A study of the factors affecting construction durations in Hong Kong

This is the second phase of an investigation into the significant factors influencing construction duration of projects in Hong Kong. The results of the first phase led to the conclusion that larger samples were justified to investigate further the discerned relationships. Expanded samples were obtained in this second phase by adding some reported data from Hong Kong projects to the original surveyed sample. The second phase of this study also further investigates the relationships between different project characteristic variables such as the construction duration, construction cost, total gross floor area and the number of storeys in the case of buildings. Moreover, a case study on plant utilization level and site labour productivity was carried out on a building site to explore the ‘micro-factors’ that affect construction durations. The findings are of importance to all construction industry participants as the derived models help to estimate the construction duration of a project on the basis of significant macro project parameters. Additionally, the results of the case study indicate the contribution of significantly variable site productivity levels to overall construction duration and suggest an agenda for future investigations. A third phase of this study is planned incorporating more detailed data collection and analysis of significant factors, as well as international comparisons where possible.     

建筑工程项目工期的影响因素与预测模型

工期目标直接影响工程项目实施效果，但是影响工程项目工期的因素众多，很难准确预测一个合理的工期。本文基于上海市2008~2015年212个建筑工程项目的实践数据，首先识别出影响项目工期的主要因素，包括总造价、总建筑面积、总层数、檐高、基础开挖深度、建筑用途、结构类型等，以及派生因素如标准层面积、单位面积造价等，然后使用多元线性回归方法得到满足假设检验条件的初步模型。经过全面对比分析，发现以对数建设速度作为工期模型的因变量更加精确。通过调整不同影响因素，得到不同预测模型。然后通过评价不同假设检验，选择最佳工期预测模型。最后验证该预测模型在不同条件变化下的稳定性和有效性，通过广泛讨论不同影响因素，提出优化后的工期预测模型和应用建议。     

基于BAS-SCA-BP模型的高校建筑工程项目投资估算研究

针对标准BP神经网络建筑工程项目投资估算模型收敛速度慢、预测精度低的问题,提出融合改进天牛须和正余弦双重优化算法（BAS-SCA）优化BP神经网络的建筑工程项目投资估算模型。以某市高校建筑工程项目为研究对象,分析相关文献并结合显著性理论初步选择工程造价影响因子,利用粗糙集属性约简算法筛选出关键因素;基于此,通过构建基于BAS-SCA-BP的神经网络估算模型实现快速、准确的建筑工程投资估算。研究结果表明：基于BAS-SCA-BP的估算模型较标准BP神经网络估算模型的估算精度有了大幅提高,与其他智能算法改进的BP神经网络估算模型的性能相比较,该模型在稳定性和预测精度方面表现更佳。     

风险分析方法在建设项目造价控制中的应用

本文针对建设项目造价控制提出了一种风险分析方法,阐述了风险分析的概念及其应用于建设项目造价控制的基本思路、方法和具体步骤,分析了建设项目主要的风险因素、风险评估体系、主要风险解决方案以及采取的风险对策。利用这种方法可以有效地预测建设项目潜在的造价风险,并准确控制项目最终造价水平,积极主动地防止造价偏差,从而将工程造价控制在一个可预测的水平。风险分析方法可以用在项目实施的准备阶段及项目施工阶段直至工程竣工,贯穿于项目建设的全过程,而且是一个动态过程、定期监控过程。风险分析方法的要点在于风险的识别、评估、监控以及对策研究。风险分析方法在建设项目造价控制中的应用前景十分广泛。     

Discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty and its application to Jinping-II Hydroelectric Project

This paper presents a discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty. A multi-objective decision making model is established in which the total project duration is regarded as a fuzzy variable. To deal with the uncertainty, the fuzzy numbers in the model are defuzzified by using an expected value operator with an optimistic–pessimistic index. The objective functions are to minimize the total project cost, project duration, crashing cost, and environmental impact. Furthermore, a fuzzy-based adaptive-hybrid genetic algorithm is developed to find feasible solutions. The one-point crossover and repairing strategy for mutations are designed to avoid infeasible solutions. Finally, the Jinping-II Hydroelectric Project is used as a practical example to demonstrate the practicality and efficiency of the model. Results and a sensitivity analysis are presented to highlight the performance of the optimization method, which proves to be very effective and efficient compared to other algorithms.     

