Novel Cellular Automata Approach to Optimal Water Distribution Network Design

This paper proposes a novel heuristic-based and cellular automata-inspired approach to the optimal design of water distribution networks. The design of water distribution networks is of central importance to the water industry, but many networks cannot be optimally designed by traditional techniques due to their complexity. Genetic algorithms have become a state-of-the-art technique for this purpose but are hampered by the fact that they are population based and require a large number of model evaluations to achieve good solutions. The proposed approach uses a parallel, localist, heuristic-based algorithm to optimally design water distribution networks requiring only a limited number of model evaluations. The algorithm is applied to a well-known simple test network and two real water distribution systems in the U.K. The results indicate that the proposed cellular approach is a viable alternative to genetic algorithm approaches while using only a fraction of the computational time required by its evolutionary counterpart.     

Optimum Concrete Mixture Proportion Based on a Database Considering Regional Characteristics

This paper presents an enhanced design methodology for optimal mixture proportion of concrete composition with respect to accuracy in the case of using prediction models based on a limited database. In proposed methodology, the search space is constrained as the domain defined by a limited database instead of constructing the database covering the region represented by the possible ranges of all variables in the input space. A model for defining the search space which is expressed by the effective region in this paper and evaluating whether a mix proportion is effective is added to the optimization process, yielding highly reliable results. To demonstrate the proposed methodology, a genetic algorithm, an artificial neural network, and a convex hull were adopted as an optimum technique, a prediction model for material properties, and an evaluation model for the effective region, respectively. And then, it was applied to an optimization problem wherein the minimum cost should be obtained under a given strength requirement. Experimental test results show that the mix proportion obtained from the proposed methodology considering the regional characteristics of the database is found to be more accurate and feasible than that obtained from a general optimum technique that does not consider this aspect.     

基于工序加工能力的并行公差优化设计新方法

文献[1]提出了一种基于工序加工能力的并行工序公差优化设计方法,并用优化算法求解优化模型,本文提出了一种将遗传算法与复合形算法相结合而构成的遗传复合形算法。在遗传算法初始化之后,用复合形法先进行局部寻优,选出较好的个体,再用遗传算法在全局寻求最优解,将其应用于并行公差模型,与资料[1]中结果做比较,发现较以前的算法有较大改进。     

Optimal Layout of Sewer Systems: A Deterministic versus a Stochastic Model

The optimization of a new or partially existing urban drainage system may be modeled as a subproblems sequence of layout and optimal design within the discrete search space. The design optimization, incorporating the optimal selection of the pumping stations, intermediate manholes, pipe sections, and installation depths, for a general system fixed layout in plan, is a high level sequential decision problem which may be efficiently solved deterministically through a multilevel dynamic programming model. The optimal general layout may be selected in a deterministic way by means of a simple economical comparison of all plan solutions having optimized designs, for small to medium sized systems (if the specific restrictions of the applications are appropriately exploited) in practicable computer time. For larger dimension networks, where it is clearly impossible to achieve plan optimization with full enumeration (which is a NP complete), stochastic search models can be used. For the subproblem layout, an effective enumeration model is presented; the results of a stochastic model proposed previously, using simulated annealing for an application example, are compared and discussed in detail.     

Address-Oriented Impedance Matrix Method for Generic Calibration of Heterogeneous Pipe Network Systems

The generic evaluation of pipeline parameters is one of the most demanding technological tasks in the efficient management of a water distribution system. Information about current pipeline status is feasible by monitoring the pressure variation online. Conventional methods of transient computation and parameter calibration for a heterogeneous pipeline network suffer from cost issues both in time and storage as well as several other constraints associated with the numerical representation of a real-life system. As an alternative approach, an extension of the impulse response method, namely the address-oriented impedance matrix method (AOIMM), has been developed for a more robust calibration of a heterogeneous and multilooped pipe network system. The genetic algorithm was incorporated into the AOIMM for generic calibration of several parameters, such as the location and quantity of leakage, friction factor, and wave propagation speed. The potential of the proposed calibration algorithm over other conventional approaches was demonstrated when it was applied to a hypothetical heterogeneous pipe network system.     

