Intelligent decision making and knowledge management in engineering project management procedures

大多数行业都有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地反映真实的项目条件，并准确地估计成本、进度和风险，特别是对于基础设施和工业大型项目。一项对63个国际采矿和冶炼项目的研究表明，每13个项目中就有一个成本超支100%或更高。公共基础设施项目的情况通常更糟。该项目旨在开发一种认知框架，将数据和专家知识整合在一起，在项目早期阶段估算资本支出和应急成本的过程中提供支持，目标是提高成本绩效。认知计算是指使用人工智能（AI）来模拟人类思维和推理的计算机系统。改进对支出和突发事件的估计可能对大型项目的福利和盈利能力产生重大的积极影响。对资本支出和突发事件的系统和准确的估计可以识别并帮助降低导致成本上升的风险因素，为客户增加项目融资，并在市场上创造更高的可靠性和竞争力。

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.