Design Optimization Of Steel Frames Research Articles

A practical tool for structural design optimization of steel frames: development and application for parametric analysis

A practical tool for structural design optimization of steel frames: development and application for parametric analysis

Optimum design of steel frames with sizing and orientation design variables using a reformulated evolution strategy

Sizing optimization of steel frame structures has been traditionally conducted based on fixed orientations of structural members. However, it seems more expedient to search for an optimum design where orientations of column members are considered as design variables together with sizing of all members for the minimum weight design of a steel frame. In order to solve the resulting optimization problem effectively, a reformulated evolution strategy (ES) algorithm is proposed and employed in the present study. The numerical investigations are carried out using three test problems regarding the optimum design of steel space frames. In each test problem, a frame is first designed for the minimum weight considering sizing design variables only, where initial orientations of the column members are kept unchanged. Next, sizing and orientation design variables are considered simultaneously for minimizing the weight of the same frame. The results obtained in the foregoing two cases are compared and the associated optimal layouts for the column orientations are depicted. The study reveals that the optimal selection of column orientations has a significant influence on the minimum weight design of steel frames.

Enhanced Dandelion Optimizer for Optimum Design of Steel Frames

Enhanced Dandelion Optimizer for Optimum Design of Steel Frames

Pproximated Method of Nonlinear Geometric Analysis Applied to Optimum Design of Steel Frames

pproximated Method of Nonlinear Geometric Analysis Applied to Optimum Design of Steel Frames

An experience based artificial neural network in the design optimization of steel frames

The design of steel frames is an iterative process relying on the experience and decisions of the designer to achieve economical and safe designs. With recent advances in artificial intelligence, particularly, artificial neural networks, it became possible to train such networks to simulate an experienced designer. Thus, the aim of this contribution is to investigate the possibility of artificial neural networks gaining design experience and using such experience in predicting adequate and economical designs. To achieve this aim, an adaptive harmony search algorithm is used to obtain the optimum structural design of two-dimensional steel frames. Those designs are, in a sense, considered an experience, which are then used in training artificial neural networks. The trained networks are finally used in predicting the optimum solution of new problem variants. In total, 18684 samples based on 3114 two-dimensional frames were used to train multiple feed forward artificial neural networks, with a training, validation and testing ratios of 70%, 15% and 15%, respectively. The trained networks’ performance was verified, and used in design predictions on interpolated and extrapolated cases. Considering the designs suggested by the artificial neural networks, 99% were adequate in the case of network verification. Furthermore, 97% and 93% of designs were adequate in the case of interpolation and extrapolation. Thus, artificial neural networks are able to learn from the design experience and provide good approximations for designs of variants even outside the training set. Such findings encourage the development of artificial intelligence assisted design systems that are capable of suggesting optimum or near-optimum designs for two-dimensional frames. Also, it could encourage further research for three-dimensional steel frames and more complex steel structure systems.

Sequential sampling approach to energy‐based multi‐objective design optimization of steel frames with correlated random parameters

AbstractThis work presents a novel sequential sampling approach to the multi‐objective reliability‐based design optimization of moment‐resisting steel frames subjected to earthquake excitation. The optimization problem is formulated with two objective functions, namely, the total mass and the energy dissipated by beam members of the frame, and subject to uncorrelated probabilistic constraints on dynamic responses under the effects of correlated random parameters of floor masses, external loads, and material properties. The dynamic responses for a small number of designs are found by nonlinear response history analysis and further approximated by Gaussian process (GP) models to mitigate the computational burden during the optimization process. Approximate solutions sorted among existing candidate solutions are updated after each optimization iteration using discrete random local and global searches. The GP models are also refined after each optimization iteration by specifying new sampling points that lie on the Pareto front of a bi‐objective deterministic maximization problem formulated for the improvement in the current approximate solutions and the feasibility of the new sampling points. As demonstrated in a test problem, the new sampling points tend to distribute in the neighborhood of the exact solutions, thereby underpinning a quick termination as well as the robustness of the proposed method. Optimization results from the test problem and a design example show that good approximate solutions are always obtained as the solution quality converges.