Numerical Error in Weighting Function-Based Unsteady Friction Models for Pipe Transients

The accurate simulation of pressure transients in pipelines and pipe networks is becoming increasingly important in water engineering. Applications such as inverse transient analysis for condition assessment, leak detection, and pipe roughness calibration require accurate modeling of transients for longer simulation periods that, in many situations, requires improved modeling of unsteady frictional behavior. In addition, the numerical algorithm used for unsteady friction should be highly efficient, as inverse analysis requires the transient model to be run many times. A popular model of unsteady friction that is applicable to a short-duration transient event type is the weighting function-based type, as first derived by Zielke in 1968. Approximation of the weighting function with a sum of exponential terms allows for a considerable increase in computation speed using recursive algorithms. A neglected topic in the application of such models is evaluation of numerical error. This paper presents a discussion and quantification of the numerical errors that occur when using weighting function-based models for the simulation of unsteady friction in pipe transients. Comparisons of numerical error arising from approximations are made in the Fourier domain where exact solutions can be determined. Additionally, the relative importance of error in unsteady friction modeling and unsteady friction itself in the context of general simulation is discussed.     

Extended Use of Linear Graph Theory for Analysis of Pipe Networks

Linear graph theory used for pipe network analysis is to make the method systematic. A numerical method that uses linear graph theory is presented for the steady-state analysis of flow and pressure in a pipe network including its hydraulic components (pumps, valves, junctions, etc.). The proposed method differs from other linear graph methods in terms of the linear graph and the selection of its tree. The solution algorithm uses a function that depends on a power law to update the pipe flows in successive iterations. The exponents of this function are chosen to obtain a fast convergence rate even for large errors in the assumption of initial pipe flows. The convergence rate of the proposed method is validated using an error function and is compared to those of other methods. Some typical networks are analyzed to check the reliability of the proposed method. The results demonstrate the superior conditioning of the proposed method.     

Genetic algorithms-based design and optimization of statistical quality-control procedures

In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable analytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I developed two interactive computer programs based on the deterministic crowding genetic algorithm. Given an analytical process, the program "Optimize" optimizes a user-defined QC procedure, whereas the program "Design" designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or near-optimal solution. The possible solutions of the optimization problem are evaluated with computer simulation.     

带钢表面缺陷多维混合特征提取及识别

为了自动获得最具区分力的多维融合特征,提出了改进的ReliefF算法对带钢多维混合特征进行自动评估选择。针对ReliefF算法不能去除冗余特征的缺点,引入最大信息压缩准则去除冗余特征。在此基础上,采用遗传神经网络建立带钢缺陷识别的知识库,遗传算法可以自主地辨识最小的包含最优解的搜索空间,再由BP算法按负梯度方向进行权值及阈值的修正。研究结果表明:改进ReliefF算法为后续分类识别提供了最优的特征向量,减少了数据的运算量和存储量;遗传神经网络算法获得了在满足准确性前提下更高网络识别缺陷的效率。     

Optimal Design of Pressurized Irrigation Subunit

A linear programming (LP) model is presented for optimal design of the pressurized irrigation system subunit. The objective function of the LP is to minimize the equivalent annual fixed cost of pipe network of the irrigation system and its annual operating energy cost. The hydraulic characteristics in the irrigation subunit are ensured by using the length, energy conservation, and pressure head constraints. The input data are the system layout, segment-wise cost and hydraulic gradients in all the alternative pipe diameters, and energy cost per unit head of pumping water through the pipeline network. The output data are: segment-wise lengths of different diameters, operating inlet pressure head, and equivalent annual cost of the pipeline network. The explicit optimal design is demonstrated with design examples on lateral and submain or manifold of pressurized irrigation systems. The effect of the equations for friction head loss calculation on optimization procedure is investigated through the design example for microirrigation manifold. The performance evaluation of the proposed model in comparison with the analytical methods, graphical methods, numerical solutions, and dynamic programming optimization model reveals the good performance of the proposed model. The verification of operating inlet pressure head obtained by the proposed model with accurate numerical step-by-step method suggested that it is mostly accurate.     

