Earthquake-resistant limit-state design for buildings

Static solution of crack propagation problems in Peridynamics

The ever-increasing demands for resource-saving, engineering technology progress, and environmental protection stimulate the progress of the progressive design method. As an excellent promising design method for dealing with the inevitable uncertainty factors, reliability-based design optimization (RBDO) is capable of offering reliable and robust results and minimizing the cost under the prescribed uncertainty level, which can provide a trade-off between economy and safety. However, the primary challenges, including global convergence capacity and complicated mixed design variable type, hinder the wider application of RBDO. This study presents a comprehensive work on the application of ten popular and recent metaheuristic algorithms of five engineering problems. Furthermore, we focus on the RBDO equip with metaheuristic algorithms about its global convergence, robustness, accuracy, and computational speed. This paper also presents the major difference of convergence property between metaheuristic algorithms and gradient algorithms. The detailed statement of this study presents the state-of-the-art in RBDO to demonstrate its crucial technologies and great challenges, as well as the beneficial future development direction.

A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems

The majority of historical and heritage structures around the world consist of unreinforced masonry walls. A masonry structure is composed of masonry units, such as brick or marble blocks, with or without a joint filling material, such as mortar. A masonry with a joint material is usually made of two different materials (i.e. masonry units and mortar), representing a non-homogeneous and anisotropic structural component. In other words, masonry is a discontinuous structural component whose deformations and failure mechanism are governed by its blocky behavior. Some ancient masonry structures, such as ancient columns and colonnades, are constructed without any form of joint material between the individual blocks. Therefore, the isotropic elastic continuum-based models are not suitable for the simulation of the real nonlinear behavior of masonry walls under applied load.

Numerical Modeling of Historic Masonry Structures

Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization

Differential evolution (DE) is a population-based metaheuristic algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such algorithms make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. DE is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems. In the field of structural engineering, most real-world optimization problems are associated with one or several constraints. Constrained optimization problems are often challenging to solve due to their complexity and high nonlinearity. In this work we examine the performance of several DE variants, namely the traditional DE, the composite DE (CODE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (JDE and SADE), for handling constrained structural optimization problems associated with truss structures. The performance of each DE variant is evaluated by using five well-known benchmark structures in 2D and 3D. The evaluation is done on the basis of final optimum result and the rate of convergence. Valuable conclusions are obtained from the statistical analysis which can help a structural engineer in practice to choose the suitable algorithm for these kind of problems.

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A comparative study of differential evolution variants in constrained structural optimization

Stochastic failure analysis of structures with softening materials

Structural damage identification is a scientific field that has attracted a lot of interest in the scientific community during the recent years. There have been many studies intending to find a reliable method to identify damage in structural elements both in location and extent. Most damage identification methods are based on the changes of dynamic characteristics and static responses, but the incompleteness of the test data is a great obstacle for both. In this paper, a structural damage identification method based on the finite element model updating is proposed, in order to provide the location and the extent of structural damage using incomplete modal data of a damaged structure. The structural damage identification problem is treated as an unconstrained optimization problem which is solved using the differential evolution search algorithm. The objective function used in the optimization process is based on a combination of two modal correlation criteria, providing a measure of consistency and correlation between estimations of mode shape vectors. The performance and robustness of the proposed approach are evaluated with two numerical examples: a simply supported concrete beam and a concrete frame under several damage scenarios. The obtained results exhibit high efficiency of the proposed approach for accurately identifying the location and extent of structural damage.

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Manolis Georgioudakis

Papers

A comparative study of differential evolution variants in constrained structural optimization

Stochastic failure analysis of structures with softening materials

A Combined Modal Correlation Criterion for Structural Damage Identification with Noisy Modal Data

On the Performance of Differential Evolution Variants in Constrained Structural Optimization

Probabilistic shape design optimization of structural components under fatigue