Automated Graph Theoretic Force Method and its Application in Optimal Design of Frame Structures

This work presents a methodology for the optimal design of frame structures with elevation irregularity equipped with fluid viscous dampers. Irregular structures became more prevalent in the past few decades due to architectural and functionality considerations. However, from an engineering point of view, they are more challenging to design. Irregular structures are characterized by a drastic change in strength, stiffness and/or mass. Therefore, significant inter-story drifts and plastic deformation are expected in the relevant stories. One of the common design strategies to control the structural response is to integrate energy dissipation devices. In this paper, an approach for the simultaneous design of the force-resisting system and the energy dissipation devices is presented. A mixed-integer optimization problem aims to minimize the cost is formulated. Nonlinear Response History Analysis (NRHA) is adopted to assess the dynamic response of the frame, accounting for both the nonlinear behavior of the structure and the dampers. The optimization process is carried out using an evolutionary optimization approach, the well-known Genetic Algorithm (GA). A numerical example shows the robustness of the proposed optimization strategy.

The sensitivity of optimal plastic (limit or shakedown) design with respect to geometric imperfections and post-critical deformations is discussed. It is shown that the concept of optimal plastic design should be modified to provide a proper safety factor against collapse for specified range of imperfections and configuration changes. The optimal design of frame structures is analyzed in detail.

Chapter 1 Introduction of swarm intelligent algorithms.- Chapter 2 Application of particle swarm optimization algorithm to engineering structures.- Chapter 3 Optimum design of structures with heuristic particle swarm optimization algorithm .-Chapter 4 Optimum design of structures with group search optimizer algorithm .-Chapter 5 Improvements and applications of group search optimizer in structural optimal design .-Chapter 6 Optimum design of structures with quick group search optimization algorithm .-Chapter 7 Group search optimizer and its applications on multi-objective structural optimal design .-Chapter 8 Prospecting swarm intelligent algorithms.

PurposeThis paper aims to summarize design rules based on the process characteristics of selective laser melting (SLM) and structural optimization and apply the design rules in the lightweight design of an aluminum alloy antenna bracket. The design goal is to reduce 30 per cent of the weight while maintaining the stress levels in the original part.Design/methodology/approachTo reduce weight as much as possible, the titanium alloy with higher specific strength was selected during the process of optimization. The material distribution of the bracket was improved by the topology optimization design. The redesign for SLM was used to obtain an optimization model, which was more suitable for SLM. The component performance was improved by shape optimization. The modal analysis data of the structural optimization model were compared with those of the stochastic lightweight model to verify the structural optimization model. The scanning data were compared with those of the original model to verify whether the model was suitable for SLM.FindingsStructural optimization design for antenna bracket realized the mass decrease of 30.43 per cent and the fundamental frequency increase of 50.18 per cent. The modal analysis data of the stochastic lightweight model and the structural optimization model indicated that the optimization performance of structural optimization method was better than that of the stochastic lightweight method. The comparison results between the scanning data of the forming part and the original data confirmed that the structural optimization design for SLM lightweight component could achieve the desired forming accuracy.Originality/valueThis paper summarizes geometric constraints in SLM and derives design rules of structural optimization based on the process characteristics of SLM. SLM design rules make structural optimization design more reasonable. The combination of structural optimization design and SLM can improve the performance of lightweight antenna bracket significantly.

Structural design optimization usually aims to extract more function from less material. This can result in more failure modes designed against their limits, thus reducing safety. The compromise between cost reduction and safety is particularly relevant for geotechnical structures, as they are subject to significant scatter and uncertainties in their properties. In this context, this paper presents an application of Reliability-Based Design Optimization (RBDO) for the problem of geosynthetic-reinforced soil retaining walls. Design variables include the mean strength of the geosynthetic, spacing between reinforcements, and the length of the geosynthetic. Random variables include soil angle of friction, soil unit weight, the strength of the geosynthetic and the friction angle between the reinforcement and soil mass. The solution procedure proposed herein combines the ant colony optimization (ACO) algorithm with the First Order Reliability Method (FORM) algorithm, which results in nested optimization loops. Results show that significant savings can be achieved in structural design without hindering structural safety.