Stochastic Construction Time-Cost Trade-Off Analysis

Traditional time-cost trade-off analysis assumes that the time and cost of an option within an activity are deterministic. However, in reality the time and cost are uncertain. Therefore, in analyzing the time-cost trade-off problem, uncertainties should be considered when minimizing project duration or cost. Simulation techniques are useful for analyzing stochastic effects, but a general strategy∕algorithm is needed to guide the analysis to obtain optimal solutions. This paper presents a hybrid approach that combines simulation techniques and genetic algorithms to solve the time-cost trade-off problem under uncertainty. The results show that genetic algorithms can be integrated with simulation techniques to provide an efficient and practical means of obtaining optimal project schedules while assessing the associated risks in terms of time and cost of a construction project. This new approach provides construction engineers with a new way of analyzing construction time∕cost decisions in a more realistic manner. Historical time∕cost data and available options to complete a project can be modeled, so that construction engineers can identify the best strategies to take to complete the project at minimum time and cost. Also, what-if scenarios can be explored to decide the desired∕optimal time and∕or cost in planning and executing project activities.     

Recursive Design of Pressurized Branched Irrigation Networks

This paper presents a new method to design pressurized branched irrigation networks. This method is called recursive design and is based on application of the problem-solving technique known as backtracking to the problem of the optimum design of pressurized branched irrigation networks with a known delivery piezometric head (pipe-sizing). Recursive design is a heuristic optimizer, like genetic algorithms, and has been implemented in a fast, versatile computer application. After presenting and precisely defining the design problem, the writers review the theoretical foundations of some of the main existing design methods: maximum velocity, recommended velocity, Mougnie velocity, constant hydraulic slope, Lagrange multipliers, linear programming, Labye’s method, and genetic algorithms. Next, the writers explain what recursive design consists of and apply its methodology in detail to a simple network. In the results section, the solutions obtained by recursive design are compared with those obtained by the other design methods, giving satisfactory results. For example, in an analyzed standard network, genetic algorithms take more than 20?minutes to offer a solution, whereas recursive design offers a cheaper solution with less than 3?seconds of computation time.     

Improved Pressurized Pipe Network Hydraulic Solver for Applications in Irrigation Systems

GESTAR is an advanced computational hydraulic software tool specially adapted for the design, planning, and management of pressurized irrigation networks. A summary is given of the most significant characteristics of GESTAR. The hydraulic solver for quasi-steady scenarios uses specific strategies and incorporates several new features that improve the algorithms for pipe network computation, overcoming some of the problems that arise when attempting to apply drinking water software, using the gradient method, to irrigation systems. It is shown that the gradient method is a nodal method variant, where flow rates are relaxed using head loss formula exponents. Although relaxation produces a damping effect on instabilities, it is still unable to solve some of the numerical problems common to the nodal methods. In this contribution the results of the research on computational strategies capable of dealing with low resistance elements, hydrant modelling, multiple regulation valves, numerous emitters, and pumps with complex curves are presented, obtaining accurate results even in conditions where other software fails to converge. GESTAR incorporates all these computational techniques, achieving a high convergence rate and robustness. Furthermore, GESTAR’s solver algorithm was easily adapted to incorporate inverse analysis options for optimum network control and parameter calibration. Illustrative examples are provided, documenting the improved numerical techniques and examples of GESTAR’s performance in comparison with EPANET2, a widely used gradient method-based hydraulic solver.     

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.     

Head- and Flow-Based Formulations for Frequency Domain Analysis of Fluid Transients in Arbitrary Pipe Networks

Applications of frequency-domain analysis in pipelines and pipe networks include resonance analysis, time-domain simulation, and fault detection. Current frequency-domain analysis methods are restricted to series pipelines, single-branching pipelines, and single-loop networks and are not suited to complex networks. This paper presents a number of formulations for the frequency-domain solution in pipe networks of arbitrary topology and size. The formulations focus on the topology of arbitrary networks and do not consider any complex network devices or boundary conditions other than head and flow boundaries. The frequency-domain equations are presented for node elements and pipe elements, which correspond to the continuity of flow at a node and the unsteady flow in a pipe, respectively. Additionally, a pipe-node-pipe and reservoir-pipe pair set of equations are derived. A matrix-based approach is used to display the solution to entire networks in a systematic and powerful way. Three different formulations are derived based on the unknown variables of interest that are to be solved: head-formulation, flow-formulation, and head-flow-formulation. These hold significant analogies to different steady-state network solutions. The frequency-domain models are tested against the method of characteristics (a commonly used time-domain model) with good result. The computational efficiency of each formulation is discussed with the most efficient formulation being the head-formulation.     