Optimum Design of Steel Frames Using Different Variants of Differential Evolution Algorithm

Optimum Design of Steel Frames Using Different Variants of Differential Evolution Algorithm

Optimum design of cold-formed steel frames via five novel nature-inspired metaheuristic algorithms under consideration of seismic loading

In this paper, an unbiased comparative assessment scheme for algorithmic performances of five novel nature-inspired metaheuristic algorithms in design optimization of steel frames made out of cold-formed steel sections under consideration of seismic loading is presented. These contemporary algorithms are so-called tree seed, squirrel search, water strider, grey wolf, and brain storm optimization. The functionality of the proposed algorithms is appraised with respect to design precisions in both portal and space cold-formed steel frames formulated according to the design provisions implemented by AISI-LRFD (American Iron and Steel Institute-Load and Resistance Factor Design). The cross-sectional dimensions of steel profiles, which are selected from available set of cold-formed thin-walled single-C sections, are treated as design variables in the optimization process in order to minimize the structural weight of the frames. In addition to specification constraint requirements, lateral and vertical displacement restrictions of the structural elements required for stability of the frames are also taken into account. Design optimization algorithms necessitate the structural response of cold-formed steel frames under load combinations including seismic loading effects which is accomplished by utilizing the open application programming interface (OAPI) mastery of MATLAB with SAP2000. The design optimization of cold-formed steel frames that is a discrete nonlinear programming problem reveal the robustness and applicability of proposed contemporary nature-inspired metaheuristic algorithms in real-sized complex structural optimization problems.

Seismic Design Optimization of Steel Frames with Steel Shear Wall System Using Modified Dolphin Algorithm

The first step in any successful design of a structure is selecting the exact and accurate designing method. Designing a structure most economically (i.e. materials such as steel and concrete) by satisfying seismic and gravity criteria based on codes of practice is a complicated procedure that requires not only technical engineering knowledge but also years of experience. Thus, this paper presents an efficient and reliable approach to obtain the optimum design in steel frames with shear walls as the main seismic bearing component. The main objective is to use a modified dolphin echolocation algorithm with a multi-variability ability approach in achieving the optimized results. This approach represents the best positioning option for steel shear walls in elevations, spans, and dimensions of the elements. According to modifications applied to the algorithm, the computational time decreases, and the results are highly accurate. The designed structures with the minimum amount of steel satisfy all the requirements in the seismic design and steel structures design code, and the results of this work are entirely efficient. This efficiency is illustrated by three numerical examples. The results illustrated the superiority of the metaheuristic approach over the standard procedure. Besides, considering the weight of shear wall for the first time led to the 10% reduction of the weight of the structure.

Reliability-based design optimization of steel frames using direct design

This paper presents an effective method for reliability-based design optimization (RBDO) of steel frames by combining direct design using nonlinear inelastic analysis, an improved importance sampling technique for structural failure probability analysis, and an improved differential evolution (DE) algorithm for optimization. The nonlinear inelastic analysis using the beam-column approach is used to capture the second-order effects and the inelastic behavior of structures. An improved importance sampling technique based on nonlinear inelastic analysis of the structure is employed that significantly reduces the number of structural analyses required for calculating the structural failure probability. An improved DE algorithm, which can effectively eliminate the redundant structural analyses for objective function evaluation, is utilized. A six-story space frame is studied to demonstrate the computational efficiency of the proposed method.

Discrete Sizing of Steel Frames Using Adaptive Dimensional Search Algorithm

Adaptive dimensional search (ADS) algorithm is a recently proposed metaheuristic optimization technique for discrete structural optimization problems. In this study, discrete sizing optimization problem of steel frames is tackled using the ADS algorithm. An important feature of the algorithm is that it does not use any metaphor as an underlying principle for its implementation. Instead, the algorithm employs an efficient performance-oriented methodology at each iteration for convergence to the optimum or a near optimum solution. The performance of the ADS is investigated through optimum design of five real-size steel frame structures and the results are compared versus several contemporary metaheuristic techniques. The comparison of the obtained numerical results with those of available designs in the literature reveals the reliability and efficiency of the ADS in optimum design of steel frames.

