水利信息化项目设计概(估)算编制研究

为规范水利信息化项目设计概（估）算编制,合理确定投资,针对水利信息化项目多学科、多行业交叉融合的特点,通过分析国内外相关标准规定,调研典型水利信息化项目实际情况,侧重解决概（估）算编制的重难点问题。提出符合水利信息化实际的项目划分、费用组成与单价构成,并结合软、硬件费用编制的特点,给出相关计算方法,最后通过实例应用验证相关结论。结果表明：水利信息化项目有别于常规水利工程以土木工程为主的特点,是以附加值含量高的软硬件设施设备为主体的新型基础设施项目,设计概（估）算编制应注重合理确定软硬件设备价格,根据项目实际情况科学划分并计算相关费用,才能满足项目设计概（估）算阶段投资控制的需要。     

Web-based CBR system applied to early cost budgeting for pavement maintenance project

With the growth of transportation networks in developing countries, the cost-efficacy control of maintenance operations has become critical to the infrastructure asset management after highway construction. To effectively manage numerous projects annually with limited resources, it is necessary to accurately estimate costs and leave a trail of project information during the process of making maintenance project selection decisions. This paper outlines the development of a case-based reasoning (CBR) expert prototype system that compares historical data at the work item-level across the case library. This study attempts to determine preliminary project cost with readily available information rapidly based on previous experience of pavement maintenance related construction to assist decision makers in project screening and budget allocation. Various CBR modeling approaches were presented and assessed in terms of their mean absolute prediction error rates. Design and implementation of a web-based CBR system is demonstrated in this study to efficiently handle the attribute and case similarity computation and the results are displayed using browsers. Furthermore, weighting attributes employed in the CBR system were compared via eigenvector and equal weighting methods for estimating aggregate cost and component costs. Historical generic pavement maintenance projects were gathered from the Taiwan transportation agencies and used for model training and testing. Furthermore, k-fold cross-validation was employed to verify the CBR estimating system. The analytical results demonstrate the ability of the system to estimate the item-level cost of pavement maintenance projects with the satisfactory precision during the conceptual project phase. The developed prototype web-based CBR system can efficiently provide timely and accurate information in an efficient way and provide an alternative estimation tool that can be combined with other evaluation criteria, such as indexes of pavement serviceability and structure strength, to improve the decision making in relation to budget allocation.     

Establishing expert system for prediction based on the project-oriented data warehouse

Many studies have generated cost estimating relationships (CERs) for transportation projects via data analysis. Some studies collected data from databases, while others sourced data from conventional paper-based formats. When cost data were not in a consistent format, many studies failed to discuss the streamlining of pattern recognition, ranging from generating a problem statement, data warehouse and prediction modeling to information management. This study adopts a standard procedure of identifying CERs for transportation projects. For the proposed dimensional data warehouse, a pavement maintenance and rehabilitation project was selected as a case study for extracting data and concealed prediction rules. Linear and log-linear statistical approaches were adopted to create most advantageous models, defined based on their explanatory power and mean absolute prediction error. The resulting favorable estimation models created from the proposed cost data warehouse were integrated into an expert system to facilitate information management and generate preliminary budgets for transportation agencies.     

Preliminary cost estimates for thin-film transistor liquid–crystal display inspection and repair equipment: A hybrid hierarchical approach

The thin-film transistor liquid–crystal display (TFT-LCD) industry has developed rapidly in recent years. Because TFT-LCD manufacturing is highly complex and requires different tools for different products, accurately estimating the cost of manufacturing TFT-LCD equipment is essential. Conventional cost estimation models include linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent evidence that a hierarchical structure outperforms a flat structure, this study proposes a hierarchical classification and regression (HCR) approach for improving the accuracy of cost predictions for TFT-LCD inspection and repair equipment. Specifically, first-level analyses by HCR classify new unknown cases into specific classes. The cases are then inputted into the corresponding prediction models for the final output. In this study, experimental results based on a real world dataset containing data for TFT-LCD equipment development projects performed by a leading Taiwan provider show that three prediction models based on HCR approach are generally comparable or better than three conventional flat models (LR, ANN, and SVR) in terms of prediction accuracy. In particular, the 4-class and 5-class support vector machines in the first-level HCR combined with individual SVR obtain the lowest root mean square error (RMSE) and mean average percentage error (MAPE) rates, respectively.     

