Reliability based optimal design of a municipal water supply pipe network

This paper intends to provide a detailed methodology on the development of the optimization model including reliability for design of water distribution networks using genetic algorithms (GA) and EPANET in the MATLAB environment. The coding developed is easy to use and also flexible. A new parameter is proposed to determine the overall network reliability using network nodal demands and their corresponding satisfaction indices and is incorporated in the optimization model. The proposed methodology is tested on a standard Hanoi network. After several runs and out of many optimal solutions obtained, the solution with a total cost of 7.78 million associated with a reliability of 0.7323 is considered as the best and the corresponding results are presented and compared with previous research.     

Generalized SDOF system for seismic analysis of concrete rectangular liquid storage tanks

This paper presents a simplified method using the generalized single degree of freedom (SDOF) system for seismic analysis and design of concrete rectangular liquid storage tanks. In most of the current design codes and standards for concrete liquid storage tanks, the response of liquid and tank structures is determined using rigid boundary conditions for the determination of hydrodynamic pressures. Also, the lumped mass approach is used for dynamic analysis. However, it has been shown that the flexibility of a tank wall increases the hydrodynamic pressures as compared to the rigid wall assumption. On the other hand, the consistent mass approach reduces the response of liquid containing structures as compared to the lumped mass approach. In the proposed method, the consistent mass approach and the effect of flexibility of a tank wall on hydrodynamic pressures are considered. The prescribed vibration shape functions representing the first five mode shapes for the cantilever wall boundary condition are studied. The application of the proposed shape functions and their validity are examined using two different case studies including a tall and a shallow tank. The results are then compared with those using the finite element method from a previous investigation and ACI 350.3 commonly used in current practice. The results indicate that the proposed method is fairly accurate which can be used in the structural design of liquid containing structures. It is also concluded that the effect of the second mode should also be considered in the dynamic analysis of liquid containing structures.     

体育场主看台悬挑屋盖的风荷载计算

采用数值模拟方法并结合风洞模型试验数据,对一平屋面和一斜屋面的体育场主看台悬挑屋盖的表面风压进行了计算和分析,对屋盖上下表面的风压分布规律进行了总结和归纳。在此基础上,提出了两种屋面情况的屋盖风荷载简化分布图线和计算方法,并与国外相关规范建议的公式作了比较。该简化计算方法可直接供同类结构的抗风设计参考和应用。     

AQUIFER PARAMETER ESTIMATION USING GENETIC ALGORITHMS AND NEURAL NETWORKS

Computational tools such as genetic algorithms and neural networks are becoming increasingly popular in scientific applications involving mathematical modeling. These tools emulate natural biological processes in an attempt to build more robust and efficient mathematical models. The present study explores the applicability of genetic algorithms and neural networks for aquifer parameter estimation, in an optimization framework. Although optimization models based on genetic algorithms are more robust than conventional nonlinear programming techniques, they often necessitate many computationally expensive function evaluations. On the other hand, genetic algorithms can also tolerate approximate function evaluations. The present study employs artificial neural networks that provide quick but reasonably accurate function evaluation, in conjunction with genetic algorithms. Such an optimization framework makes the resulting calibration model highly robust and efficient. Applicability of the proposed model is demonstrated on a hypothetical aquifer using synthetic test data. Through an extensive sensitivity analysis, the present study reiterates that a low probability of mutation (0.02-0.03) and a moderately high probability of crossover (0.6-0.7) are essential for good convergence of a genetic optimization model.     

Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate model

This paper presents a combined method based on optimized neural networks and optimization algorithms to solve structural optimization problems. The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce the number of computations for structural analysis. First, the OANN is trained appropriately. Subsequently, the main optimization problem is solved using the OANN and a population-based algorithm. The algorithms considered in this step are the arithmetic optimization algorithm (AOA) and genetic algorithm (GA). Finally, the abovementioned problem is solved using the optimal point obtained from the previous step and the pattern search (PS) algorithm. To evaluate the performance of the proposed method, two numerical examples are considered. In the first example, the performance of two algorithms, OANN + AOA + PS and OANN + GA + PS, is investigated. Using the GA reduces the elapsed time by approximately 50% compared with using the AOA. Results show that both the OANN + GA + PS and OANN + AOA + PS algorithms perform well in solving structural optimization problems and achieve the same optimal design. However, the OANN + GA + PS algorithm requires significantly fewer function evaluations to achieve the same accuracy as the OANN + AOA + PS algorithm.     

