Vagelis Plevris

A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization

:u2002The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO-based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient-based quasi-Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms.

Large scale structural optimization: Computational methods and optimization algorithms

SummaryThe objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programming and evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particular emphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimization procedure. Modified versions of both genetic algorithms and evolution strategies combined with mathematical programming methods to form hybrid methodologies are also tested and compared and proved particularly promising. Furthermore, the structural analysis phase is replaced by a neural network prediction for the computation of the necessary data required by the evolutionary algorithms. Advanced domain decomposition techniques particularly tailored for parallel solution of large-scale sensitivity analysis problems are also implemented. The efficiency of a rigorous approach for treating seismic loading is investigated and compared with a simplified dynamic analysis adopted by seismic codes in the framework of finding the optimum design of structures with minimum weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. The numerical tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale optimization problems.

MULTI-OBJECTIVE OPTIMIZATION OF SKELETAL STRUCTURES UNDER STATIC AND SEISMIC LOADING CONDITIONS

This chapter presents a evolution strategies approach for multiobjective design optimization of structural problems such as space frames and multi-layered space trusses under static and seismic loading conditions. A rigorous approach and a simplified one with respect to the loading condition are implemented for finding optimal design of a structure under multiple objectives.

Neurocomputing strategies for solving reliability‐robust design optimization problems

Purpose – This paper, by taking randomness and uncertainty of structural systems into account aims to implement a combined reliability‐based robust design optimization (RRDO) formulation. The random variables to be considered include the cross section dimensions, modulus of elasticity, yield stress, and applied loading. The RRDO problem is to be formulated as a multi‐objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized.Design/methodology/approach – The solution of the optimization problem is performed with the non‐dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis required is carried out with the Monte Carlo simulation method. Despite the computational advances, the solution of a RRDO problem for real‐world structures is extremely computationally demanding and for this reason neurocomputing estimations are implemented.Findings – The obtained estimates w...

A Swarm Intelligence Approach for Emergency Infrastructure Inspection Scheduling

Natural hazards such as earthquakes, floods and tornadoes can cause extensive failure of critical infrastructures including bridges, water and sewer systems, gas and electricity supply systems, and hospital and communication systems. Following a natural hazard, the condition of structures and critical infrastructures must be assessed and damages have to be identified; inspections are therefore necessary since failure to rapidly inspect and subsequently repair infrastructure elements will delay search and rescue operations and relief efforts. The objective of this work is scheduling structure and infrastructure inspection crews following an earthquake in densely populated metropolitan areas. A model is proposed and a decision support system is designed to aid local authorities in optimally assigning inspectors to critical infrastructures. A combined Particle Swarm - Ant Colony Optimization based framework is developed which proves an instance of a successful application of the philosophy of bounded rationality and decentralized decision-making for solving global optimization problems.

OPTIMUM DESIGN OF SPACE FRAMES UNDER SEISMIC LOADING

The objective of this paper is to perform structural optimization under seismic loading. Combinatorial optimization methods and in particular algorithms based on Evolution Strategies are implemented for the solution of large-scale structural optimization problems under seismic loading. In this work the efficiency of a rigorous approach in treating dynamic loading is investigated and compared with a simplified dynamic analysis in the framework of finding the optimum design of structures with minimum weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. This approach is compared with an approximate design approach based on simplifications adopted by the seismic codes. The results obtained for a characteristic test problem indicate a substantial improvement in the final design when the proposed optimization procedure is implemented.

OPTIMUM DESIGN OF CANTILEVER WALLS RETAINING LINEAR ELASTIC BACKFILL BY USE OF GENETIC ALGORITHM

Retaining walls are used in many geotechnical engineering applications, e.g. sup- porting deep excavations, bridge abutments, harbor-quay walls, anchored retaining walls, etc. Although they are generally simple structures, their static and dynamic interaction with the supporting and/or retained soil is a subject of ongoing research. Apart from this, seismic de- sign of retaining walls is primarily based on rules of thumb and the designers experience, in order to set the initial dimensions and make the necessary checks to comply with the design codes. In addition, the calculation of the seismic earth pressures is done in a rather simplistic way which may lead to either conservative or unsafe designs. In the present study, after a comprehensive literature review, optimum design is performed for cantilever walls retaining soil layers of two different heights, using numerical two-dimensional simulations and a genet- ic algorithm. Numerical simulations are performed using the finite element code ABAQUS (1) whereas for optimization purposes, the genetic algorithm provided with MATLAB (2) is uti- lized. For the calculation of the seismic earth pressures, linear elastic soil, retaining wall stem and wall foundation are assumed. The optimization procedure involves four design vari- ables that have to do with the wall geometry, while the soil and wall material parameters and the frequency range of interest are kept fixed. Structural and geotechnical constraints as well as upper and lower bounds for the design variables are imposed to ensure technical feasibil- ity of the solutions. The results on the optimum solutions are presented and comparisons are made with the corresponding results according to conventional seismic design methods. The numerical results of the study provide a clear indication of the direct dynamic interaction be- tween the retaining wall and the surrounding soil, whereas the complexity of the optimization problem itself is evident. This justifies the necessity for a more elaborate consideration of the optimum design of retaining walls, especially if material and geometric non-linearities are taken into account.

DESIGN OF RC SECTIONS IN THE ULTIMATE LIMIT STATE UNDER BENDING AND AXIAL FORCE ACCORDING TO EC2

Abstract. In the imminent future the design of concrete structures in Europe will be governed by the application of Eurocode 2 (EC2). In particular, EC2 – Part 1-1 [1] deals with the general rules and rules for concrete buildings. An important aspect of the design is specifying the necessary tensile (and compressive, if needed) steel reinforcement required for a Reinforced Concrete (RC) section, in order to ensure that the RC member will be able to resist the design loads.