An efficient discrete optimization algorithm for performance-based design optimization of steel frames

Performance-based design optimization of steel frames, with element sections selected from standard sections, is a computationally intensive task. In this article, an efficient discrete optimization algorithm is proposed for performance-based design optimization of steel frames. The computational efficiency is improved by searching in a sensible manner, guided by the deformation information of structural elements. To include all standard sections in the design space, the cross-sectional area ( Area) and moment of inertia ( Ix) are selected as the design variables. Based on different relationships between Area and Ix, a twofold strategy is put forward, which includes a quick exploration and an elaborate exploitation. For comparison, a similar algorithm is also proposed, using Area as the only design variable. A fixed relationship between Area and other sectional properties is used. Two numerical examples are presented to minimize the structural weight while satisfying performance constraints. The results indicate that the proposed discrete algorithm can achieve lighter structural designs than the area-only algorithm. Furthermore, the convergence history proves that a high computational efficiency can be realized by using the proposed algorithm.

Optimum design of steel braced frames considering dynamic soil-structure interaction

Recent studies on design optimization of steel frames considering soil-structure interaction have focused on static loading scenarios, and limited work has been conducted to address the design optimization under dynamic soil-structure interaction. In the present work, first, a platform is developed to perform optimization of steel frames under seismic loading considering dynamic soil-structure interaction (SSI) in order to quantify the effects of earthquake records on the optimum design. Next, verification of the adopted modeling technique is conducted using comparison of the results with the reference solution counterparts in frequency domain. For time history analyses, records from past events are selected and scaled to a target spectrum using simple scaling approach as well as spectrum matching technique. For sizing of the steel frames, a recently developed metaheuristic optimization algorithm, namely exponential big bang-big crunch optimization method, is employed. To alleviate the computational burden of the optimization process, the metaheuristic algorithm is integrated with the so-called upper bound strategy. Effects of factors such as the building height, presence of soil domain, and the utilized ground motion scaling technique are investigated and discussed. The numerical results obtained based on 5- and 10-story steel braced frame dual systems reveal that, although dynamic SSI reduced the seismic demands to some extent, given the final design pertains to different load combinations, the optimum weight difference is not considerable.

A Comparison between different techniques for optimum design of steel frames subjected to blast

Abstract According to the conditions of today's world, design of resistant structures against blast loading is an important subject that requires special attention. Thus, given the benefits of optimization in engineering, development and assessment of optimization methods for optimum design of structures against blast is of great importance. In this research, the optimum design of steel frame structures against blast loading is investigated. For this purpose first an optimization methodology is proposed. In the proposed method the structural analysis is performed using nonlinear explicit finite element analysis. Based on the proposed method a framework is developed and three numerical examples are investigated using different numerical optimization techniques. Results of this study show that by using nonlinear explicit FE analysis as the structural analysis method and NLPQLP optimization technique as the optimization method, the current optimization problem can be performed effectively, because the procedure is relatively accurate and computationally inexpensive.

An efficient coupled numerical method for reliability-based design optimization of steel frames

The paper proposes an efficient coupled numerical method for reliability-based design optimization (RBDO) of steel frames. In this RBDO problem, the objective function is to minimize the weight of the whole steel frame. Design variables are cross sectional areas of beams and columns which are considered as discrete variables and chosen from the sets of wide-flange shape steel sections provided by the American Institute of Steel Construction (AISC). Random variables relate to the material properties and applied loads. Probabilistic constraints of ultimate load limits and serviceability limits are defined following the specifications for structural steel buildings by AISC. For analyzing the behavior of steel frames, the finite element method for frame structures is utilized. For searching the optimal solution of RBDO problems, an efficient coupled numerical method by combining the Single-Loop Deterministic Method (SLDM) and the Improved Differential Evolution (IDE) is proposed to give the method called SLDM-IDE. In the SLDM-IDE, all of the probabilistic constraints are converted to the approximate deterministic constraints. This helps transform the RBDO problem into an approximate deterministic optimization problem which can be solved by standard optimization algorithms. This helps reduce significantly the computational cost for solving the original RBDO problem. Three numerical examples are conducted, and obtained results are compared with those of previous publications to demonstrate the robustness and efficiency of the proposed approach.