Shortening the search time in optimization of water distribution networks

Various optimization techniques have been implemented for minimizing the costs associated with water distribution networks (WDNs). In this regard, meta-heuristic algorithms have represented the highest efficiency. One of the weaknesses of these algorithms is their high computational costs, which make their implementation sometimes impracticable for optimization of large real life WDNs. In this article an optimization model based on the ant colony optimization algorithm is presented for least cost design of WDNs. In the proposed model, ants select pipe diameters so that the energy loss per unit length of pipes will be in a specific range. In this model, the number of objective function evaluations is small. Two sample networks have been optimized using the proposed model. Obtained results show that the model presented in this article has a very low search time, which makes its implementation possible for large real-life WDNs.     

Project scheduling with limited resources using a genetic algorithm

This paper presents a genetic algorithm for the Resource-Constrained Project Scheduling Problem (RCPSP). In comparison with previous genetic algorithms proposed in literature for this problem, this paper proposes an alternative representation of the chromosomes using a multi-array object-oriented model in order to take advantage of programming features in most common languages for the design of decision support systems. The approach was tested on sets of standard problems taken from the literature and freely available on the Internet (PSPLIB). Computational results validate the effectiveness of the proposed algorithm and show that our procedure equals most of previous results with less computational time.     

Investigation of sustainable and resilient design alternatives for water distribution networks

Nowadays there is a pronounced interest in the need for sustainable and resilient infrastructure systems to address the challenges of future infrastructure development. This paper presents a three-objective optimization model for the investigation of various sustainable and resilient design alternatives for water distribution networks. Although optimal design of water distribution networks is a thoroughly studied area, most researchers focused on efficient algorithms to solve the complex design problem. Cost has predominantly been the objective in previous studies with few models also considering resilience or environmental impacts (CO₂ emissions). This research combined all these parameters in a multi-objective model to obtain various sustainable and resilient design alternatives. The model is demonstrated on a three-loop benchmark network that was previously studied. The tradeoff between the objectives is further analyzed to identify the most beneficial solution from the pareto-optimal set of solutions. The dollar worth of enhancing resilience of the benchmark network by a unit is estimated to be in the range of $1.23 to $3.93 million, which translates to about 22% to 69% of the least possible life cycle cost. The results of this research reveal that resilience can be increased by paying a justifiable cost that will also compensate for CO₂ emissions.     

琅琊山抽水蓄能电站地下厂房 开挖位移的智能反演

 基于正交设计、差分法、神经网络以及遗传算法，对琅琊山抽水蓄能电站地下厂房的开挖位移进行智能反演。参考实测的地质资料，按照正交设计的要求对岩石和岩脉分别选取不同的参数和水平，利用FLAC3D差分程序计算得到相应的神经网络分析样本；进行神经网络训练，利用遗传算法的寻优特性获取最佳反演参数；将所得参数代入FLAC3D差分程序计算，最后所得结果与现场监测基本吻合。     

基于自适应神经-模糊推理系统和遗传算法的桥梁耐久性评估

将模糊推理、神经网络、遗传算法三种技术有机融合在一起,建立了基于自适应神经-模糊推理系统(ANFIS)和遗传算法(GAS)的桥梁耐久性评估专家系统。该系统将专家的模糊推理过程蕴含于神经网络结构中,使神经网络的节点和权值具有明确的物理意义,避免了传统神经网络工作过程的“黑盒”性。同时该系统又具有神经网络的自适应性和学习能力,克服了传统模糊推理系统学习能力差的缺点。而且采用遗传和反向传播相结合的GA-BP混合算法训练网络,充分发挥了遗传算法的全局搜索性和BP的局部微调快速性的优点。并以辽宁省13座桥300根梁的实测数据对其进行训练和测试,系统输出与期望输出吻合较好,证明该专家系统性能稳定、有效,具有实用价值。     