This paper includes description of reliability based design optimization of elasto-plastic skeletal structures under multi-parameter static loading. Presented approach is based on the assumption that the complementary strain energy of the residual forces is considered as an overall measure of the plastic performance for plastic shakedown analysis and optimal design of the structure, and this measure is an uncertain quantity responsible for resistance of the structure by assuming Gaussian distribution. The problems yield to nonlinear mathematical programming which are solved by the use of bi-level sequential quadratic algorithm. Simple portal frames are optimized to illustrate the proposed approaches.

Optimum design of structures with stress and displacement constraints using the force method

During the last 20 years, structural optimization has become one of the most important topics of engineering applications. Design optimization of structure has been an interesting area of research in the field of engineering design for its ability to short the design cycle and to enhance product quality. Significant research activity has occurred in the area of structural optimization in the last decade. Especially for topology optimization of structure, many new theoretical, algorithmic, and computational contributions have resulted by researchers and engineers. Topology optimization is a powerful tool for global and multi-scale design of macrostructures, microstructures, and the cell of prescribed composite materials. The population based evolutionary algorithms have emerged as powerful mechanism for finding optimum solutions of complex optimization problems in engineering during the last two decades. Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation [1]. The thinking has wide application in various engineering fields, such as computer science, artificial intelligence, operations research. Genetic algorithm is another kind of bio-inspired optimization method and it is playing an increasingly important role in studies of complex adaptive systems. Its application ranges from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices and structures, such as aircraft turbines and integrated circuits [2]. Optimization of structures can be classified into three categories: sizing, shaping, and topology optimization. In the topology optimization, it is concerned with the structure members and connectivity between members. In general, it is easily represented by discrete variables rather than by those used for continuous optimization problems. Topology optimization is the most difficult and complex among three categories and it is special useful in developing innovative conceptual designs. Structural optimization, in particular the topology optimization, has been identified as one of the most challenging tasks in structural design. Various techniques and approaches have been established during the last two decades. Topology optimization usually referred to as layout optimization or general shape optimization [3]. It lets engineers get the optimal topology of structure or new configurations during product design phase, as they are implementing the design of the size

Construction of ellipsoid convex model of bounded uncertainties with outlier detection for application in non-probabilistic topology optimization

Structural analysis and design optimization have recently been extended to stochastic approach to take various uncertainties into account. However in areas whwew it is not possible to produce accurate statistical information, the probabilistic method is not appropriate for stochastic structural analysis and design optimization, since improper modeling of uncertainty could cause greater degree of statistical uncertainty than those of physical uncertainty. For uncertainty with insufficient information, possibility-based (or fuzzy set) methods have recently been introduced in stochastic structural analysis and design optimization. The main advantage of the fuzzy analysis is that it preserves the intrinsic random nature of physical variables through their membership functions and, when used for evaluation of designs, yields more conservative design than those from the probabilistic methods. There are two computational aspects in the fuzzy analysis compared to the probability analysis. First, the input fuzzy variables can be defined easier than the input random variables when no or very limited statistical data are available. Secondly, extended fuzzy operations are much simpler than those required to use probability, especially when a number of variables are involved. For possibility-based design optimization (PBDO), like for reliability-based design optimization (RBDO), the performance measure approach (PMA) with an inverse fuzzy analysis is more appropriate than other approaches, such as a possibility index approach. This paper proposes a new formulation of PBDO using PMA. It is also found that PBDO is more desirable than RBDO, since PBDO can inherently handle a system level possibility unlike RBDO. For the inverse fuzzy analysis, the maximal possibility search (MPS) method is proposed to improve numerical efficiency and accuracy comparing with the vertex method and the

DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems

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.