改进GM(1,1)模型在建筑工程现金流预测中的应用

准确的建筑工程现金流预测是提高建筑行业投资和融资决策效果、获得稳定收益的前提,合理的现金流预测方法是确保预测结果准确的关键。引入灰色预测和"1/4:1/3"现金流预测理论,提出基于权重因子的背景值构造,并以实地收集的10余个建筑工程项目为样本,采用黄金分割法和对半分割法对GM(1,1)模型进行改进,以提高GM(1,1)模型在建筑工程现金流预测实践中的适用性和适应性。实证结果表明,改进后的灰色预测模型能够以较高精度预测建筑工程现金流,从而为公司投资、融资决策以及资金管理提供可靠依据。     

Impact of contractor's optimized financing cost on project bid price

The financing cost depends on the incoming and outgoing cash flow throughout the project, and can differ greatly from project to project. This study proposes a model that calculates the expected financing cost based on the cash flow forecast. This approach is more realistic than assuming an approximate percentage of the total cost. The proposed model calculates the bid price using an optimized financing cost that is obtained by selecting an optimum combination of available financing alternatives offered by different lenders. The proposed model minimizes financing cost, reduces the bid price, enhances the competitiveness of the bidder, increases the contractor`s negotiating power with a lender by providing an optimum financing schedule, and eliminates the risk of financing surprises during construction. This study investigates the impact of different financing considerations on bid price in three cases to prove the effectiveness of the proposed model.     

A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress

Abstract:   A methodology to forecast project progress and final time-to-completion is developed. An adaptive Bayesian updating method is used to assess the unknown model parameters based on recorded data and pertinent prior information. Recorded data can include equality, upper bound, and lower bound data. The proposed approach properly accounts for all the prevailing uncertainties, including model errors arising from an inaccurate model form or missing variables, measurement errors, statistical uncertainty, and volitional uncertainty .
As an illustration of the proposed approach, the project progress and final time-to-completion of an example project are forecasted. For this illustration construction of civilian nuclear power plants in the United States is considered. This application considers two cases (1) no information is available prior to observing the actual progress data of a specified plant and (2) the construction progress of eight other nuclear power plants is available. The example shows that an informative prior is important to make accurate predictions when only a few records are available. This is also the time when forecasts are most valuable to the project manager. Having or not having prior information does not have any practical effect on the forecast when progress on a significant portion of the project has been recorded .     

基于灰色系统理论的桩基工程造价预测研究

对桩基工程进行造价分析,将灰色系统理论应用于柱基工程的造价预测中;结合某桩基工程造价实例,以历史资料为依据建立灰色预测模型,对人工及主要材料价格进行分析预测;并采用小误差概率、标准差比以及相对误差作为预测模型的精度检验指标,评价预测结果的可靠性;最后,将预测结果与实际结果进行对比得出了有益结论;本文思路与方法可为同类工...     

Cost simulation in an item-based project involving construction engineering and management

Despite the extensive use of simulation in management, the continuous simulation model for cost estimation remains unexploited, especially for construction engineering and management. This study introduces streamlining Monte Carlo simulation procedures with evaluation of stochastic processes and input probability distribution selection via hypothesis testing, and specification of correlations between simulated variates. By using self-developed algorithms and a spreadsheet-add-on program, this investigation uses historical construction projects as case study data to create an early-stage cost distribution for budget allocation. While establishing the applicability of the proposed simulation procedures, this study demonstrates that the simulated cost results present superior simulation accuracy in addition to separating the principal work items and unit price component model. Generally, the precision and absolute error rates fall into acceptable ranges when the proposed systematic simulation procedures are adopted. The cost simulation approach offers a simplified decision tool for fairly assessing construction cost and uncertainties based on the experienced judgment of project managers.     

