Harmony Search Optimization and Damage Tolerance of Structural Systems

In this thesis, multiple structural systems are investigated by utilizing the Harmony Search optimization algorithm for least weight optimization. An analytical overview of structural optimization, matrix analysis, damage tolerance, steel connections and structural reliability analysis and methodology are presented. To support the methodology, three example problems have been provided. The first example demonstrates damage tolerant optimization of a simple truss structure. The second example focuses on the harmony search optimization of a more complex steel frame along with damage tolerant optimization. The third example provides a brief connection between damage tolerance and structural reliability. The findings show that the harmony search algorithm can be a powerful tool when optimizing structural systems. They also show the power of linking optimization, damage tolerance and structural reliability when considering the design of a structure

Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges

This paper aims to find sustainable designs of post-tensioned concrete box-girder pedestrian bridges. A hybrid harmony search algorithm combining threshold optimization is used to find the geometry and the materials for which the sum of the costs or the emissions are the lowest, yet satisfying the requirements for structural safety and durability. An experimental design method was used to adjust the algorithm parameters. The parametric study was applied to three-span deck bridges ranging from 90 m to 130 m. The findings indicated that both objectives lead to similar cost results. However, the variables presented some differences. Such deviations suggested greater depths, more strands and a lower concrete strength for CO2 target functions. Carbonation captured less than 1% of the deck emissions over 100 years. This methodology leads to a precise analysis of the practical rules to achieve an environmental design approach.This research was financially supported by the Spanish Ministry of Science and Innovation (Research Project BIA2011-23602).García Segura, T.; Yepes Piqueras, V.; Alcalá González, J.; Pérez López, E. (2015). Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges. Engineering Structures. 92:112-122. https://doi.org/10.1016/j.engstruct.2015.03.015S1121229

Metaheuristic optimization of reinforced concrete footings

The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings

Member SIzing Optimization of Steel Space Trusses Designed based on AISC 360-10 using Symbiotic Algorithms Search

Producing an economical truss structure that satisfies the governing design codes is a desire of structural engineers and owners. Engineers strive to meet these design requirements traditionally by trail-and-error selecting the member sizes based on the engineers’ intuition and judgment. This design method, however, cannot guarantee the realization of an optimal design, especially for large and complex structures. Thus, a systematic approach of optimization is needed to achieve an optimal design of truss structures. In the last two decades, many researchers have developed and applied various ‘metaheuristic’ optimization methods (i.e. a class of stochastic methods that simulates different natural phenomena to obtain a nearly optimal solution) to design of truss structures, such as the genetic algorithm, particle swarm optimization, ant colony optimization, big bang-big crunch optimization, and harmony search algorithm. Among many newly developed metaheuristic algorithms, an algorithm called Symbiotic Organisms Search (SOS) has drawn our attention because of its excellent performance and parameter-less nature. The SOS algorithm has been successfully used to solve different optimization problems in engineering, including truss design optimization problems. However, the truss problems considered in the previous studies are relatively small. This paper presents applications of the SOS algorithm to optimize member sizing of relatively large steel space trusses, that is, (1) a 120-bar dome shaped truss and (2) a 160-bar pyramid shaped truss. The structural analyses are carried out using the standard finite element method. The strength design of steel members follows the ‘Specification for Structural Steel Buildings’, AISC 360-10. The profile of the members is circular hollow structural sections selected from a set of the American Institute of Steel Construction standard profiles. The design results are then compared to those obtained using other metaheuristic methods, namely the particle swarm optimization, differential evolution, and teaching-learning-based optimization. The comparison shows the superior performance of the SOS in optimizing member sizes of large-scale truss structures

Multi-objective design of post-tensioned concrete road bridges using artificial neural networks

[EN] In order to minimize the total expected cost, bridges have to be designed for safety and durability. This paper considers the cost, the safety, and the corrosion initiation time to design post-tensioned concrete box-girder road bridges. The deck is modeled by finite elements based on problem variables such as the cross-section geometry, the concrete grade, and the reinforcing and post-tensioning steel. An integrated multi-objective harmony search with artificial neural networks (ANNs) is proposed to reduce the high computing time required for the finite-element analysis and the increment in conflicting objectives. ANNs are trained through the results of previous bridge performance evaluations. Then, ANNs are used to evaluate the constraints and provide a direction towards the Pareto front. Finally, exact methods actualize and improve the Pareto set. The results show that the harmony search parameters should be progressively changed in a diversification-intensification strategy. This methodology provides trade-off solutions that are the cheapest ones for the safety and durability levels considered. Therefore, it is possible to choose an alternative that can be easily adjusted to each need.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (BRIDLIFE Project: BIA2014-56574-R) and the Research and Development Support Program of Universitat Politecnica de Valencia (PAID-02-15).García-Segura, T.; Yepes, V.; Frangopol, D. (2017). Multi-objective design of post-tensioned concrete road bridges using artificial neural networks. Structural and Multidisciplinary Optimization. 56(1):139-150. doi:10.1007/s00158-017-1653-0S139150561Alberdi R, Khandelwal K (2015) Comparison of robustness of metaheuristic algorithms for steel frame optimization. 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Orfeo, Osmin and Otello: towards a theory of opera analysis

Three diverse operatic selections are discussed in light of a new approach to opera analysi

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era