基于遗传算法的建筑工程三大目标综合优化

工期、成本和质量是建筑工程的三大主要目标,他们能否顺利实现将直接影响项目总体目标的好坏,而以往提出的模型大都是侧重于对工期一成本两目标的综合优化,很少涉及质量目标,这显然是满足不了现实要求的。因此在这里提出了一个对三大目标综合优化的模型,它是在工期一成本、工期一质量为线性关系的基础上建立的,并且以综合目标函数作为适应度函数建立的依据,运用遗传算法进行求解。最后用一个实例验证了此模型的可行性,并且运算之后可以得到多组解,决策者可以根据自己的偏好选择自己满意的一组解。     

Optimizing net present values of risk avoidance for mountain railway alignments with seismic performance evaluation

Railway alignment optimization in earthquake-prone mountainous (EPM) regions should quantify and trade off construction investments and seismic risks. Unfortunately, slight attention has been previously devoted to this trade-off. To this end, based on the FEMA-P58 methodology, a net present value (NPV) model of risk avoidance is presented and solved. In the model, alignment alternatives are first segmented into structural groups with different probabilistic seismic fragility curves, which are then used to generate structural repair cost and repair time curves. Afterward, a probabilistic seismic hazard curve is introduced to estimate the expected annual repair cost and time for computing railway direct and indirect seismic losses. Hence, the railway total annual loss caused by seismic activity can be obtained. Next, a benefit–cost analysis is performed to combine construction cost and seismic loss as the risk-cost NPV. To optimize this objective function, a particle swarm algorithm is used as the basic approach. For implementing the probabilistic seismic performance analysis, a Monte Carlo simulation (MCS) is employed as the risk assessment module. Furthermore, due to the computationally intensive nature of MCS, a CPU-based parallelization is embedded into the algorithm to expedite the search. Finally, the proposed model and method are applied to a representative real-world railway case in an EPM region. Their effectiveness is discussed and verified in five experiments, including algorithm convergence analysis, alignment solution comparison, seismic risk interpretation, computational efficiency test, and a specific sensitivity analysis.     

基于模糊规划的工程项目工期—成本—质量均衡优化研究

在工程项目的实施过程中,由于存在技术、天气、人员等诸多不确定因素的影响,导致工期、成本、质量这三大目标均具有模糊性。由于工期、成本、质量是相互关联的矛盾统一体,对这3 个不确定目标约束进行均衡优化,也就成为工程项目管理的重要内容之一。根据可信性理论,建立了多模式工期—成本—质量均衡优化的模糊期望值模型;设计了模糊模拟、神经网络和多目标粒子群算法相结合的混合智能算法来求解模型;并通过一个工程实例验证了所构建的多模式工期-成本-质量均衡优化模型的合理性和混合智能算法对该类问题求解的有效性。     

Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm

Minimizing both project time and cost is an important matter in today's competitive environment. Therefore trade-off between project time and cost is necessary. In projects, each activity can be started at different time points, depending on its precedence relationship and resource availability. Also cost and duration of the activities could be changed depending on the allocated resources. In addition, another strategy that affects the project total time and cost is resource leveling, which is applied to reduce excessive fluctuations in the resource usage. In this paper multi-mode resource-constrained project scheduling problem (MRCPSP), discrete time–cost trade-off problem (DTCTP) and also resource allocation and resource leveling problem (RLP) are considered simultaneously. This paper presents the multi-mode resource-constrained discrete time–cost-resource optimization (MRC-DTCRO) model in order to select starting the time and the execution mode of each activity satisfying all the project constraints. To solve these problems, non-domination based genetic algorithm (NSGA-II) is employed to search for the non-dominated solutions considering total project time, cost, and resources moment deviation as three objectives. The results of MRC-DTCRO model presented in this paper show that adding the resource leveling capability to the previously developed multi-mode resource-constrained discrete time–cost trade-off problem (MRC-DTCTP) models provides more practical solutions in terms of resource allocation and leveling, which makes this research applicable to both construction industry and researchers.     

基于微粒群算法的工程项目质量、费用和工期综合优化

进度、费用和质量称为工程项目的三大控制目标,三者之间相互依存、相互影响。工程项目控制的理想状态是同时实现合理的工期、较低的费用和较高的质量。微粒群算法(PSO)是新近出现的一种仿生算法,具有简单容易实现,而且随机搜索的优点,使得搜索不易陷于局部最优。将该算法引入工程项目优化领域,研究工程项目的质量、费用和工期的综合优化问题。系统介绍微粒群算法原理、流程以及算法的改进发展,研究工程项目质量、费用和工期的优化,并建立质量、费用和工期的多目标综合优化模型,介绍应用微粒群算法编码解决工程项目多目标优化的方法步骤。最后,通过一个应用实例,计算表明微粒群算法可以准确快速地解决工程项目多目标优化问题。     

Reliability analysis on civil engineering project based on integrated adaptive simulation annealing and gray correlation method

Dynamic reliability is a very important issue in reliability research. The dynamic reliability analysis for the project is still in search of domestic and international research in the exploration stage. By now, dynamic reliability research mainly concentrates on the reliability assessment; the methods mainly include dynamic fault tree, extension of event sequence diagram and Monte Carlo simulation, and et al. The paper aims to research the dynamic reliability optimization. On the basis of analysis of the four quality influence factors in the construction engineering, a method based on gray correlation degree is employed to calculate the weights of factors affecting construction process quality. Then the weights are added into the reliability improvement feasible index (RIFI). Furthermore, a novel nonlinear programming mathematic optimization model is established. In the Insight software environment, the Adaptive Simulated Annealing (ASA) algorithm is used to get a more accurate construction subsystem optimal reliability under different RIFI conditions. In addition, the relationship between construction quality and construction system reliability is analyzed, the proposed methods and detailed processing can offer a useful reference for improving the construction system quality level.     