浅析灰色预测模型在建筑工程造价中的应用

针对建筑工程造价预测问题,提出了灰色模型方法,以工程造价历史资料为依据,建立了建筑工程造价的灰色预测模型,为建筑工程的工程招投标提供了依据,从而使工程投资达到预期目标。     

‘CHASTE’: construction hazard assessment with spatial and temporal exposure

CHASTE—‘Construction Hazard Assessment with Spatial and Temporal Exposure’—is a conceptual model that enables forecasting of safety risks in construction projects for different trades, at appropriate levels of detail and reliability for different planning windows and managerial purposes, in a highly automated fashion. Unlike earlier models, CHASTE explicitly accounts for the fact that construction workers are frequently endangered by activities performed by teams other than their own. The risks to which workers are exposed change through time, as the activities performed and the physical environment of construction sites change. CHASTE uses a knowledge base of construction activities and probabilities of loss‐of‐control events, coupled with a project’s construction plan and a digital building model, to forecast risk levels for work teams. It has been implemented in prototype software and tested on two projects.     

Construction equipment productivity estimation using artificial neural network model

Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well‐known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non‐linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network's inherent ability to capture non‐linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non‐linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.     

On using a communication-mapping model for environmental management (CMEM) to improve environmental performance in project development processes

This paper examines the existing environmental management communications practice among project participants in project development processes. There is a growing pressure for all project participants to extend their traditional business objectives of only lowering cost and shortening project duration to improve environmental performance. In achieving better environmental performance, it is essential to collect responses from project participants including project clients, consultants, contractors and various sub-contractors to build up collaboration. As this collaboration is based largely on communications among various parties, the validity and effectiveness of the communications are essential to enable effective collaboration. By investigating practical applications, this paper identifies gaps of communications among project participants, and shows that these gaps present barriers to the improvement of environmental performance in construction. Possible solutions to communication gaps are investigated, leading to the proposal of a communication-mapping model for environmental management (CMEM). The CMEM is proposed as a method aimed at effectively implementing environmental management in construction practice. Data used for this study are collected from six construction projects in Hong Kong. A free-flow mapping approach is also employed to assist the investigations. It is believed that this study can help improving communications among project participants and environmental performance in the construction industry.     

虚拟施工的BIM应用:三大高校合作项目

越来越多的设施业主要求项目组移交竣工BIM模型,以便用于建筑的运营和维护。根据项目要求的不同,业主可能将竣工模型建立与移交的责任指派给总承包商。因此,施工队伍的各成员不但越来越需要熟悉BIM建模软件的应用,还需熟悉利用可靠的设施信息支持完整BIM的必要业务流程。竣TBIM模型的创建,要求承包商首先要基于建筑师、工程师和顾问等设计师所提供的图纸和信息的基础上,创建建筑,结构和设备模型。这要求承包商的组织机构内要有一名人员具备各与专业设计模型相关的技能和知识。一旦设计模型被创建(或重新创建),承包商可将这些模型用于开发施工用的BIM模型。一个完整的施工模型通常包括:成本估算,施工进度计划、冲突检测与分析以及现场物流。业主对设施管理用竣工模型提出新增要求,包括添加运营和维护用的具体数据。本文所述项目旨在为了更好地了解,在为业主实施BIM的项目上,承包商对项目所起作用的复杂性。为实现此目标,三所大学合作建立了数个校际团队,从每所学校各抽调一名学生分配至三个团队的一个,充当BIM商业建筑项目BIM应用里的承包商角色。本文从两个方面汇报项目的挑战和细节:导师——项目组织、学习目标和策略,学生——项目组织、团队合作过程以及策略。     

Planning-stage estimation of highway project duration on the basis of anticipated project cost, project type, and contract type

At the planning phase of project development, highway agencies seek to estimate the time duration of project implementation for purposes such as construction planning, contract administration, and workzone impact assessments. This paper investigates the estimation of highway project duration on the basis of variables known at the planning phase such as planned cost and project type, and contract type. Project types are pavement construction, rehabilitation, maintenance, traffic facility installation, and bridge construction. The contract types considered are fixed-duration and fixed-deadline contracts. Using a variety of model specifications, the paper presents mathematical relationships between highway project duration and the magnitude of the planned cost and project type, and contract type. The paper shows that all other factors remaining the same, the duration of fixed-date deadline contracts generally exceed that of fixed-duration contracts; and higher levels of planned cost translate non-linearly into greater project duration. The developed models can help project administrators provide improved estimates of project duration and thus could help reduce project time delays. Also, the models are useful in forecasting workzone durations, a key aspect of workzone user cost estimation.     

