Cyber‐physical approach to the optimization of semiactive structural control under multiple earthquake ground motions

This paper presents a cyber‐physical approach to optimize the semiactive control of a base‐isolated structure under a suite of earthquakes. The approach uses numerical search algorithms to guide the exploration of the design space and real‐time hybrid simulation (RTHS) to evaluate candidate designs, creating a framework for real‐time hybrid optimization (RHTO). By supplanting traditional numerical analysis (i.e., finite element methods) with RTHS, structural components that are difficult to model can be represented accurately while still capturing global structural performance. The efficiency of RTHO is improved for multiple design excitations with the creation of a multiinterval particle swarm optimization (MI‐PSO) algorithm. As a proof‐of‐concept, RTHO is applied to improve the seismic performance of a base‐isolated structure with supplemental control. The proposed RTHO framework with MI‐PSO is a versatile technique for multivariate optimization under multiple excitations. It is well suited for the accurate and rapid evaluation of structures with nonlinear experimental substructures, in particular, those that do not undergo permanent damage such as structural control devices. The RTHO framework integrates popular optimization algorithms with advanced experimental methods, creating an exciting new cyber‐physical approach to design.     

Fluid transients and pipeline optimization using GA and PSO: the diameter connection

This paper describes the optimal selection of pipe diameters in a network considering steady state and transient analysis in water distribution systems. Two evolutionary approaches, namely genetic algorithms (GA) and particle swarm optimization (PSO), are used as optimization methods to obtain pipe diameters. Both optimization programs, inspired by natural evolution and adaptation, show excellent performance for solving moderately complex real-world problems which are highly nonlinear and demanding. The case study shows that the integration of GA or PSO with a transient analysis technique can improve the search for effective and economical hydraulic protection strategies. This study also shows that not only is the selection of pipe diameters crucially sensitive for the surge protection strategies but also that more global systematic approaches should be involved in water distribution system design, preferably at an early stage in the design process.     

An integrated topology optimization framework for three‐dimensional domains using shell elements

In the last decades, topology optimization has been widely investigated as a preliminary design tool to minimize the use of material in a structure. Despite this, applications to realistic three‐dimensional engineering problems are still limited. This study provides the instruments for the definition of a versatile and integrated framework in order to apply topology optimization to large‐scale 3‐D domains for the design of efficient and high‐performing structures. The paper proposes a novel topology optimization strategy to identify the optimal layout of lateral resisting systems for tall buildings through the adoption of Mindlin–Reissner shell elements for the discretization of the continuum design domain. The framework is based on the practical interoperability between MATLAB, Ansys, and computer‐aided design (CAD) environments to incorporate optimization routines in the conceptual design phase of structural systems. Finally, the paper examines a three‐dimensional tall building case study in order to demonstrate the applicability of the proposed procedure to realistic Civil Engineering design problems and its robustness in finding optimal layouts free from mesh‐dependency instabilities.     

A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization

Abstract: The 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.     

Drift design of steel‐frame shear‐wall systems for tall buildings

Drift design methods based on resizing algorithms are presented to control lateral displacements of steel‐frame shear‐wall systems for tall buildings. Three algorithms for resizing of structural members of the steel‐frame shear‐wall systems are derived by formulating the drift design process into an optimization problem that minimizes lateral displacement of the system without changing the weight of a structure. During the drift design process, cost‐effective displacement participation factors obtained by the energy method are used to determine the amount of material to be modified instead of calculating sensitivity coefficients. The overall structural design model with the drift design method for the steel‐frame shear‐wall systems is proposed and applied to the structural design of three examples. As demonstrated in the examples, the lateral displacement and interstorey drift of a frame shear‐wall system can be effectively designed by the drift design method without the time‐consuming trial‐and‐error process. Copyright © 2002 John Wiley & Sons, Ltd.     

Constrained mean‐variance mapping optimization for truss optimization problems

Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.     

Design optimization of truss structures using cuckoo search algorithm

A new metaheuristic optimization algorithm is developed to solve truss optimization problems. The new algorithm, called cuckoo search (CS), is examined by solving five truss design optimization problems with increasing numbers of design variables and complexity in constraints. The performance of the CS algorithm is further compared with various classical and advanced algorithms, selected from a wide range of the state‐of‐the‐art algorithms in the area. The results identify that the final solutions obtained by the CS are superior compared with the best solutions obtained by the other algorithms. Finally, the unique search features used in the CS and the implications for future researches are discussed in detail. Copyright © 2012 John Wiley & Sons, Ltd.     

Building energy system optimizations with utilization of waste heat from cogenerations by means of genetic algorithm

Distributed energy systems based on cogeneration offer significant potential to save energy since they effectively utilize waste heat from power generators. However, unless there is an appropriate combination of machinery and operations, the planned performance cannot be achieved. Thus, it is quite difficult to determine the optimal combination of machinery and operations. For this, an optimal design approach is needed. In this study, a new optimal design method for building energy systems is proposed. There are an enormous variety of combinations with regard to energy supply and demand. This method designs the most efficient energy system by optimizing the operation of available systems with consideration for the optimal capacity of machinery in the systems. Optimization algorithms known as “genetic algorithms” (GAs) with the capacity to deal with non-linear optimization problems have been adopted in this optimization analysis. In this study, a single-building energy system is evaluated. The result shows that the proposed method is sufficiently capable of optimizing the design, and has the potential to be applied to very complex energy systems with appropriate improvements.     

Optimum design of post‐tensioned axially symmetric cylindrical walls using novel hybrid metaheuristic methods

An efficient methodology for various structural design problems is needed to optimize the total cost for structures. Although some methods seem to be efficient for applications, due to using special algorithm parameters, computational cost, and some other reasons, there is still much to be done in order to develop an effective method for general design applications. This paper describes the influence of the selected procedure on the design of cost‐optimized, post‐tensioned axially symmetric cylindrical reinforced concrete walls. In this study, the optimum design of axially symmetric cylindrical walls using several metaheuristic algorithms is investigated. The new generation algorithms used in the study are flower pollination algorithm, teaching–learning‐based optimization, and Jaya algorithm (JA). These algorithms are also compared with one of the previously developed algorithm called harmony search. The numerical examples were done for walls with 4‐ to 10‐m height and for 1, 5, 10, 15, 20, and 25 post‐tensioned load cases, respectively. Several independent runs are conducted, and in some of these runs, JA may trap to a local solution. To overcome this situation, hybrid algorithms such as JA using Lévy flights, JA using Lévy flights with probabilistic student phase (JALS), JA using Lévy Flights with consequent student phase (JALS2), and JA with probabilistic student phase are developed. It is seen that in many respects, the JALS2 and JALS are the most effective within the proposed hybrid approaches.     

Advanced energy‐based analyses of trusses employing hybrid metaheuristics

Total potential optimization using metaheuristic algorithm (TPO/MA) is an alternative method in structural analyses, and it is a black‐box application for nonlinear analyses. In the study, an advanced TPO/MA using hybridization of several metaheuristic algorithms is investigated to solve large‐scale structural analyses problems. The new generation algorithms considered in the study are flower pollination algorithm (FPA), teaching learning‐based optimization, and Jaya algorithm (JA). Also, the proposed methods are compared with methodologies using classic and previously used algorithms such as differential evaluation, particle swarm optimization, and harmony search. Numerical investigations were carried out for structures with four to 150 degrees of freedoms (design variables). It has been seen that in several runs, JA gets trapped into local solutions. For that reason, four different hybrid algorithms using fundamentals of JA and phases of other algorithms, namely, JA using Lévy flights, JA using Lévy flights and linear distribution, JA with consequent student phase, and JA with probabilistic student phase (JA1SP), are developed. It is observed that among the variants tried, JA1SP is seen to be more effective on approaching to the global optimum without getting trapped in a local solution.     

Reliability-based optimization in structural engineering

In this paper reliability-based optimization problems in structural engineering are formulated on the basis of the classical decision theory. Several formulations are presented: Reliability-based optimal design of structural systems with component or systems reliability constraints, reliability-based optimal inspection planning and reliability-based experiment planning. It is explained how these optimization problems can be solved by application of similar techniques. The reliability estimation is limited to first order reliability methods (FORM) for both component and systems reliability evaluation. The solution strategies applying first order non-linear optimization algorithms are described in detail with special attention to sensitivity analysis and stability of the optimization process. Furthermore, several practical aspects are treated as: Development of the reliability-based optimization model, inclusion of the finite element method as the response evaluation tool and how the size of the problem can be made practicable. Finally, the important task of model evaluation and sensitivity analysis of the optimal solution is treated including a strategy for model-making with both pre and post-analysis.     

An approach for building design optimization using design of experiments

Building simulation based optimization involves direct coupling of the optimization algorithm to a simulation model, making it computationally intensive. To overcome this issue, an approach is proposed using a combination of experimental design techniques (fractional factorial design and response surface methodology). These techniques approximate the simulation model behavior using surrogate models, which are several orders of magnitude faster than the simulation model. Fractional factorial design is used to identify the significant design variables. Response surface methodology is used to create surrogate models for the annual cooling and lighting energy with the screened significant variables. The error for these models is less than 10%, validating their effectiveness. These surrogate models speed up optimization with genetic algorithms, for single- and multi-objective optimization problems and scenario analyses, resulting in a better solution. Thus, optimization becomes possible within reasonable computational time with the proposed methodology. This framework is illustrated using the case study of a three-storey office building for New Delhi.     

Sense‐Think‐Act Framework for Intelligent Building Energy Management

The realization of smart and energetically efficient buildings is contingent upon the successful implementation of two tasks that occur on distinct phases of the building life cycle: in the design and subsequent retrofitting phases, the selection and implementation of an effective energy concept, and, during the operation phase, the actuation of energy systems to ensure parsimonious energy use while retaining acceptable end‐user thermal comfort. Operational efficiencies are achieved through the use of Building Energy Management Systems tasked to deliver core Sense, Think, Act (STA) functionalities: Sense, using sensing modalities installed in the building; Think, utilizing, typically a rule‐based decision system; and Act, by sending actuation commands to controllable building elements. Providing the intelligence in this STA process can be a formidable task due to the complex interplay of many systems and occurrence of disturbances. In this article, an architectural and algorithmic framework is presented to provide streamlined implementation of this process. Important ingredients in this framework are: (S) a data access component capable of collecting and aggregating information from a number of heterogeneous sources (sensors, weather stations, weather forecasts); (T) a model‐based optimization methodology to generate intelligent operational decisions; and (A) an assessment and actuation component. An illustrative application of the proposed methodology in an office building is provided.     

Convex relaxation for efficient sensor layout optimization in large‐scale structures subjected to moving loads

This paper proposes a computationally effective framework for load‐dependent optimal sensor placement in large‐scale civil engineering structures subjected to moving loads. Two common problems are addressed: selection of modes to be monitored and computational effectiveness. Typical sensor placement methods assume that the set of modes to be monitored is known. In practice, determination of such modes of interest is not straightforward. A practical approach is proposed that facilitates the selection of modes in a quasi‐automatic way based on the structural response at the candidate sensor locations to typical operational loads. The criterion used to assess sensor placement is based on Kammer's Effective Independence (EFI). However, in contrast to typical implementations of EFI, which treat the problem as a computationally demanding discrete problem and use greedy optimization, an approach based on convex relaxation is proposed. A notion of sensor density is applied, which converts the original combinatorial problem into a computationally tractable continuous optimization problem. The proposed framework is tested in application to a real tied‐arch railway bridge located in central Poland.     

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

Steel dome structures, with their striking structural forms, take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns. In this paper, the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider (WS), grey wolf (GW), and brain storm optimization (BSO). The structural elements of the domes are treated as design variables collected in member groups. The structural stress and stability limitations are enforced by ASD-AISC provisions. Also, the displacement restrictions are considered in design procedure. The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface (OAPI). The optimum spatial steel dome designs achieved by proposed WS, GW, and BSO algorithms are compared with respect to solution accuracy, convergence rates, and reliability, utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization (HBO) algorithm.     

Computational intelligence techniques for HVAC systems: A review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.     

Plant growth simulation algorithm based on double growth point parallel growth mechanism and its structural optimization

针对模拟植物生长算法(PGSA)系列算法中存在的搜索路径相对单一、搜索覆盖面不够广等问题，结合复杂结构优化问题中设计变量多、存在多个局部最优解、算法难以自动终止等特点，基于PGSA的基本原理和植物的实际生长规律，提出一种新的算法机制——双生长点并行生长机制，并与基于生长空间限定与并行搜索(GSL&PS-PGSA)算法相融合。通过典型数学及空间桁架结构算例进行了验证，结果表明：双生长点并行生长机制增加了寻优搜索路径，拓宽了搜索覆盖面，降低了陷入局部最优解的概率，并为算法提供更为有效的终止机制，从而具有更加显著的优化效率及全局搜索能力；与序列两级算法、蚁群算法等常用优化方法相比，融入双生长点并行生长机制的GSL&PS-PGSA进一步提升了算法的优化求解能力，在结构优化问题中表现出良好的适应性及有效性。     

基于双生长点并行生长机制的模拟植物生长算法及其结构优化

针对模拟植物生长算法(PGSA)系列算法中存在的搜索路径相对单一、搜索覆盖面不够广等问题,结合复杂结构优化问题中设计变量多、存在多个局部最优解、算法难以自动终止等特点,基于PGSA的基本原理和植物的实际生长规律,提出一种新的算法机制——双生长点并行生长机制,并与基于生长空间限定与并行搜索(GSL&PS-PGSA)算法相融合。通过典型数学及空间桁架结构算例进行了验证,结果表明：双生长点并行生长机制增加了寻优搜索路径,拓宽了搜索覆盖面,降低了陷入局部最优解的概率,并为算法提供更为有效的终止机制,从而具有更加显著的优化效率及全局搜索能力;与序列两级算法、蚁群算法等常用优化方法相比,融入双生长点并行生长机制的GSL&PS-PGSA进一步提升了算法的优化求解能力,在结构优化问题中表现出良好的适应性及有效性。     

Virtuelles Bauen und partnerschaftliche Geschäftsmodelle – eine innovative Verbindung

Virtual construction and partnership‐based business models – an innovative combination. When clients, design engineers and construction companies work together within the framework of partnership‐based business models from an early stage, major disadvantages of traditional business relationships in the construction sector can be eliminated. Close cooperation in an early phase of a construction project makes for interdisciplinary optimization with regard to a property's design, construction and utilization. In this context, the methods of virtual construction are useful tools to improve communication between the parties involved in a construction project. Using virtual construction has therefore proved to be particularly effective when working with partnership‐based business models that comprise a preconstruction phase. Virtual construction helps to optimize building structures and assists in graphically communicating optimization results by way of visualization with interactive computer models. Virtual Design and Construction (ViCon) introduces virtual construction in the daily design and construction work and is a key technology in the construction industry. In practice, ViCon is employed in numerous projects that are implemented based on the PreFair business model. Using selected examples, this article shows the range of ViCon's possible application.     

Optimal probability‐based partial mass isolation of elevated coal scuttle in thermal power plant building

Partial mass isolation (PMI) system is a practical strategy to mitigate seismic response of the main structure. Many studies provide various formulas to estimate an optimum design solution for the structure under simplified excitation models. But the efficiency of these optimization methods under actual ground motion records requires investigation. This paper proposes a new optimization design framework, which considers the randomness of ground motions. To describe the vibration depression effects of the PMI under various records, several theoretical distributions were assumed and tested. A Weibull distribution was selected because of its best performance in the chi‐squared tests among the several theoretical distributions. A sensitivity study on the number of records was performed to ensure the accuracy of estimated parameters with a relatively small sample size. This framework was adopted in the design of a PMI system for a large‐scale thermal power plant building through both single‐objective and multiobjective optimization procedures. Optimal design results from the single‐objective optimization procedure were compared with those from traditional formulas. Additionally, with the relative displacement limitation, the Pareto optimum set was obtained from the multiobjective optimization procedure. The final design was compared with the single‐objective optimization result.