A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.     

A taxonomy of reasoning mechanisms and data synchronization framework for road excavation productivity monitoring

Project management tasks, such as productivity monitoring and cost estimation, require data to be fused from multiple data sources, which are typically spatial and temporal in nature. In order to fuse a pair of spatial and temporal data sources, a number of different types of reasoning mechanisms are needed. This paper presents a taxonomy of spatial and temporal reasoning mechanisms needed to fuse spatial and temporal data sources to support construction productivity monitoring. In addition, the paper also describes two different approaches (i.e., interpolation and nearest neighbor approaches) that can be used to synchronize the temporal and/or spatial data sources. The developed taxonomy has been validated based on representative queries of construction engineers and managers that are identified in previous research studies. The interpolation and nearest neighbor approaches have been validated with real and simulated construction data sources.     

An integrated data collection and analysis framework for remote monitoring and planning of construction operations

Recent advances in data collection and operations analysis techniques have facilitated the process of designing, analyzing, planning, and controlling of engineering processes. Mathematical tools such as graphical models, scheduling techniques, operations research, and simulation have enabled engineers to create models that represent activities, resources, and the environment under which a project is taking place. Traditionally, most simulation paradigms use static or historical data to create computer interpretable representations of real engineering systems. The suitability of this approach for modeling construction operations, however, has always been a challenge since most construction projects are unique in nature as every project is different in design, specifications, methods, and standards. Due to the dynamic nature and complexity of most construction operations, there is a significant need for a methodology that combines the capabilities of traditional modeling of engineering systems and real time field data collection. This paper presents the requirements and applicability of a data-driven modeling framework capable of collecting and manipulating real time field data from construction equipment, creating dynamic 3D visualizations of ongoing engineering activities, and updating the contents of a discrete event simulation model representing the real engineering system. The developed framework can be adopted for use by project decision-makers for short-term project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities.     

协同过率在工作量估算中的应用

工作量估算是软件项目管理的重要内容之一,协同过滤是一种在历史数据中确定相似用户或物品产生推荐的方法,已成功的应用于电子商务、影视推荐等多个领域,本文将协同过滤技术应用于软件工作量的估算。首先对历史项目集中的数值型数据进行归一化,然后采用均值对缺失值进行插补,余弦用于计算项目的相似度,最后确定项目的近邻集对待评估项目的工作量进行估算。从USP05-FT中选择了4个项目作为实例来说明该估算过程,估算结果与实际值有一些偏差是由于协同过滤仅能处理数值型数据。     

基于改进AHP和SVR的油田产能建设项目综合后评价模型

根据油田产能建设项目后评价的特点以及吉林油田的实际情况,详细分析油田产能建设项目后评价中的评价指标、各指标间的关系以及对最终综合后评价的影响,提出了一种基于改进层次分析法（AHP）和支持向量机回归（SVR）的综合后评价模型. 利用改进AHP法确定综合后评价指标体系中各个评价指标权重,然后通过SVR进行综合后评价,以提评价结果的精确度. 该模型不仅弥补了人为主观估计权重的缺陷,并且考虑到各评价指标对综合后评价结果的影响,科学并客观的对产能建设项目进行综合后评价,实验结果验证了该方法的有效性.     