Optimal structural design of stiffened plate structure with multi order modal frequencies objective is discussed. It is a structural reverse design problem for the first several order modal frequencies to reach a set of given value. A new method based on bi-directional evolutionary structural optimization (BESO) and size optimization is presented. To solve the porous and irregular shape problem in the optimized structure, a regular shaped grid-like frame structure optimization model is established. The bars in the frame structure are regarded as basic unites of structure modification. The bars’ sensitivity is discussed, and applied to optimization process based on frequency sensitivity. The structural optimal design process can be described as follows: the frequencies are used as the target, the volume is considered as constraint, adjacent sensitivity redistribution method is adopted to suppress numerical instability. Then size optimization method is adopted to conduct detailed design. Finally this method is applied to a stiffened plate structure. Results show that the proposed approach is feasible to achieve given multi order modal frequencies. The optimized structure consists of regular bars, so a clear structure is obtained.

In order to solve the problem that a traditional articulated arm coordinate measuring machine (AACMM) cannot measure automatically, a self-driven AACMM is proposed. Based on the self-driven AACMM design indicators, the length of the connecting rods of the self-driven AACMM was allocated. The self-driven AACMM virtual prototype was designed and assembled based on joint module selection and joint component design, and its measurement space range was also verified. The key components of the structural optimized design and dynamic performance analysis of motion parameters are adopted, respectively, to correct the positioning error of the self-driven AACMM’s probe caused by static and dynamic deformations. The static deformation of the structure caused by the self-driven AACMM’s weight was analyzed. The influence of the dynamic flexible deformation on the positioning error of the probe of the self-driven AACMM during the variable-speed approach and the constant-speed touch measurement stage was studied. The results show that the measurement space range of the self-driven AACMM designed in this paper can meet the design index of the measuring radius. The probe position error caused by static deformation of the measuring machine after structural optimization is reduced by an order of magnitude, and the equivalent stress of the mechanical structure of the self-driven AACMM is within the allowable stress of the material. The positioning error of the probe caused by the dynamic deformation of the self-driven AACMM structure meets the positioning accuracy index. During the stage of self-driven AACMM constant-speed touch measurement, the instantaneous position error of the probe changes linearly with the touch time and the optimal touch speed (6.6 mm s−1, 6.4 mm s−1) exists to minimize the probe positioning error. During the variable-speed approach stage, the influence of the angular acceleration and the angular velocity of each joint on the positioning error of the probe is negligible when the self-driven AACMM is in the typical posture. In the extreme posture, with the optimal joint angular acceleration ( 0.00055∘s−2 ) and angular velocity ( 0.0185∘s−1 ), the inertial force of the measuring machine structure and the instantaneous position error of the probe are the smallest, and the movement stability is the best. The structural optimization design and motion parameter performance analyses of the self-driven AACMM can provide a theoretical research foundation for subsequent motion parameter optimization and dynamic error compensation modeling.

The growing urgency for sustainable construction practices has necessitated a reevaluation of traditional design approaches in favor of more environmentally responsible methodologies. This paper presents a comparative analysis between optimal and adequate structural design within the context of sustainable construction. Focusing on two case studies—a cutting-edge, sustainably designed commercial building (the Bullitt Center) and a conventional mid-rise residential building in Chicago—the research explores how different design philosophies impact material efficiency, environmental footprint, economic performance, and occupant satisfaction.Through detailed life cycle assessments (LCA) and life cycle energy analyses (LCEA), the study quantifies the advantages of optimal design, which integrates advanced materials, renewable energy systems, and water conservation technologies. The findings demonstrate that while optimal design requires a higher initial investment, it significantly reduces long-term operational costs and environmental impact, achieving a net-zero energy status and greatly improving occupant well-being. In contrast, the conventional building, which adheres to standard design practices, exhibits higher embodied energy, greater environmental degradation, and lower occupant satisfaction.The paper concludes that optimal structural design is not only more sustainable but also economically viable over the building's life span. It emphasizes the importance of integrating sustainability from the earliest stages of design to achieve meaningful environmental and economic benefits. The study calls for the adoption of policy incentives and advanced modeling tools to facilitate the widespread implementation of optimal design principles in the construction industry. Keywords: Sustainable Construction, Optimal Structural Design, Adequate Design, Building Performance, Embodied Energy, Environmental Impact, Economic Viability, Material Efficiency