Intelligent decision making and knowledge management in engineering project management procedures

工程项目管理流程中的智能决策和知识管理

• 批准号: 491877-2015
• 负责人: McCabe, Brenda
• 金额: $ 2.81万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618042
• 关键词: Intelligent; decision; making; knowledge; management

Most industries have standard values that are applied to cost estimates to reflect uncertainty early in the life of a project. Much research has focused on our failure to properly reflect real project conditions and to accurately estimate costs, schedules, and risks, especially for infrastructure and industrial megaprojects. A study of 63 international mining and smelting projects suggested that one out of thirteen projects had cost overruns 100% or higher. Public infrastructure projects typically fair worse. This project aims to develop a cognitive framework in which data and expert knowledge are integrated to provide support during the process of estimating capital expenditure and contingency costs early in the project phase with the goal of improving cost performance. Cognitive computing refers to computer systems built to simulate human thinking and reasoning using artificial intelligence (AI). Improving the estimates for expenditures and contingencies could have a major positive impact on the well-being and profitability of megaprojects. The systematic and accurate estimation of capital expenditures and contingencies could identify and help mitigate risk factors that drive up costs, enhance financing of the project for the client, and create higher reliability and competency in the marketplace.

大多数行业都有适用于成本估算的标准值，以反映项目生命周期早期的不确定性。许多研究都集中在我们未能正确反映真实的项目条件并准确估计成本、进度和风险，特别是对于基础设施和工业大型项目。对 63 个国际采矿和冶炼项目的研究表明，十分之一的项目成本超支 100% 或更高。公共基础设施项目通常情况更糟。该项目旨在开发一个认知框架，将数据和专家知识相结合，为项目早期估算资本支出和应急成本的过程提供支持，以提高成本绩效。认知计算是指使用人工智能（AI）模拟人类思维和推理的计算机系统。改进支出和意外事件的估计可能会对大型项目的福祉和盈利能力产生重大积极影响。对资本支出和意外事件的系统而准确的估计可以识别并帮助减轻导致成本上升的风险因素，增强客户的项目融资，并在市场上创造更高的可靠性和竞争力。