Dynamic modeling of the quantitative risk allocation in construction projects

Allocation of construction risks between owners and their contractors has a significant impact on the total construction costs. This research presents an integrated fuzzy-system dynamics approach for quantitative risk allocation. All the factors affecting the risk allocation process are modeled using system dynamics approach. Fuzzy logic is integrated into system dynamics modeling structure to account for the existing uncertainties. The values of different factors that have an uncertain nature are determined by fuzzy numbers. The application of Zadeh's extension principle and interval arithmetic is proposed for the system dynamics to enable the system outcomes to be presented considering uncertainties in the input variables. Using the proposed integrated fuzzy-SD model, the project cost is simulated at different percentages of risk allocation. The optimum percentage of risk allocation is determined as a point in which the project cost is minimized. The performance of the proposed method is assessed by employing the method in a tunneling project.     

A catastrophe model of construction conflict behavior

Buildings are part of the built environment in which many activities are performed. One of the critical part of a development process is the physical construction of the proposed facility. As such an efficient construction process is invaluable. Moreover, due to the inherent divergence in interest, conflict among the contracting parties appears inevitable. Escalating conflict level may turn into psychological struggles between the contracting parties and manifests as dispute. The unfortunate outcomes are loss of productivity and increase in cost of construction. This paper describes the dynamic change in construction conflict behavior based on the catastrophe theory. How conflict behavior is affected by conflict level is first discussed. As such a catastrophe model of construction conflict behavior with tension level, behavioral flexibility as control variables is proposed. It is suggested that conflict is positively correlated to the tension level among the project team and subject to the moderating effect of the behavioral flexibility displayed by the project team members. The model suggests a sudden jump in conflict level will occur when tension reaches a threshold. Once this happens the conflict level will not subside even the tension level returns to the threshold just reached. The proposed model was tested by an empirical study that affirms: (1) The appropriateness of the use of tension and behavioral flexibility as control variables; (2) catastrophe model is a better fit to describe construction conflict behavior than the linear and logistic model; and (3) the bimodal nature of construction conflict behavior. The model reinforces the conventional wisdom of ‘prevention is better than cure’ as far as construction conflict resolution is concerned.     

A Petri net simulation model for virtual construction of earthmoving operations

A common and extended Petri net simulation framework for virtual construction of earthmoving operations is developed to simulate dynamic changes of workflow and information flow in the earthmoving construction process and illustrate the constraint relationship between various operational equipment and construction restrictions. The proposed framework considers factors that influence earthmoving operations including randomness of construction activities, individual preference of equipment scheduling, and constraint relationship between equipment and construction environment. With the given equipment availability and project indirect cost, the framework can predict construction situation, equipment utilization rate, estimated duration and cost to achieve visualized and intelligent scheduling of virtual construction process in earthmoving operations. The simulation process is conducted on the CPNTools platform. The data required by the research were collected on-site in an actual case. The randomness of construction activities in earthmoving operations and main factors influencing construction are simulated. The sensitivity analysis for the model is carried out. The study will provide technical support and a management basis for equipment scheduling of earthmoving operations.     

基于 SVR 的工程建设项目快速投资估算方法研究

在建设项目前期,如何快速而准确地估算工程项目的造价,对项目的投资决策具有很大的意义。针对传统造价估算 方法的不足之处,采用 SPSS 统计分析软件进行工程造价指标的相关性分析及指标体系选取,将之作为输入变量,使用真实 案例训练集样本训练 SVR 模型并进行仿真模拟预测。为了验证提出的 SVR 模型的有效性,引入 BP 人工神经网络来进行预 测结果的对比验证。结果表明,SVR 模型得到的预测值平均绝对百分比误差约为 5%,拟合优度 R2高达 0.97,远小于 BPNN 模型的预测误差 14%,即提出的 SVR 估算模型要比 BP 人工神经网络预测模型具有更良好的泛化能力,预测精度更高,因 此其在工程项目前期投资估算实践中具有一定的现实意义。