Multi-objective seismic design optimization of steel frames by a chaotic meta-heuristic algorithm

In this study, multi-objective optimization is applied to implement performance-based design of steel moment-resisting frame (SMRF) structures. In order to efficiently achieve this purpose, a chaotic multi-objective firefly algorithm (CMOFA) is proposed to find the Pareto optimal front for the multi-objective performance-based optimum design (MO-PBOD) problem of SMRFs. The structural weight and the maximum inter-story drift at performance levels are taken as the conflicting objective functions of the MO-PBOD problem which should be optimized simultaneously subject to serviceability and ultimate limit-state constraints. In order to illustrate the efficiency of the proposed CMOFA meta-heuristic, two benchmark truss examples and three MO-PBOD examples of SMRFs are presented. The numerical results demonstrate the better computational performance of the proposed CMOFA meta-heuristic in comparison with some existing multi-objective algorithms.

Design optimization of steel frames using an enhanced firefly algorithm

Mathematical modelling of real-world-sized steel frames under the Load and Resistance Factor Design–American Institute of Steel Construction (LRFD-AISC) steel design code provisions, where the steel profiles for the members are selected from a table of steel sections, turns out to be a discrete nonlinear programming problem. Finding the optimum design of such design optimization problems using classical optimization techniques is difficult. Metaheuristic algorithms provide an alternative way of solving such problems. The firefly algorithm (FFA) belongs to the swarm intelligence group of metaheuristics. The standard FFA has the drawback of being caught up in local optima in large-sized steel frame design problems. This study attempts to enhance the performance of the FFA by suggesting two new expressions for the attractiveness and randomness parameters of the algorithm. Two real-world-sized design examples are designed by the enhanced FFA and its performance is compared with standard FFA as well as with particle swarm and cuckoo search algorithms.

Optimum design of steel frames with semi-rigid connections and composite beams

In this paper, an optimization process using Genetic Algorithm (GA) that mimics biological processes is presented for optimum design of planar frames with semi-rigid connections by selecting suitable standard sections from a specified list taken from American Institute of Steel Construction (AISC). The stress constraints as indicated in AISC-LRFD (American Institute of Steel Construction - Load and Resistance Factor Design), maximum lateral displacement constraints and geometric constraints are considered for optimum design. Two different planar frames with semi-rigid connections taken from the literature are carried out first without considering concrete slab effects in finite element analyses and the results are compared with the ones available in literature. The same optimization procedures are then repeated for full and semi rigid planar frames with composite (steel and concrete) beams. A program is developed in MATLAB for all optimization procedures. Results obtained from this study proved that consideration of the contribution of the concrete on the behavior of the floor beams provides lighter planar frames.

Wind-moment design of semi-rigid un-braced steel frames using cruciform column (CCUB) section

The design of un-braced frames using wind-moment method (WMM) with cruciform column (CCUB) section as vertical member is presented here. Steel frames on a regular grid with approximately equal column spacings in the y-y and z-z direction using UC/UB sections has resulted in minor axis controlled the design, which leads to a significant loss in performance. The use of CCUB sections with equal Iy and Iz warrants an equal behaviour in both directions whilst ensuring that both the major and minor axis beam to column connections remain straightforward. The study has been conducted on 2-bay and 4-bay plane frames with 2, 4, 6 and 8 storey heights, and two different load cases are considered: minimum wind load in conjunction with maximum gravity load and vice versa. Structural design optimization of steel frames was conducted on the selection of steel sections for beam and column. The selection was carried out in such a way that the steel frame had the minimum weight while the performance of the structure was within the limitations described by BS EN 1993-1-1: 2005. Significant column weight savings (between 17–66%) was achieved by using CCUB section in the design, as compared to conventional UC sections.

Performance-Based Optimum Design of Steel Frames by an Improved Quantum Particle Swarm Optimization

The main aim of this work is to present a methodology for performance-based optimum seismic design of moment resisting steel frame structures. In the present study, an improved quantum particle swarm optimization (IQPSO) metaheuristic algorithm is proposed to implement performance-based optimum design (PBOD) process. During the optimization process, QPSO and IQPSO algorithms minimize the structural weight subject to performance constraints on inter-story drift ratios based on FEMA-356 provisions at the immediate occupancy (IO), life safety (LS) and collapse prevention (CP) performance levels. Nonlinear pushover analysis is conducted to compute the necessary structural responses during the PBOD process. Two numerical examples are presented to illustrate the efficiency of the presented methodology. The numerical results demonstrate the superiority of the proposed IQPSO to the classical QPSO algorithm.