Evolution Program for Layout Geometry of Rectilinear Looped Networks

This paper presents a new algorithm for the design of layout geometry of looped water distribution networks based on rectilinear grids. The algorithm is an evolution program based on genetic algorithms. It incorporates new methods for generating the initial population and performing the operations of crossover and mutation. The new methods overcome the problem of generating infeasible solutions that result when the commonly used genetic algorithm methods operate on solutions using the chosen coding scheme. This paper includes the results of tests that measure the effectiveness and computational effort of the new methods and a demonstration of the algorithm through application to an example problem.     

Design of Straight Expansive Pipe Transitions

Pipe transitions frequently occur in piping networks such as a water distribution system, or piping in a chemical engineering plant. Whereas the contraction transitions involve less head loss, the expansion transitions involve a sizable head loss. Considerable energy savings can be achieved if the transitions are designed by minimizing the head loss. The conservation of energy will be significant if the transition is meant for a steel-lined power tunnel where the flow velocities are high. Similarly, in a system having frequent occurrence of transitions, such as a high-rise building or plan of a city, the optimum transition profile can be used most effectively. This paper uses optimal control theory to develop a methodology for optimal design of expansion pipe transitions. Analyzing a large number of designed optimal transitions; empirical design equations for the pipe diameter profile are obtained.     

Thermal comfort in offices:comfort values and optimization of indoor control variables

In order to search for reasonable air-conditioned indoor control variables and save energy consumption and meet tO need of personal thermal comfort,a method which is based on numerical simulation is employed to optimize indoor control variables.Computational fluid dynamics(CFD)is used to describe thermal state of office.An optimal method is proposed in this paper,dual neural network model is firstly used to acquire reliable information,data from CFD model are pre-processed,and the remaining data are used to train artificial neural networks(ANN),then CFD model is replaced by ANN model to reduce computational cost when is optimized,indoor control variables are optimized by genetic algorithm.Simulation results show that indoor thermal comfort is improved obviously,and the energy cost is decreased accordingly.     

Parallel Computing Framework for Optimizing Construction Planning in Large-Scale Projects

Available construction optimization models can be used to generate optimal tradeoffs between construction time and cost, however their application in optimizing large-scale projects is limited due to their extensive and impractical computational time requirements. This paper presents the development of a parallel computing framework in order to circumvent this limitation. The framework incorporates a multi-objective genetic algorithm module that identifies optimal trade-offs between construction time and cost; and a parallel computing module that distributes genetic algorithm computations over a network of processors. The performance of the framework is evaluated using 150 experiments that represent various combinations of project sizes and numbers of processors. The results of this analysis illustrate the robust capabilities of the developed parallel computing framework in terms of its efficiency in reducing the computational time requirements for large-scale construction optimization problems, and its effectiveness in obtaining high quality solutions identical to those generated by a single processor.     

两级选址--路径问题的大规模邻域搜索模拟退火算法

针对目前越来越普遍的多级配送模式,建立以总成本最小为目标函数的两级选址-路径问题模型,并提出了大规模邻域搜索模拟退火算法进行求解.在模拟退火算法框架中,嵌入大规模邻域搜索过程,包含破坏、重组和局部搜索方法,从而进一步提高算法在解空间中构建邻域的范围.采用两级选址-路径问题标准算例对算法求解效果进行验证,并与标准模拟退火算法和国际已知最优解进行对比.结果显示,所建模型和算法正确有效,并且在求解大规模问题时算法能够取得相对更好的优化结果.