Genetic Algorithm Approach for Modeling the Structural Global Stiffness (original) (raw)

A genetic programming approach for stiffness estimation

2007

Genetic Programming (GP) is an automatic method for generating computer programs inspired by analogies with the evolution theory described by Darwin. This paper introduces the fundamental elements of Genetic Programming and demonstrates its effectiveness on an illustrative engineering problem: the stiffness problem.

Application of Evolutionary Optimization in Structural Engineering

IFIP Advances in Information and Communication Technology, 2009

Practical optimization methods including genetic algorithms are introduced, based on evolutionary computing or soft computing. Several application examples are presented to demonstrate and discuss the efficiency and applicability of the described methods.

Structural Topology Optimization in Linear and Nonlinear Elasticity Using Genetic Algorithms

Volume 1: 21st Design Automation Conference, 1995

In this paper, structural topology optimization is addressed through Genetic Algorithms. A set of designs is evolved following the Darwinian survival-of-fittest principle. The standard crossover and mutation operators are tailored for the needs of 2D topology optimization. The genetic algorithm based on these operators is experimented on plane stress problems of cantilever plates: the goal is to optimize the weight of the structure under displacement constraints. The main advantage of this approach is that it can both find out alternative optimal solutions, as experimentally demonstrated on a problem with multiple solutions, and handle different kinds of mechanical model: some results in elasticity with large displacements are presented. In that case, the nonlinear geometrical effects of the model lead to non viable solutions, unless some constraints are imposed on the stress field.

Contractor Report 4597 Strain Gage Selection in Loads Equations Using a Genetic Algorithm

1996

Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a compar...

A Genetic Evolution Algorithm for Structural Optimization

The application of genetic algorithm-based methodology for the structural design is presented in this study. The genetic algorithm is used to design prestressed concrete beams (PCB). The target objective in this method is to obtain set of optimal geometrical dimensions of symmetrical I-beam cross section. Additionally, the amount of pre-stressing steel is optimized. Post-tensioned prestressed beam with a single duct of parabolic shape is considered in the application. Several parameters are studied including the effect of the span length considering different loading cases. The performance constraints are adopted according to the ACI 318/95 Building Code provisions [1]; including the flexural stresses, the ultimate moment capacity of the section with respect to cracking moment, the maximum crack width, the immediate deflection and the long term deflection in addition to the side constraints. The results are presented and compared; several design charts are developed and presented. The present study showed the promising capabilities of the genetic algorithm in optimal designs, and showed the practicability of the genetic algorithm for different structural optimization problems.

On the Use of Genetic Algorithms to Assess the Seismic Resistance of Planar Frame Structures

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015), 2017

Limit analysis represents a widely adopted strategy for the assessment of the bearing capacity and collapse mechanism of frame structures. Within this framework, in this paper an original strategy for the effective evaluation of the ultimate resistance and the corresponding failure mechanism of planar frame structures subjected to lateral loads is presented. The methodology is based on the generation of the elementary collapse mechanisms to be combined following a collapse load factor minimization criterion. When a large number of possible combinations has to be investigated a prompt procedure able to quickly converge to the actual collapse load factor and to jump out of local minima is needed. For this reason a procedure based on genetic algorithms is here adopted and a dedicated user-friendly software was developed in the NetLogo programming environment. Validations of the proposed procedure with respect to nonlinear static analysis are reported together with some significant sensitivity analyses with respect to load distribution parameters. The results demonstrate the reliability of the procedure and can provide useful information also in view of seismic design optimization strategies.

An Adaptive Correction Function for Structural Optimization with Genetic Algorithms

III European Conference on Computational Mechanics

This paper presents a new self adaptive correction function for the optimisation of size, geometry and topology of space truss structures using the Genetic Algorithm (GA) method, applied to both continuous and discrete design variables. This function guarantees the diversity of the population at the early stages of the optimisation process. In addition, this function moves the final solution to the feasible region. The self adaptive correction function proposed is the product of two independent functions. The first is an individual correction function that corresponds to the increase of the objective function that will be necessary to move an unfeasible individual into the feasible region. The second is a penalty function that increases or decreases the imposed correction, achieving this based on the feasibility or infeasibility of the population members during recent generations. The application of this self adaptive correction function to structural optimization was made using a very simple GA algorithm [1] with binary codification, standard crossover, mutation and elitism. The self adaptive correction function proposed was implemented in the optimal design system DISSENY [2]. Numerical experiments were carried out with different structural optimization problems (i.e. cross-sectional size, topology and shape optimization of 3-D trusses), with continuous and/or discrete variables, stress, displacement, slenderness and buckling constraints. The obtained results demonstrate that this self adaptive correction function is effective and robust, relieving the user from the burden of having to determinate the penalty parameters for each new problem. The results produced using this self adaptive function are equal or better than those produced using penalty functions.

Structural Parameter Estimation Using Modal Responses and Utilizing Genetic Algorithm

Genetic Algorithms (GA) are powerful tools for solving large and complicated optimization problems. Objective functions used in parameter estimation (PE) are commonly nonlinear due to available measurements at a limited number of degrees of freedom for a structure. Sparse measurements create a fairly complicated objective function surface that requires a robust algorithm to find its global minimum without converging to a local minimum or diverging. This paper examines the potential of genetic algorithms to find the global minimum associated with modal stiffness and flexibility based objective functions used in PE. A finite element model of a six-story two-parameter shear building is used for this study. Using three-dimensional plots of the two objective functions, few cases with complex surface and several local minima are selected. FlexGA TM genetic algorithm software is then used to estimate parameters of the model. The overall performance of GA to find the global minimum for these cases is compared with gradient-based optimization methods (commonly referred to as hill climbing, HC). It is concluded that GA's performance in locating the basin of the global minimum is superior to HC. It is further recommended that when GA converges, switching to HC yields more accurate parameter estimates.

Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming

Journal of Constructional Steel Research, 2009

This paper proposes an alternative approach for predicting the flexural resistance and initial rotational stiffness of semi-rigid joints in steel structures using Linear Genetic Programming (LGP). Three types of steel beam-column joints i.e. endplates, welded, and end bolted joints with angles are investigated. Models are constructed by utilizing test results available in the literature. The accuracy of the proposed models is verified by comparing the outcomes to the experimental results.

Optimization of stiffened plates for steel bridges based on Eurocode 3 Part 1-5 using genetic algorithms

Steel Construction, 2011

Bridges are an important part of any country's infrastructure. Welded stiffened plates are widely used as box girders for bridges. This article presents a design optimization problem for a single steel box with stiffeners on webs. The design is intended to be formulated based only on the current version of Eurocode 3 Part 1-5 "Plated structural elements". Eurocode 3 will definitely be introduced in all the Member States of the European Union. This optimization resource represents an engineering compromise, not only for society, but also for avoiding negative impacts on the environment.