more digestible format. The app includes data cleaning specific to UCT's data management system and was presented to the UCT Examinations Office where they expressed support for further development: in its current form, the app would be used as a secondary tool after an initial solution has been manually obtained

Statistical Inference on Dynamical Systems

The ordinary differential equation (ODE) is one representative and popular tool in modeling dynamical systems, which are widely implemented in physics, biology, economics, chemistry and biomedical sciences, etc. Because of the importance of dynamical systems in scientific studies, they are the main focuses of my dissertation. The first chapter of the dissertation is introduction and literature review, which mainly focuses on numerical integration algorithms of ODEs that are difficult to solve analytically, as well as derivative-free optimization algorithms for the so-called inverse problem. The second chapter is on the estimation method based on numerical solvers of differential equations. We start by reviewing the state-of-the-art Gauss-Newton algorithm based method, with the derivation of approximate confidence intervals. Furthermore, we propose and illustrate a method using Differential Evolution along with numerical ODE integration algorithms, as well as a hybrid method to improve the convergence issue for Gauss-Newton algorithm. A numerical comparison study shows the hybrid method is more numerically stable than the traditional Gauss-Newton algorithm based estimation method. In Chapter 3 we propose a novel two-step estimation method based on Fourier basis smoothing and pseudo least square estimator. It is less computationally intensive than methods using numerical ODE integration algorithms, and it works better on periodic or near periodic ODE model functions. In Chapter 4 we expand our study to a population-based hierarchical model to study the correlation between individual features and certain parameter values. Both ML and REML estimation are studied, with more emphasis on REML. An iterative estimation method that incorporates numerical ODE solver into the stochastic approximation EM algorithm for the hierarchical model is proposed and illustrated. Several simulation studies are presented, and a parallel version of the algorithm is implemented as well

A multi-criteria design framework for the synthesis of complex pressure swing adsorption cycles for CO2 capture

Pressure Swing Adsorption (PSA) is the most efficient option for middle scale separation processes. PSA is a cyclic process whose main steps are adsorption, at high pressure, and regeneration of the adsorbent, at low pressure. The design of PSA cycles is still mainly approached experimentally due to the computational challenges posed by the complexity of the simulation and by the need to detect the performance at cyclic steady state (CSS). Automated tools for the design of PSA processes are desirable to allow a better understanding of the the complex relationship between the performance and the design variables. Furthermore, the operation is characterised by trade-o�ffs between conflicting criteria. A multi-objective flowsheet design framework for complex PSA cycles is presented. A suite of evolutionary procedures, for the generation of alternative PSA con�figurations has been developed, including simple evolution, simulated annealing as well as a population based procedure. Within this evolutionary procedure the evaluation of each cycle confi�guration generated requires the solution of a multi-objective optimisation problem which considers the conflicting objectives of recovery and purity. For this embedded optimisation problem a multi-objective genetic algorithm (MOGA), with a targeted fi�tness function, is used to generate the approximation to the Pareto front. The evaluation of each alternative design makes use of a number of techniques to reduce the computational burden. The case studies considered include the separation of air for N2 production, a fast cycle operation which requires a detailed di�ffusion model, and the separation of CO2 from flue gases, where complex cycles are needed to achieve a high purity product. The novel design framework is able to determine optimal configurations and operating conditions for PSA for these industrially relevant case studies. The results presented by the design framework can help an engineer to make informed design decisions

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

Parameters optimization of a charge transport model for the electrical characterization of dielectric materials