基于公共交通智能卡数据的可视化分析

针对现有公共交通数据的可视分析方法很难在不同空间粒度下对乘客时空分布、客流时空分布、区域间客流时序变化进行多任务分析的问题,设计实现了一个多视图融合的可视化分析系统。该系统结合城市公共交通的智能卡数据、车辆GPS数据、地铁和公交线路信息,利用出行链路模型和基于出行时空特征的回归模型完成了乘客起讫点（origin-destination,OD）推断;然后,设计了层次聚类的地图可视化方法,结合了融合方位信息的玫瑰图和动态对比堆叠折线流图来分析各区域间的客流时序特点、关联关系;最后,利用真实的深圳市公共交通数据的可视分析结果验证了系统的有效性。     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

跨组织数据的软件成本估算方法

软件成本估算一直是软件项目管理的重要部分。经过半个多世纪的研究和工业实践,成本估算方法、模型得到了极大的丰富。这些方法、模型也衍生出了各种成本估算工具。但是,成本估算方法和模型的基础是历史项目数据。没有历史项目数据的公司和组织只能利用其他公司或组织的数据来进行自己项目的成本估算。如何利用跨组织数据进行有效的成本估算成为更具现实意义的问题。针对这一问题,提出了一种有效利用跨组织数据进行成本估算的方法,并通过实验说明了方法的有效性。     

Statistical analysis of deviation of actual cost from estimated cost using actual project data

This paper analyzes an association of a deviation of the actual cost (measured by person-month) from the estimated cost with the quality and the productivity of software development projects. Although the obtained results themselves may not be new from the academic point of view, they could provide motivation for developers to join process improvement activities in a software company and thus become a driving force for promoting the process improvement.We show that if a project is performed faithfully under a well-organized project plan (i.e. the plan is first constructed according to the standards of good writing, and then a project is managed and controlled to meet the plan), the deviation of the actual cost from the estimated one becomes small. Next we show statistically that projects with small deviation of the cost estimate tend to achieve high quality of final products and high productivity of development teams. In this analysis, the actual project data on 37 projects at a certain company are extensively applied.     

Feature weighting heuristics for analogy-based effort estimation models

Software cost estimation is one of the critical tasks in project management. In a highly demanding and competitive market environment, software project managers need robust models and methodologies to accurately predict the cost of a new project. Analogy-based cost estimation is one of the widely used models that rely on historical project data. It checks the similarity of features between past and current projects, and it approximates current project cost from past ones. One shortcoming of analogy-based cost estimation is that it assumes all project features as equal. However, these features may have different impacts on project cost based on their relevance. In this research, we present two feature weight assignment heuristics for cost estimation. We assign weights to the project features by benefiting from a statistical technique, namely principal components analysis (PCA) that is used for extracting optimal linear patterns of high dimensional data. We test our proposed heuristics on public datasets and conclude that the prediction performance in terms of MMRE and Pred(25) increases with a statistical-based assignment technique rather than random assignment approach.     

Hierarchy probability cost analysis model incorporate MAIMS principle for EPC project cost estimation

Two new types of hierarchy probability cost analysis (HPCA) model incorporating money allocated is money spent (MAIMS) principle based on definite work breakdown structure (WBS) level for EPC (engineering, procurement and construction) projects are presented. The proposed models have skillfully solved dilemma to appropriate cost elements and maximize the efficiency of information for cost risk analysis. Macroscopic and microscopic risk analysis of the project cost elements are introduced for meaningful model input. The illustration of an actual bidding EPC project substantiates that proposed integrating HPCA-hierarchy MAIMS models have demonstrated effective and viable for EPC projects.     