Comparing Delay Minimization and Emissions Minimization in the Network Design Problem

Abstract: Traditionally, transportation road networks are designed for minimal congestion. Unfortunately, such approaches do not guarantee minimal vehicle emissions. To fill this apparent gap in network design research, an emissions network design problem and solution method is proposed in this article for the purposes of comparing to the traditional network design results. Three air pollutants are considered on two road networks. The model is formulated as a bi‐level optimization problem and a solution is approximated using a genetic algorithm. The influence of demand uncertainty is also incorporated into the model. Designing for minimal congestion tends to increase emissions of criteria air pollutants. However, not adding capacity to a road network also increases emissions of pollutants. Therefore, an optimization problem and solution method, such as the emissions network design problem and solution method presented here, is useful for identifying capacity additions that reduce vehicle emissions. It is also useful for understanding the tradeoffs between designing a network for minimal congestion versus minimal vehicle emissions.     

Optimal planning of distributed generation with application of multi-objective algorithm including economic,environmental and technical issues with considering uncertainties

This paper proposes a stochastic multi-objective model for integration of distributed generations (DGs) in distribution networks. The proposed model determines the optimal location and size of DGs by optimising different objective functions dependently and simultaneously subject to the operating constraints. If proper sizes of DGs are located in suitable sites and are also managed properly they can improve integrity, reliability and efficiency of the system. Regarding the widespread impact of uncertainties, some strategies must be devised in order to incorporate them well into power system modelling and hence achieve the best possible strategy to be adopted which its characteristics keep closer to reality. The most important uncertainties in network planning are load forecasting and market price errors. The proposed scheme is solved using non-dominated sorting genetic algorithms II, allowing the distribution company (DisCo) to exercise his/her personal preferences. To validate the effectiveness of the proposed scheme, simulations are carried out on a 33-bus distribution network and finally the attained results are discussed.     

Study of lateral earth pressures on nonyielding retaining walls with deformable geofoam inclusions

This paper proposes a method to predict the lateral earth pressures on nonyielding retaining walls with geofoam inclusions. The previous study of the lateral stress-strain relation of the backfill was extended, and the solution was derived by the iterative method. The proposed solution could be applied without the known value of the compression of the geofoam inclusions. Model tests for nonyielding retaining walls with expanded polystyrene (EPS) geofoam were also conducted to investigate lateral earth pressures. The accuracy of the proposed solution was verified by comparison to test data in the absence of surface loading. The proposed solution was also validated by a previous study of laboratory-scale model tests with surface loading as well as numerical simulations for field-scale applications with a vehicle load. Furthermore, the effect of the density and thickness of EPS on the reduction of lateral earth pressures was discussed, and appropriate design parameters of EPS were suggested for nonyielding retaining walls with EPS geofoam.     

EXPLICIT CALCULATION OF WATER QUALITY PARAMETERS IN PIPE DISTRIBUTION SYSTEMS

Explicit algorithms have been developed for use in water quality studies and management of distribution networks with sources of dissimilar quality. The proposed algorithms can be effectively used for directly determining a variety of blended water quality parameters for a given set of network loading and operational conditions. The parameters include the spatial distribution of constituent concentrations, the flow influence characteristics of supply sources, and the water age distribution throughout the system. The developed algorithms are formulated analytically from mass balance relationships as contingent linear boundary value problems in conjunction with a topological sorting methodology. The resulting formulations yield coefficient matrices, of order equal to the number of junction nodes, that are triangular and positive definite. The parameter solutions sought are, thus, derived through a direct application of a one step substitution process. The proposed algorithms are both robust and efficient, and guaranteed to yield mathematically consistent solutions. Moreover, the algorithms are simple to understand and implement, and are well suited to the needs of those practicing engineers who wish to write and run their own programs. Two example applications are presented. Enhancement of distribution system water quality management is a principal benefit of the algorithms.     