Cost–safety trade-off in unequal-area construction site layout planning

Cost and safety are two key elements when designing a good construction site layout planning (CSLP). Previous research works always considered CSLP from the aspect of reducing cost and treated SCLP as a single objective optimization problem. In the paper, CSLP was designed by a multi-objective optimization (MOO) model using modified Pareto-based ant colony optimization (ACO) algorithm, which could find a Pareto solution (trade-off layout) to fulfill the requirement of reducing cost and improve the site safety level simultaneously. Furthermore, in order to apply MOO model to solve unequal-area problem, the random grids-recognition strategy was employed in the proposed MOO model to solve the unequal-area site layout problems without increasing the computational complexities. A case study of a residential building project is used to validate the proposed MOO model and the results are very positive.     

Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis

Multi-objective optimization for designing of a small-scale distributed CCHP system has been performed. Small-scale combined cooling, heating, and power generation technologies represent a key resource to increase generation efficiency and reduce greenhouse gas emissions with respect to conventional separate production means. In the multi-objective optimization of the small-scale distributed CCHP system, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms however this objective has not been integrated with the thermoeconomic objective. The thermodynamic modeling has been implemented comprehensively while the economic analysis conducted in accordance with the total revenue requirement (TRR) method. One of the most suitable optimization techniques namely as genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. In the present work, reliability and availability are introduced in the thermoenvironomic model of the system, so that redundancy is embedded in the optimal solution. Risk analysis is used for decision-making of the final optimal solution from the obtained Pareto optimal frontier.     

Multi-objective genetic optimization for scheduling a multi-storey building

Multi-objective optimization problems can be found in various fields, however, optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. This paper proposes developed Genetic Algorithms (GAs) that enabled multi-objective optimization for scheduling a multi-storey building. However, a trade-off between time and cost for habitation projects is required to face limited fund and increasing population environment. This problem has an important position for developing countries' governments because it's related to low cost habitation projects. Multi-storey buildings are classified as special repetitive projects because of skeleton constraints. Activities can be classified into: repetitive and non-repetitive ones. The present model enables construction planners to direct controlled time–cost construction plan by investigating optimal plans, generated from a set of feasible alternatives, which minimize project duration, total number of crews, and total work interruptions. This model satisfies the following aspects: 1) it is based on the line of balance technique; 2) it considers non-serial typical activity networks with finish–start relationship and both lag and overlap time between activities are allowed; 3) it utilizes a tool to identify activities in a typical repeated floor; and 4) it is developed as a spreadsheet template that is easy to use. Details of the model with visual charts are presented. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the scheduling of a multi-storey building.     

基于Petri 网的工程项目多目标协同优化研究

工程项目建设过程需要对项目的进度、质量和成本这三大目标进行集成控制和有效管理。但是,同时实现三大目标的集成控制需要考虑各种约束变量的限制,探讨建立三个目标的相关函数以及进度、质量和成本集成控制机理,并建立多目标协同优化模型,提出工程项目三大目标协同优化的普遍适用性方法。模型结合Petri 网的实时性和动态性,采用粒子群优化算法对模型进行求解,结合实证分析了该模型的可行性和有效性,为工程项目的建设提供决策支持。     

基于遗传算法质量工期成本多目标优化研究

本文应用遗传算法及系统可靠度理论 ,建立了基于遗传算法的质量、工期、成本的多目标优化模型 ,并编制了相应的程序 ,使建筑工程领域的进度控制、成本控制和质量控制这三大控制目标达到了协调统一     

The development of work performance analysis system

The purpose of this study is to develop the web-based system for work performance analysis (WPAS). The need of work performance analysis system has already been suggested in many previous researches on the computerization of the performance measurement in the construction site by using the indicators such as time, cost and quality.However, they had focused on measuring or analyzing the result when the project would be over. The WPAS suggests three indicators — work reliability, work effectiveness and work efficiency — to manage the performance of the construction site. It can help the manager more easily recognize the status of on-going work in the construction site by measuring and analyzing the work reliability rate, the work effectiveness and work efficiency every day.     