Quantifying IT estimation risks

A statistical method is proposed for quantifying the impact of factors that influence the quality of the estimation of costs for IT-enabled business projects. We call these factors risk drivers as they influence the risk of the misestimation of project costs. The method can effortlessly be transposed for usage on other important IT key performance indicators (KPIs), such as schedule misestimation or functionality underdelivery. We used logistic regression as a modeling technique to estimate the quantitative impact of risk factors. We did so because logistic regression has been applied successfully in fields including medical science, e.g. in perinatal epidemiology, to answer questions that show a striking resemblance to the questions regarding project risk management. In our study we used data from a large organization in the financial services industry to assess the applicability of logistic modeling in quantifying IT risks. With this real-world example we illustrated how to scrutinize the quality and plausibility of the available data. We explained how to deal with factors that cannot be influenced, also called risk factors, by project management before or in the early stage of a project, but can have an influence on the outcome of the estimation process. We demonstrated how to select the risk drivers using logistic regression. Our research has shown that it is possible to properly quantify these risks, even with the help of crude data. We discussed the interpretation of the models found and showed that the findings are helpful in decision making on measures to be taken to identify potential misestimates and thus mitigate IT risks for individual projects. We proposed increasing the auditing process efficiency by using the found cost misestimation models to classify all projects as either risky projects or non-risky projects. We discovered through our analyses that projects must not be overstaffed and the ratio of external developers must be kept small to obtain better cost estimates. Our research showed that business units that report on financial information tend to be risk mitigating, because they have more cost underruns in comparison with business units without reporting; the latter have more cost overruns. We also discovered a maturity mismatch: an increase from CMM level 1 to 2 did not influence the disparity between a cost estimate and its actual if the maturity of the business is not also increased.     

An information fusion approach for filtering GNSS data sets collected during construction operations

Global Navigation Satellite Systems (GNSS) are widely used to document the on- and off-site trajectories of construction equipment. Before analyzing the collected data for better understanding and improving construction operations, the data need to be freed from outliers. Eliminating outliers is challenging. While manually identifying outliers is a time-consuming and error-prone process, automatic filtering is exposed to false positives errors, which can lead to eliminating accurate trajectory segments. This paper addresses this issue by proposing a hybrid filtering method, which integrates experts’ decisions. The decisions are operationalized as parameters to search for next outliers and are based on visualization of sensor readings and the human-generated notes that describe specifics of the construction project. A specialized open-source software prototype was developed and applied by the authors to illustrate the proposed approach. The software was utilized to filter outliers in sensor readings collected during earthmoving and asphalt paving projects that involved five different types of common construction equipment.     

基于WebGIS的水文基本建设项目管理平台设计与实现

针对水文基本建设项目空间跨度大、设备占比高、行业特殊性强的特点,利用 WebGIS 和物联网等技术,开发基于 WebGIS 的水文基本建设项目管理平台。在分析设计理念的基础上,采用 B/S 模式,设计包含采集传输层、数据管理层、应用支撑层、业务服务层 4 层内容的总体架构,通过信息展示、跟踪管理、信息查询和系统管理等服务功能,实现从规划设计、项目实施、工程验收到运行管理的全生命周期智能化管理,突出水文建设项目的行业特点,满足智能化管理的要求,实现水文建设信息的实时共享。平台在工程进度实时统计分析、仪器设备全过程管理及档案资料规范化管理等应用场景上取得明显效果,可有效提升水文基本建设项目的管理效率和信息化水平。     

Architecture analysis of enterprise systems modifiability: a metamodel for software change cost estimation

Enterprise architecture models can be used in order to increase the general understanding of enterprise systems and specifically to perform various kinds of analysis. The present paper proposes a metamodel for enterprise systems modifiability analysis, i.e. assessing the cost of making changes to enterprise-wide systems. The enterprise architecture metamodel is formalized using probabilistic relational models, which enables the combination of regular entity-relationship modeling aspects with means to perform enterprise architecture analysis. The content of the presented metamodel is validated based on survey and workshop data and its estimation capability is tested with data from 21 software change projects. To illustrate the applicability of the metamodel an instantiated architectural model based on a software change project conducted at a large Nordic transportation company is detailed.     

Managing uncertainty in project portfolio cost estimation

Although typically a software development organisation is involved in more than one project simultaneously, the available tools in the area of software cost estimation deal mostly with single software projects. In order to calculate the possible cost of the entire project portfolio, one must combine the single project estimates taking into account the uncertainty involved. In this paper, statistical simulation techniques are used to calculate confidence intervals for the effort needed for a project portfolio. The overall approach is illustrated through the adaptation of the analogy-based method for software cost estimation to cover multiple projects.