Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions

Probabilistic Seismic Loss Analysis for Design of Steel Structures - Optimizing for Multiple-Objective Functions

An optimized seismic performance-based design methodology considering structural and non-structural system performance and seismic losses is considered to design steel structures. Multi-objective optimization methodology is implemented considering various sets of optimization objectives which would take into account minimization of the initial construction cost, associated with the weight of the structural system, and the expected annual loss considering direct economic losses, and a social loss parameter defined as expected annual social loss. A non-dominated sorting genetic algorithm method is implemented for the multi-objective optimization. Achieving the desired confidence levels in meeting performance objectives of interest are set as constraints of the optimization problem. Inelastic time history analysis is used to evaluate structural response under different levels of earthquake hazard to obtain engineering demand parameters. Hazus fragility functions are employed for obtaining the damage probabilities for the structural system and non-structural components. The optimized designs and losses are compared for example steel structures, located in two geographic locations: Central United States and Western United States

Reliability Based Design Optimization of Reinforced Concrete Frames Using Genetic Algorithm

This paper introduces a new framework for reliability based design optimization (RBDO) of the reinforced concrete (RC) frames. This framework is constructed based on the genetic algorithm (GA) and finite element reliability analysis (FERA) to optimize the frame weight by selecting appropriate sections for structural elements under deterministic and probabilistic constraints. Modulus of elasticity of the concrete and steel bar, dead load, live load, and earthquake equivalent load are considered as random variables. Deterministic constraints include the code design requirements that must be satisfied for all the frame elements according to the nominal values of the aforementioned random variables. On the other hand, this framework provides the minimum required reliability index as the probabilistic constraint. The first-order reliability method (FORM) using the Newton-type recursive relationship will be used to compute the reliability index. The maximum inter-story drift is considered as an engineering demand parameter to define the limit-state function in FORM analysis. To implement the proposed framework, a mid-rise five-story RC frame is selected as an example. Based on the analysis results, increasing the minimum reliability index from 6 to 7 causes an 11 % increase in the weight of the selected RC frame as an objective function. So, we can obtain a trade-off between the optimized frame weight and the required reliability index utilizing the developed framework. Furthermore, the high values of the reliability index for the frame demonstrate the conservative nature of code requirements for interstory drift limitations based on the linear static analysis method

Searching for improvement

Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents

Magnetic charged system search for structural optimization

In this paper the Magnetic Charged System Search algorithm is applied to structural optimization. This algorithm uses the Biot-Savar law of electromagnetism to incorporate magnetic forces into the already existing Charged System Search algorithm and thus can be considered as an extension of it. Each search agent exerts magnetic forces on other agents based on the variation of its objective function value during its last movement. This additional force provides some additional information and enhances the performance of the Charged System Search. The efficiency of the Magnetic Charged System Search is examined by application of this algorithm to four structural optimization problems. The results are compared to those of CSS and some of the methods available in the literature

Framework to Explore the Design Space for Design of Tall Buildings

Design of tall buildings is undergoing a resurgence that is driven by a variety of factors – economical growth, scarcity of land in urban areas, high land costs, increased population density, technological advancements and man’s desire to build taller structures. Considerable research work has been done in the last two decades to meet this demand. Computer-based tools that help design engineers explore design alternatives are indispensable in tackling this complex problem. In addition, a framework that finds the near optimal design, adds value to this exploratory work. In this paper, we develop a general framework for the design optimization of buildings using sizing, shape, and topology design variables. Sizing optimization can be carried out using discrete design variables (from a database of available sections) or continuous design variables (cross-sectional dimensions of custom wide flange sections). Similarly, shape optimization can be carried out using either discrete or continuous design variables. And finally, topology optimization can be carried out using boolean design variables. Allowable stress design guidelines are used as constraints along with displacement, inter-story drift, total structural weight, and frequency constraints. The finite element model is made of three-dimensional beam elements. A typical function evaluation involves a linear, static analysis with multiple load cases, a linear, modal analysis to extract the lowest few eigenpairs, and a linear, buckling analysis to find the buckling capacity. An optimization toolbox that contains gradient-based and population-based optimizers, is a part of the framework. Numerical results how that the framework is capable of producing efficient designs effectivel

Structural Design Optimization of Steel Buildings Using GS-USA© Frame3D

abstract: Tall building developments are spreading across the globe at an ever-increasing rate (www.ctbuh.org). In 1982, the number of ‘tall buildings’ in North America was merely 1,701. This number rose to 26,053, in 2006. The global number of buildings, 200m or more in height, has risen from 286 to 602 in the last decade alone. This dissertation concentrates on design optimization of such, about-to-be modular, structures by implementing AISC 2010 design requirements. Along with a discussion on and classification of lateral load resisting systems, a few design optimization cases are also being studied. The design optimization results of full scale three dimensional buildings subject to multiple design criteria including stress, serviceability and dynamic response are discussed. The tool being used for optimization is GS-USA Frame3D© (henceforth referred to as Frame3D). Types of analyses being verified against a strong baseline of Abaqus 6.11-1, are stress analysis, modal analysis and buckling analysis. The provisions in AISC 2010 allows us to bypass the limit state of flexural buckling in compression checks with a satisfactory buckling analysis. This grants us relief from the long and tedious effective length factor computations. Besides all the AISC design checks, an empirical equation to check beams with high shear and flexure is also being enforced. In this study, we present the details of a tool that can be useful in design optimization - finite element modeling, translating AISC 2010 design code requirements into components of the FE and design optimization models. A comparative study of designs based on AISC 2010 and fixed allowable stresses, (regardless of the shape of cross section) is also being carried out.Dissertation/ThesisMasters Thesis Civil Engineering 201

Identification of flexible structures for robust control

Documentation is provided of the authors' experience with modeling and identification of an experimental flexible structure for the purpose of control design, with the primary aim being to motivate some important research directions in this area. A multi-input/multi-output (MIMO) model of the structure is generated using the finite element method. This model is inadequate for control design, due to its large variation from the experimental data. Chebyshev polynomials are employed to fit the data with single-input/multi-output (SIMO) transfer function models. Combining these SIMO models leads to a MIMO model with more modes than the original finite element model. To find a physically motivated model, an ad hoc model reduction technique which uses a priori knowledge of the structure is developed. The ad hoc approach is compared with balanced realization model reduction to determine its benefits. Descriptions of the errors between the model and experimental data are formulated for robust control design. Plots of select transfer function models and experimental data are included

Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers