Thermal optimization of multilayered walls using genetic algorithms

This paper reports on an optimization of a building wall using genetic algorithms. The double objective of optimization is maximizing thermal insulation and maximizing thermal inertia. If insulation is easily quantified with thermal resistance, inertia is a criterion not characterized with a single parameter. Using the quadrupoles method, we propose the thermal capacitance as a way to quantify the inertia of the wall. Walls realizing the best trade-off between the two conflicting objectives are presented in a Pareto front. Optimal walls composition shows that the best layers disposition is massive layer at indoor side and insulating layer at outdoor side. An important and new result obtained in this study is that the optimal thickness of the indoor side massive layer is Λ/4 where the thermal wavelength Λ is an intrinsic parameter of the layer material depending on the period of oscillations.     

Simulation optimization for earthmoving operations using genetic algorithms

This paper presents a methodology for simulation optimization utilizing genetic algorithms and applies it to a newly developed simulation-based system for estimating the time and cost of earthmoving operations. The genetic algorithm searches for a near-optimum fleet configuration that reduces project total cost, and considers a set of qualitative and quantitative variables that influence earthmoving operations. Qualitative variables represent the models of equipment used in each fleet scenario, whereas quantitative variables represent the number of items of equipment involved in each scenario. Pilot simulation runs were carried out for all configurations generated by the developed algorithm, and a complete simulation analysis was then performed for the fleet recommended by the algorithm. The numerical example demonstrates the use of the proposed methodology and illustrates its essential features.     

Fuzzy optimization of cold-formed steel sheeting using genetic algorithms

In this paper, genetic algorithms are applied for optimization of dimensions of cold-formed steel trapezoidal sheeting. The objective of the optimization is to obtain the minimum weight subjected to the given constraints in accordance with Eurocode 3, Part 1.3. In traditional optimization, these constraints are defined with crisp number. However, in practical engineering, constraints with a small certain percentage of violation can be acceptable. Thus, in this research, sheeting is optimized to satisfy the constraints considering the fuzziness so that the optimization is more practical from the engineering point of view. The better performance of introducing the fuzziness into a constraints-handling technique has been demonstrated with a design example.     

Capital renewal optimisation for large-scale infrastructure networks: genetic algorithms versus advanced mathematical tools

Civil infrastructure assets require continuous renewal (repair, rehabilitation or replacement) actions to modernise the inventory and sustain its operability. Allocating limited renewal funds among numerous asset components, however, represents a complex optimisation problem. Earlier efforts using genetic algorithms (GAs) could optimise small size problems yet exhibiting steep degradation in solution quality as problem size increases. Even by applying sophisticated mechanisms such as ‘segmentation’ to improve the performance of GAs, large processing time hinders the practicality of the algorithm for large-scale problems. This article, therefore, aims at improving both processing speed and solution quality for very large-scale problems (up to 50,000 assets). The article develops optimisation models using an advanced modelling tool (GAMS/CPLEX), and compares its results with GAs on three different model formulations. Both approaches proved to be beneficial, yet the advanced mathematical approach showed superior performance.     

Strut-and-tie modelling of reinforced concrete beams using genetic algorithms optimization

This paper presents a method for automatically producing optimal strut-and-tie models for the design of reinforced concrete beams. The optimal model is generated by means of an optimization problem solved by using genetic algorithms. The basic idea developed here is that from an initial random generation of possible configurations of the strut-and-tie model for the beam subjected to study, new populations of possible configurations may be generated in an iterative way by using genetic operators until reaching an optimum solution for the studied problem which corresponds to the strut-and-tie configuration which minimizes the total strain energy. In the optimal configuration, compressive struts are not enforced to be parallel, which allows representing more consistently the physical reality of the flow of forces. Furthermore, the method is more simple and easier to apply than the methods based on the concepts of evolutionary structural optimization.     

Development of intervention programs for inland waterway networks using genetic algorithms

Inland waterways often consist of large numbers of man-made objects to ensure navigability. These objects are of many different types, ages and sizes, and deteriorate in uncountable of different ways. In order to ensure that the deterioration of the objects does not result in a loss of navigability, interventions must be executed. This, however, produces costs, in terms of both labour and material costs and costs of loss of service if the waterway is rendered non-navigable during intervention. In this paper, a methodology is presented to determine optimal multiple time period intervention programmes for inland waterways. The optimal intervention programme is the one that has highest net benefit, i.e. overall benefits minus overall costs, where benefits are the reduction in risk of failure. A genetic algorithm is used to overcome the problem of combinatorial explosion when many objects, in many states, over many time periods are to be considered. The exact formulation of the genome, as well as the genetic fitness function, are presented. They are used to determine an optimal intervention programme for a fictive inland waterway network. The results are presented and discussed, and an outlook is provided on further steps to improve this methodology.     

Multi-stage production cost optimization of semi-rigid steel frames using genetic algorithms

The response of a steel structure is closely related to the behavior of its joints. This means that it is necessary to take explicit account of joint properties in order to ensure a consistent approach to design optimization of steel frames. Semi-rigid design has been introduced into steel construction standards such as Eurocode 3 and AISC. However, in the absence of appropriate guidelines, engineers encounter difficulties when bringing in semi-rigid design to everyday engineering practice. Moreover, connection design significantly affects the production cost of steel frame structures. Thus, a realistic optimization of frame design should take into account the effective costs of different stages of production including manufacturing and erection activities. This paper presents a Genetic Algorithm based method for multi-stage cost optimization of steel structures. In the objective function, the total cost of different production stages is minimized. A new cost model is presented that itemizes costs of all stages of production (material supply, manufacturing, erection and foundation). Design examples are used to validate the proposed methodology. Numerical validation shows that the multi-stage design optimization results in substantial cost benefits between 10% and 25% compared to traditional design of steel frames. Furthermore, the developed methodology is shown to be capable of measuring the possible impact of design choices in the early design stage thus assisting designers to make better design decisions.     

Reliability based optimization of laminated composite structures using genetic algorithms and Artificial Neural Networks

The design of anisotropic laminated composite structures is very susceptible to changes in loading, angle of fiber orientation and ply thickness. Thus, optimization of such structures, using a reliability index as a constraint, is an important problem to be dealt. This paper addresses the problem of structural optimization of laminated composite materials with reliability constraint using a genetic algorithm and two types of neural networks. The reliability analysis is performed using one of the following methods: FORM, modified FORM (FORM with multiple checkpoints), the Standard or Direct Monte Carlo and Monte Carlo with Importance Sampling. The optimization process is performed using a genetic algorithm. To overcome high computational cost it is used Multilayer Perceptron or Radial Basis Artificial Neural Networks. It is shown, presenting two examples, that this methodology can be used without loss of accuracy and large computational time savings, even when dealing with non-linear behavior.     

HVAC system optimization with CO2 concentration control using genetic algorithms

This study describes the use of genetic algorithms (GAs) for operating standard HVAC systems (HVAC—heating, ventilation and air conditioning) in order to optimize performance, primarily with regard to power saving. Genetic algorithms were introduced as an instrument for solving optimization problems. Analytic optimization procedures are widely used in other fields of engineering, but they are difficult to operate within HVAC systems, because the range of the research is usually too broad, the problems are not linear but rather discontinuous, and they mostly have complex limitations. This is why for this type of system genetic algorithms are used, since they have the qualities of robustness and efficiency that are crucial for finding the optimal solution. A simulation is conducted in order to demonstrate how much power can be saved by using the suggested method of CO₂ concentration control in a standard HVAC system. In addition to Matlab Simulink, the suggested method is verified with Energy software.     

Applying multi-objective genetic algorithms in green building design optimization

Since buildings have considerable impacts on the environment, it has become necessary to pay more attention to environmental performance in building design. However, it is a difficult task to find better design alternatives satisfying several conflicting criteria, especially, economical and environmental performance. This paper presents a multi-objective optimization model that could assist designers in green building design. Variables in the model include those parameters that are usually determined at the conceptual design stage and that have critical influence on building performance. Life cycle analysis methodology is employed to evaluate design alternatives for both economical and environmental criteria. Life cycle environmental impacts are evaluated in terms of expanded cumulative exergy consumption, which is the sum of exergy consumption due to resource inputs and abatement exergy required to recover the negative impacts due to waste emissions. A multi-objective genetic algorithm is employed to find optimal solutions. A case study is presented and the effectiveness of the approach is demonstrated for identifying a number of Pareto optimal solutions for green building design.     

Optimization of Mediterranean building design using genetic algorithms

While it is possible to check the energy performance of a given building by means of several available methods, the inverse problem of determining the optimum configuration given a desired performance is more difficult to solve. In the Mediterranean region this problem is more complex due to the following two reasons: the air-conditioning load is as important as the heating load, and the energy needs depend on a high number of architectural parameters which have different, even contradictory, effects on summer and winter loads. In this paper we present an optimization algorithm that couples pseudo-random optimization techniques, the genetic algorithms (GA), with a simplified tool for building thermal evaluation (CHEOPS) for the purpose of minimizing the energy consumption of Mediterranean buildings. Since increasing the energy performance usually requires the use of special devices resulting in a high construction cost, we also propose to use GA for the purpose of economical optimization.     

遗传算法在悬索结构形态优化中的应用

本文提出了关于悬索结构形态优化时设计变量的选取和优化目标的确立方法.通过对基于遗传算法的悬索结构形态优化的研究,表明遗传算法应用于柔性结构优化的可行性和收敛性,且其具有全局搜索能力强、运算稳健性好等优点.     

Maintenance incentives under different infrastructure regimes

An increasing issue in privatised infrastructure is the appropriate incentives needed to ensure adequate maintenance of the infrastructure as a public resource. This paper explores the implications of some of the insights from theories of regulation and contracts for optimal management of transport infrastructure maintenance with respect to the interests of different stakeholder groups: contractors, owners, regulators, governments (subsidy providers or guarantors) and users. Evidence is taken from two UK examples: the major road network and the rail network. The former is seen to be largely a successful involvement of private capital through PFI-style DBFO deals, which has had positive impacts on service quality and cost to the public budget, though arguably less than could have been achieved. The rail network privatisation is seen as a failure in which maintenance was sacrificed in the interests of short-term profit. However, it can also be argued here that the real mistake was to underestimate the quality of the network inherited from British Rail. The paper concludes with some lessons and recommendations taken from the analysis of these two sets of cases.     

遗传算法在洞群二次衬砌厚度优化中的应用

 因地形地貌及工程间的相互影响,高密度地下洞室群的出现概率逐渐增大,洞群各洞室与洞群整体稳定性的关系、洞群的主导破坏模式、合理规划设计各洞室支护措施强度以实现等强度设计理念等课题研究的需求越加迫切。基于有限元强度折减理论,依托重庆某一大型高密度地下洞室群,对比现行安全系数主要判定准则在洞群应用中的可行性及合理性;基于最小二乘法,利用二次多项式对各洞室安全系数与二衬厚度的隐含关系进行显示拟合,并通过遗传算法对二衬厚度进行优化。洞室间夹层围岩等次要部位的局部破坏对洞室乃至洞群的整体稳定性影响较小。并行洞室的主导破坏模式与两者间距、埋深等因数有关,主要分为3种破坏模式：埋深较浅且洞间间距较大,洞室拱顶塑性围岩破坏区向背离洞群中心的斜上方发展并贯通至地表,洞群破坏;当埋深较深且洞间间距较小,各洞室拱顶塑性围岩破坏区向靠近洞群中心的斜上方交汇贯通,洞群破坏;当洞间间距及埋深均处于两种破坏模式之间时,上述两种破坏模式共同导致洞群破坏。洞群主导破坏模式多样,洞室关键点位移随强度折减系数的变化曲线不一定有明显的突变点,此时宜结合塑性区分布及其发展历程共同确定洞室安全系数。基于最小二乘法的二次多项式对隐含关系显示表达,利用遗传算法对洞群各二衬厚度进行优化,既可达到一定的拟合精度也能得到较理想的优化结果。     

Optimal design method for building energy systems using genetic algorithms

In this paper, a new optimal design method for building energy systems is proposed. This method provides the most efficient energy system, best combination of equipment capacity and best operational planning for cooling, heating, and power simultaneously with respect to certain criteria such as energy consumption, CO₂ emission, etc. Specifically for this paper, the authors apply this method to a sample building as a case study. The “Genetic Algorithms (GA)” optimization method, which can resolve nonlinear optimization problems, is adopted for this optimization analysis. Also its applicability is analyzed in a case study. In order to validate the accuracy of this method, the correct optimum solution based on comprehensive inquiries is also calculated. A comparison of the GA solution with the correct solution demonstrates fairly good agreement. The results show that the proposed method is sufficiently capable of determining the optimal design and has the potential to be applied to very complex energy systems with appropriate modifications.     

Design of water distribution networks using particle swarm optimization

Application of particle swarm optimization (PSO) is demonstrated through design of a water distribution pipeline network. PSO is an evolutionary algorithm that utilizes the swarm intelligence to achieve the goal of optimizing a specified objective function. This algorithm uses the cognition of individuals and social behaviour in the optimization process. For the optimization of water distribution system, a simulation – optimization model, called PSONET is developed and used in which the optimization is by PSO. This formulation is applied to two benchmark optimization design problems. The results are compared with the results obtained by other optimization methods. The results show that the PSO is more efficient than other optimization methods as it requires fewer objective function evaluations.     

Multi-objective maintenance strategy for in-service corroding pipelines using genetic algorithms

Abstract

The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints.     

关于“遗传算法在土钉支护结构优化设计中的应用”的讨论

学习了贵刊 2 0 0 1年第 5期发表的“遗传算法在土钉支护结构优化设计中的应用”(作者 :贺可强 ,阳吉宝 ,王胜利 ,以下简称“原文”)一文 ,颇受启发 ,有些疑问提出与原文作者讨论。 (1)关于最小稳定系数问题边坡等的最危险圆弧滑动面位置一般由三个参数确定 ,即圆心位置的两个坐标及圆弧半径 ,而原文中却只用遗传算法求出了圆心位置的两个坐标 ,并没有求出圆弧半径 ,应该说此时圆弧滑动面并不能确定出来 ,如果圆弧滑动面仅由圆心位置确定 ,此时应作滑动面过边坡坡角等一些假设 ,而原文中没有。从原文图 1来看 ,原文中似乎隐含了圆弧滑动面过坡角的假设 ,如果是 ,那么此假设的依据是什么 ,是否     

Optimum design of cold-formed steel columns by using micro genetic algorithms

Cold-formed steel members such as beams and columns have the great flexibility of cross-sectional profiles and sizes available to structural steel designers. However, this flexibility makes the selection of the most economical section difficult for a particular situation. In this study, micro genetic algorithm (MGA) is used to find an optimum cross section of cold-formed steel channel and lipped channel columns under axial compression. Flexural, torsional and torsional–flexural buckling of columns and flat-width-to-thickness ratio of web, flange and lip are considered as constraints. The design curves are generated for optimum values of the thickness, the web flat-depth-to-thickness ratio, the flange flat-width-to-thickness ratio for columns. As numerical results, the optimum design curves are presented for various load level and column lengths.     

Automatic generation of energy conservation measures in buildings using genetic algorithms

Building energy simulations are key to studying energy efficiency in buildings. The state-of-the art building energy simulation tools requires a high level of multi disciplinary domain expertise from the user and many technical data inputs that curb the usability of such programs. In this paper an IT tool is presented, which has the capability of predicting a building's energy utilization configuration based on the reported annual energy and a few non-technical inputs from the user; and correspondingly generates cost effective energy conservation measures for the intended savings.The approach first identifies the system variables that are critical to a building's energy consumption and searches for the combination of these parameters that would give rise to the annual energy consumption as reported by the facility. Genetic algorithms are utilized to generate this database. A statistical fit is formulated between the system variables and the annual energy consumption from the database. Using this correlation, system configuration for the target energy efficiency is determined with corresponding energy conservation measures. A cost analysis is carried out to prescribe the most cost effective energy conservation measures. Competency of the tool is demonstrated in the paper through case studies on three geographies with different climate conditions.