Un modèle mathématique basé sur la physique des matériaux isolants a été développé dans notre laboratoire pour décrire le transport de charge bipolaire (BCT) dans le polyéthylène basse densité (LDPE) sous contrainte de courant continu. Les phénomènes de piégeage et de dé-piégeage, la hauteur de la barrière pour l'injection, la mobilité et le processus de recombinaison des charges positives et négatives sont considérés. Le modèle est basé sur l'équation de Poisson et la loi de conservation des charges. Ce modèle nécessite des entrées qui sont reliées aux conditions expérimentales telles que la température, la tension appliquée, l'épaisseur du diélectrique, etc., ainsi qu'un l'ensemble de paramètres tels que la barrière d'injection, la mobilité, les coefficients de piégeage et de dé-piégeage. La plupart de ces paramètres ne peuvent être prédits, observés ou estimés par des expériences indépendantes. Pour cette raison, un algorithme d'optimisation est utilisé pour optimiser le modèle BCT afin qu'il s'adapte aux mesures expérimentales, quelles que soient les conditions expérimentales. Le principe de ce type d'algorithme est basé sur la minimisation d'une fonction coût qui rend compte des écarts entre les données issues de l'expérience et celles issues du modèle. Les données expérimentales utilisées sont la densité de charge nette mesurée par la méthode électro-acoustique pulsée (PEA) ainsi que les mesures du courant de charge externe. Après avoir testé cinq algorithmes d'optimisation nous avons sélectionné l'algorithme Trust Region Reflective qui répond au mieux à nos critères. Cet algorithme a permis de trouver un ensemble de paramètres permettant une bonne corrélation entre les densités de courant et de charge simulées et celles obtenues expérimentalement. Cette optimisation a été réalisée en considérant différent champs électriques appliqués au matériau afin d'avoir un jeu de paramètre qui caractérise au mieux le matériau d'étude. En outre, l'algorithme d'optimisation a permis d'analyser la barrière d'injection lorsque les interfaces sont de natures différentes.A mathematical model based on the physics of insulating materials has been developed in our laboratory to describe the bipolar charge transport (BCT) in low-density polyethylene (LDPE) under DC stress. The phenomena of trapping and detrapping, the barrier height for injection, the mobility, and the recombination process of positive and negative charges are considered. The model is based on the Poisson equation and the law of conservation of charges. This model requires inputs that are related to the experimental conditions such as temperature, applied voltage, dielectric thickness, etc., as well as a set of parameters such as the injection barrier, mobility, trapping, and detrapping coefficients. Most of these parameters cannot be predicted, observed, or estimated by independent experiments. For this reason, an optimization algorithm is used to optimize the BCT model to fit the experimental measurements, whatever the experimental conditions, by minimizing the sum of squares of the deviations between the experimental data and the model data. The experimental data used are the net charge density measured by the pulsed electro-acoustic method (PEA) as well as the external charge current measurements. After testing five optimization algorithms we selected the following algorithm Trust Region Reflective which best meets our criteria. This algorithm has allowed us to find a set of parameters allowing a good correlation between the simulated current and charge densities with those obtained experimentally. This optimization was performed by considering different electric fields applied to the material in order to have a unique set of parameters that best characterizes the studied material. In addition, the optimization algorithm allowed to analyze the injection barrier when the interfaces are of different natures

Analysis of design variables of prestressed concrete structures using optimization algorithms

V posledních letech je stále více kladen důraz na úspory a ekologická hlediska ve stavebnictví. Celkové množství vyprodukovaného betonu na zemi je obrovské (odhadem 1010 tun za rok). Možnost snížení jeho výroby pouze o několik procent může přinést významné materiálové úspory, redukci výdajů na přepravu a s tím souvisejících nákladů. Mezi další aspekty je možné zařadit omezení produkce CO2 a jiných škodlivin. Z těchto důvodů se jeví optimalizační analýza návrhových parametrů betonových konstrukcí jako velmi důležitá. Obecně lze definovat optimalizaci jako nalezení nejlepšího řešení pro danou úlohu. Optimalizační úlohy jsou definovány pro různá odvětví a spojuje je hledání minima nebo maxima cílové funkce. Je známo, že matematické metody a algoritmy umožňující nalezení optimálního tvaru nebo parametrů konstrukce, jsou již řadu let používány ve strojírenství. Každodenní aplikace ve stavebnictví je však doslova výjimečná. Návrhem stavební konstrukce pomocí optimalizačních algoritmů se zabývají pouze práce na vědecké úrovni. Nalezení optimálního tvaru konstrukce je obvykle otázkou zkušeností a znalostí projektanta, který návrh následně ověří svým výpočtem. Existuje mnoho důvodů, proč nejsou tyto algoritmy používány v běžné praxi. Patří mezi ně zejména absence uživatelsky přístupného a srozumitelného programu, který by napomáhal zoptimalizovat konstrukci v relativně krátkém čase, a také složitost optimalizační úlohy. Dalším imitujícím faktorem je skutečnost, kdy stavební konstrukce jsou vystaveny mnoha omezujícím podmínkám požadovaných normou. A v neposlední řadě pak změna návrhových parametrů budov, mostů či speciálních typů konstrukcí nevykazuje pravidelnou odezvu. Výše uvedená problematika je náplní předložené disertační práce.During recent years more and more emphasis has been put on saving and ecological aspects of the civil engineering industry. As the total volume of concrete being produced on our planet is immense (ca 1010 tons per year), the possibility of decreasing it by even a small percentage can bring large savings in material costs, transport and other costs and reduction of CO2 production and other pollution. Therefore, optimal analysis of design variables of concrete structures appears to be of high importance. Optimization is finding the best solution to a given problem. Many disciplines define different optimization problems and it is typically the minimum or maximum value of the objective function that is searched. It is known that mathematical procedures and algorithms to find an optimal structural design are used in practice in mechanical engineering, but the use of these tools in civil engineering is rather exceptional. Generally, scientific works deal with the optimal design of structures only. Finding of an optimal shape and dimensions is usually a question of the engineer’s experience and good “guess”, which is then verified by calculation. There are many reasons explaining why optimization in common practise is used only occasionally. One of them is the absence of proper user friendly software tools which could help within relatively short time available for structural design. Another reason is the complexity of optimization tasks as well as a lot of constraints in civil engineering design codes. Last but not least, the change of design variables of buildings, bridges and structures of special types do not express regular response. This issue is discussed in the submitted work.