Minimum Weight Design of Large Structures on a Network of Workstations

Abstract: Advances in performance of local area networks and microprocessors are providing significant computational capability at a relatively low cost. This paper is concerned with development of a distributed algorithm for minimum weight design of large structures on a network of workstations using biologically inspired genetic algorithms. Communication constructs from the software library Parallel Virtual Machine (PVM) have been used for message passing between the workstations. The algorithm has been applied to minimum weight design of two example structures. Performance estimates are provided based on the granularity and parallelization efficiency of the distributed model. The speedup of the distributed algorithm increases with the size of the structure, making it particularly suitable for optimization of large structures. For large structures, a high average speedup of about 10 is achieved using 11 workstations. The high scalability of the distributed genetic algorithm demonstrates that a cluster of workstations provides a cost-effective alternative for high-performance computing for coarse-grained applications such as the GA-based structural optimization.     

Comparison of algorithms for seismic topology optimisation of lifeline networks

A performance comparison among five optimisation algorithms for the topology design of lifeline network subjected to earthquakes is presented in this study. The topology optimisation model in conjunction with the argument of seismic reliability is firstly introduced for the design of lifeline networks subjected to earthquakes. Subsequently, two quite standard optimisers used in the numerical optimisation, i.e. a genetic algorithm (GA) and a simulated annealing algorithm, are investigated. Their hybrid scheme, entitled a simulated annealing GA that combines the advantages of two standard optimisers, is introduced as well. Besides, an ant colony algorithm and a particle swarm algorithm that have been developed in recent years are explored. Three modelled lifeline networks, including two benchmark networks and one actual network, are used as the numerical carriers that the five optimisation algorithms accommodate. It is concluded that the simulated annealing GA provides an excellent tool with higher efficiency to achieve optimal topology of lifeline networks.     

The pre-consolidation pressure of a laterite soil

The pre-consolidation pressure of a soil is often determined in the oedometer using the Casagrande method. Recently, the trend has been to carry out this determination using the ‘Constrained Modulus’ method. The relationships between the constrained moduli, the normal pressures and the pre-consolidation pressures of temperate-zone soils have also been established. These relationships are investigated for an undisturbed tropical soil—an amphibolite derived laterite soil in the fixed ring oedometer. The pre-consolidation pressures as well as the over-consolidation ratios thus obtained are evaluated along with those obtained using the Casagrande method. The values of the pre-consolidation pressures obtained using the Casagrande and the ‘Constrained Modulus’ methods gave means of 3,8×10² and 3,5×10² KN/m² respectively. The respective coefficients of variation are 0.40 and 0.58. A test of the significance of the difference of the means shows no significance at the 1% level on the Student-t distribution. The constrained modulus method is however recommended for determination of pre-consolidation pressures of laterite soils in view of its advantages over the other method.     

遗传算法在土钉支护结构优化设计中的应用

以土钉支护的极限平衡设计方法为基础 ,用遗传算法作为计算工具 ,提出了土钉支护的优化设计方法 ,并对计算过程进行了编程。工程实例计算表明 ,该方法是进行土钉优化设计的有效方法。     

Optimal Layout of Bridge Trusses by Genetic Algorithms

In this paper we present an approach to the layout and shape-optimization problem of bridge truss structures using genetic algorithms. The objective is to find an optimal layout design that will have minimum weight or material volume, subject to performance constraints related to member stresses, joint displacements, and member buckling. An automated two-stage optimization search process, which integrates structural analysis by finite-element method, genetic algorithms, and cognitive topology patterns (domain knowledge), is developed to solve the optimal problem. Two examples concerning bridge truss structure are investigated to demonstrate the effectiveness of the proposed method in solving these layout-optimization problems.