A two-step approach for damage detection in beam based on influence line and bird mating optimizer

This paper presents a two-step approach for structural damage identification in beam structure using the influence line and bird mating optimizer (BMO). Local damage is simulated as the reduction of the elemental Young’s modulus and mass of beam element. The technique for damage localization based on influence line and its derivatives before and after damage for beam structure was outlined. An objective function comprised of dynamic acceleration is utilized for BMO algorithm. The dynamic response data under external force is calculated by Newmark integration method. Numerical examples of a simply supported beam was investigated. Effect of measurement noise is studied. Studies in the paper indicate that the proposed method is efficient and robust for identifying damages in beam structures

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

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

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

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

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

Studies on the biology of the butterflies anthocharis cardamines (L) and pieis napi (L), in relation to speciation in Pierinae

The evolutionary biology of Pierinae is described in three separate studies. In Part One, the population biology of the Orange Tip butterfly (Anthocharis card amines) is described. Colonies of this species are localised in riverbank habitats in Co. Durham. Using mark-recapture studies, the population size and movements of individual males were assessed. Studies of individual pre-adult survival indicated that food-plant related mortality and parasitisation were important causes of death. However a key-factor analysis for one population showed that failure of adult females to lay all their eggs was" the most Important factor influencing population size. The adaptiveness of oviposition behaviour was examined by following individual females and by mapping the distribution of eggs upon foodplants. It was shown that many aspects of oviposition choice were best interpreted as chance outcomes of searching behaviour. The observed differences in larval survival on different foodplants, and the constraints placed by time shortage on oviposition were used to construct a simple optimality model. It was shown that oligophagy, the typical condition of A. cardamines populations, leads to increased individual fitness when compared to monophagy. The effect of this stabilising selection on future speciation in Pierinae is discussed. The concept of effective population size is developed for A. cardainines and it is shown that allelic variation at one locus conforms to the predictions of sampling theory in small populations. Part two describes the mating behaviour of several Pierinae, and demonstrates that reproductive isolation in this group is not a result of male behaviour, but of female discrimination of male characters. The agreement of observed behaviour with the predictions of sexual selection is noted, and the likely effects of sexual selection in past and future speciation is discussed. Part three describes the amount of structural gene change that has occurred during the evolution of Pierinae It is shown that little genetic differentiation at such loci occurs prior to or during speciation. Rather, differentiation of species appears to occur at other loci, including those involved in local adaptation, as was described for montane and lowland populations of the Green-Veined White butterfly (Artogeia napi) in Co. Durham. The importance of these results to the wider field of speciation studies is discussed

In-Mold Assembly of Multi-Functional Structures

Combining the recent advances in injection moldable polymer composites with the multi-material molding techniques enable fabrication of multi-functional structures to serve multiple functions (e.g., carry load, support motion, dissipate heat, store energy). Current in-mold assembly methods, however, cannot be simply scaled to create structures with miniature features, as the process conditions and the assembly failure modes change with the feature size. This dissertation identifies and addresses the issues associated with the in-mold assembly of multi-functional structures with miniature components. First, the functional capability of embedding actuators is developed. As a part of this effort, computational modeling methods are developed to assess the functionality of the structure with respect to the material properties, process parameters and the heat source. Using these models, the effective material thermal conductivity required to dissipate the heat generated by the embedded small scale actuator is identified. Also, the influence of the fiber orientation on the heat dissipation performance is characterized. Finally, models for integrated product and process design are presented to ensure the miniature actuator survivability during embedding process. The second functional capability developed as a part of this dissertation is the in-mold assembly of multi-material structures capable of motion and load transfer, such as mechanisms with compliant hinges. The necessary hinge and link design features are identified. The shapes and orientations of these features are analyzed with respect to their functionality, mutual dependencies, and the process cost. The parametric model of the interface design is developed. This model is used to minimize both the final assembly weight and the mold complexity as the process cost measure. Also, to minimize the manufacturing waste and the risk of assembly failure due to unbalanced mold filling, the design optimization of runner systems used in multi-cavity molds for in-mold assembly is developed. The complete optimization model is characterized and formulated. The best method to solve the runner optimization problem is identified. To demonstrate the applicability of the tools developed in this dissertation towards the miniaturization of robotic devices, a case study of a novel miniature air vehicle drive mechanism is presented