Automated trade‐off between time and cost in planning repetitive construction projects

An automated model is developed to support the optimization of the planning and scheduling of repetitive construction projects. The model provides the capability of optimizing two important objectives commonly sought in scheduling repetitive construction projects: minimizing project duration; and minimizing project cost. The model performs this multi‐objective optimization using a genetic algorithm approach. The output of the model is a set of optimal solutions that represent the trade‐off between time and cost in planning repetitive construction projects. Furthermore, the model can be utilized to find a single scheduling solution that provides the minimum overall project cost by simply adding project indirect cost to the obtained project direct cost for each of the obtained scheduling solutions on the Pareto optimal curve. Other important time‐related costs are also considered in the model including: early completion incentives, late completion penalties and lane rental costs. Providing the planners of repetitive construction projects with an automated set of optimal time–cost trade‐off solutions should contribute to cost‐effective and speedy delivery of this type of construction project. An application example is analysed to illustrate the use of the model and demonstrate its capabilities in generating optimal trade‐off solutions between minimizing the project time and cost for repetitive construction projects.     

A multi-attribute model for construction site layout using intuitionistic fuzzy logic

Most researchers have concentrated on studying optimization models to produce optimal construction site layout plans using different algorithms, while the overall method for evaluating and selecting the best site layout generated from optimization models has received less attention. In an optimization model, construction cost is generally considered in the objective function. However, several objectives, such as security and tie-in with external transportation, are difficult to quantify in the objective function and were not considered in previous studies. This paper focuses on evaluating and selecting the construction site layout considering qualitative objectives. An intuitionistic fuzzy multi-attribute decision-making model is developed that combines intuitionistic fuzzy set theory and the technique for order preference by similarity to the ideal solution (TOPSIS). This model overcomes the shortcomings of a traditional fuzzy set when describing ambiguous and unclear circumstances by using membership functions. The application of this model for site layout selection is shown to be reasonable and effective based on data from a real construction project.     

建设项目目标规划模型研究及实证分析

工程建设研究领域越来越重视工程项目目标最优化的研究,动态规划方法被广泛的运用于工程项目目标管理研究,以动态控制的思想设计和实施工程投资、进度和质量控制系统,丰富和发展了项目目标管理理论,对提高我国项目管理水平具有重要意义。本文运用动态规划最优控制理论,结合建设工程项目自身的特点,研究工程项目的质量、工期、进度规划的最优化目标,建立了项目目标管理的动态优化模型和目标偏差控制模型,并进行实证分析,验证了模型的可行性。     

工程项目工期—成本—质量—安全水平综合优化研究

目前工程项目多目标优化的相关研究中,较少考虑工期、成本、质量等目标与安全水平之间的联系。针对这一不足,在分析工序安全水平影响因素的基础上,得到了成本—安全水平优化模型,并将安全目标引入工期—成本—质量优化模型,构建了工期—成本—质量—安全水平均衡优化模型。为了有效求解该型,引入了量子粒子群算法,结合工程实例进行模拟仿真,得到工期、成本、质量与安全水平的一系列 Pareto 解,并进行方案决策,结果表明得到的 Pareto 解集能为项目管理者的决策提供有效支持,量子粒子群优化算法在求解该模型方面是有效性的。     

塑料板排水法处理软土地基中排水板布置方案的优化设计

本文以塑料排水板打设深度和间距为决策变量 ,塑料排水板板材和打设费用最低为目标函数 ,建立了塑料排水板布置方案优化设计的数学模型。依据优化设计的原理提出简化的求解方法。本文的研究表明 :采用优化设计的方法可节省塑料排水板板材和打设费用 ,加快工程设计进程。     

基于耦合评价模型的深基坑支护优化设计

利用信息熵和模糊层次分析法的基本原理,提出深基坑支护优化设计的耦合评价模型。该方法充分考虑优化过程中安全可靠性、造价、环境影响和工期等诸多指标因素。通过信息熵所反映指标本身的效用值来修正主观权重,建立了耦合评价模型,克服传统优化方法权重随意性的缺点。最后通过实例分析,验证了耦合评价模型的合理性和可靠性。     

结合 BIM 与驾驭式计算的施工协同管理方法研究

BIM 技术是解决工程项目施工过程中协同性问题的重要方法,结合驾驭式计算的建设项目施工协同管理方法能够进行实时可视化并能对计算仿真过程进行动态驾驭。通过分析驾驭式计算和 BIM 结合对于施工协同管理的重要作用,利用BIM 理论、工程协同管理理论以及多目标优化理论建立了基于 IFC 的多目标优化模型,并设计了驾驭式计算、协同进化算法和 BIM 的结合方案用来实现对多目标优化模型的计算。构建了施工阶段的协同管理模型来整合多目标优化流程以及施工各参与方的协同工作流程,该施工协同模型能够有效地实现施工阶段各个参与方之间的协同工作、对于多目标的协同优化以及对于过程的实时可视化与实时控制     

钢筋混凝土框架梁基于可靠度理论的抗震优化设计

基于现行建筑抗震设计规范 ,把设计变量 (荷载、材料强度和几何尺寸 )看作随机变量 ,针对钢筋混凝土框架梁进行优化设计。目的是使设计的梁在满足现行规范要求的基础上 ,能保持一致的可靠性 ,同时使设计造价达到最小。文中可靠度分析和优化计算都基于Monte Carlo随机模拟方法。