Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms

Improved dynamical particle swarm optimization method for structural dynamics

A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version

Best-worst northern goshawk optimizer: a new stochastic optimization method

This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

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

Development of Hierarchical Optimization-based Models for Multiscale Damage Detection

In recent years, health monitoring of structure and infrastructure systems has become a valuable source of information for evaluating structural integrity, durability and reliability throughout the lifecycle of structures as well as ensuring optimal maintenance planning and operation. Important advances in sensor and computer technologies made possible to process a large amount of data, to extract the characteristic features of the signals, and to link those to the current structural conditions. In general, the process of data feature extraction relates to solving an inverse problem, in either a data-driven or a model-based type setting. This dissertation explores state-of-the-art hierarchical optimization-based computational algorithms for solving multiscale model-based inverse problems such as system identification and damage detection. The basic idea is to apply optimization tools to quantify an established model or system, characterized by a set of unknown governing parameters, via minimizing the discrepancy between the predicted system response and the measured data. We herein propose hierarchical optimization algorithms such as the improved artificial bee colony algorithms integrated with local search operators to accomplish this task. In this dissertation, developments in multiscale damage detection are presented in two parts. In the first part, efficient hybrid bee algorithms in both serial and parallel schemes are proposed for time domain input-output and output-only identification of macro-scale linear/nonlinear systems such as buildings and bridges. Solution updating strategies of the artificial bee colony algorithm are improved for faster convergence, meanwhile, the simplex method and gradient-based optimization techniques are employed as local search operators for accurate solution tuning. In the case of output-only measurements, both system parameters and the time history of input excitations can be simultaneously identified using a modified Newmark integration scheme. The synergy between the proposed method and Bayesian inference are proposed to quantify uncertainties of a system. Numerical and experimental applications are investigated and presented for macro-scale system identification, finite element model updating and damage detection. In the second part, a framework combining the eXtended Finite Element Method (XFEM) and the proposed optimization algorithms is investigated, for nondestructive detection of multiple flaws/defects embedded in meso-scale systems such as critical structural components like plates. The measurements are either static strains or displacements. The number of flaws as well as their locations and sizes can be identified. XFEM with circular and/or elliptical void enrichments is employed to solve the forward problem and alleviates the costly re-meshing along with the update of flaw boundaries in the identification process. Numerical investigations are presented to validate the proposed method in application to detection of multiple flaws and damage regions. Overall, the proposed multiscale methodologies show a great potential in assessing the structural integrity of building and bridge systems, critical structural components, etc., leading to a smart structure and infrastructure management system

Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference

Structural damage identification can be considered as an optimization problem, by defining an appropriate objective function relevant to structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for structural damage identification with the modified objective function based on sparse regularization and Bayesian inference. To improve the global optimization capacity and robustness of the original Jaya algorithm, a clustering strategy is employed to replace solutions with low-quality objective values and a new updated equation is used for the best-so-far solution. The objective function that is sensitive and robust for effective and reliable damage identification is developed through sparse regularization and Bayesian inference and used for optimization analysis with the proposed I-Jaya algorithm. Benchmark tests are conducted to verify the improvement in the developed algorithm. Numerical studies on a truss structure and experimental validations on an experimental reinforced concrete bridge model are performed to verify the developed approach. A limited quantity of modal data, which is distinctively less than the number of unknown system parameters, are used for structural damage identification. Significant measurement noise effect and modelling errors are considered. Damage identification results demonstrate that the proposed method based on the I-Jaya algorithm and the modified objective function based on sparse regularization and Bayesian inference can provide accurate and reliable damage identification, indicating the proposed method is a promising approach for structural damage detection using data with significant uncertainties and limited measurement information

Stability Analysis of Artificial Bee Colony Optimization Algorithm

Theoretical analysis of swarm intelligence and evolutionary algorithms is relatively less explored area of research. Stability and convergence analysis of swarm intelligence and evolutionary algorithms can help the researchers to fine tune the parameter values. This paper presents the stability analysis of a famous Artificial Bee Colony (ABC) optimization algorithm using von Neumann stability criterion for two-level finite difference scheme. Parameter selection for the ABC algorithm is recommended based on the obtained stability conditions. The findings are also validated through numerical experiments on test problems

Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant

The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems: Minimum Spanning Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color Quantization, and Image Segmentation and compared to traditional and bio-inspired algorithms. Overall, the performance of the algorithm in all tasks is satisfactory.Comment: 45 page