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DYNAMIC PERFORMANCE FORECASTING MODEL AND MEASUREMENT SYSTEM IN CONSTRUCTION PROJECT

机译:施工项目动态性能预测模型与测量系统

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Evaluating and predicting Key Performance Indicators (KPI) facilitates monitoring and controlling project progress. Limited work has been done in forecasting construction project performance using Key Performance Indicators. Developing key performance indicators that model the project performance over its life cycle provide useful management tools. This paper proposes a model to forecast the construction project performance and introduces a quantitative method to measure KPIs of projects dynamically. Key performance indicators assess different aspects of projects and are used as a thermometer to demonstrate the health status of projects. The first step in measuring project performance is defining the project objectives and clarifying from whose point of view the performance is measured. Then, indicators are measured by considering the profits and damages of the specific stakeholder. For quantitative KPI, mathematical calculations are applied and for qualitative indicators, a questionnaire is designed and used. The relative weight factors of each indicator are determined using Analytic Hierarchy Process (AHP). Subsequently, mathematical calculations are performed to get the project overall performance. This paper applies neuro-fuzzy technique 'to develop the model for forecasting construction project performance. The proposed method uses a set of key performance indicators to predict the project status at different time horizons. Then if the project has a major deviation, corrective actions are used to improve project performance. This model can be used in building construction projects to help decision makers evaluate and improve the performance of their projects.
机译:评估和预测关键绩效指标(KPI)有助于监视和控制项目进度。使用关键绩效指标来预测建筑项目绩效的工作量有限。制定对项目绩效进行生命周期建模的关键绩效指标,可以提供有用的管理工具。本文提出了一个预测建设项目绩效的模型,并提出了一种动态测量项目KPI的定量方法。关键绩效指标评估项目的各个方面,并用作温度计来证明项目的健康状况。衡量项目绩效的第一步是定义项目目标并阐明从谁的角度衡量绩效。然后,通过考虑特定利益相关者的损益来衡量指标。对于定量KPI,应用数学计算,对于定性指标,设计并使用调查表。每个指标的相对权重因子是使用层次分析法(AHP)确定的。随后,进行数学计算以获得项目的整体绩效。本文应用“神经模糊技术”来开发预测建筑项目绩效的模型。所提出的方法使用一组关键绩效指标来预测不同时间范围内的项目状态。然后,如果项目存在重大偏差,则可以使用纠正措施来提高项目绩效。此模型可用于建筑项目,以帮助决策者评估和改善其项目的绩效。

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