Enhancing Regression-based Analytics for Addressing Applied Research Needs in Construction Engineering & Management (CEM)

增强基于回归的分析，以满足建筑工程的应用研究需求

• 批准号: RGPIN-2016-04687
• 负责人: Lu, Ming
• 金额: $ 1.97万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615242
• 关键词: Enhancing; Regression; based; Analytics; Addressing

Regression analysis results in simple equations to sufficiently represent the real world systems in Construction Engineering & Management (CEM), which can be effectively applied to tackle conventional “historical data” problems as well as emerging “big data” problems in connection with rapid developments in computing (mobile, social, cloud), sensor technologies, parametric design databases underlying Building Information Models (BIM), and the Internet of Things. Yet, regression has not been able to catch up with rapid technology advances and practical application needs. In the real world, problems can be most mind-boggling, and the data often contain noises or missing information, while the problem-solving methods are expected to be computationally simple, fast to calibrate, straightforward to explain the reasoning logic, and easy to keep current as new data become available. In order to be acceptable and truly effective, user experiences of data-based, analytics-driven decision support systems in CEM must not be perceived as tapping a “black box” or requiring much “trial and error”. The proposed research program will enhance linear regression based analytics in support of modeling, prediction and improvement of productivity and cost performances in CEM. In parallel to the pursuit of simplicity, the research will address following crucial challenges: (1) how to enhance the sophistication and intelligence of linear-regression-based analytics so as to match up with the “non-linearity” native to most complicated application problems in CEM? (2) How to streamline high-dimensional regression equations by selecting the most dominant input features while retaining model accuracy? (3) How to define uncertainties associated with point-value predictions by analytically characterizing model prediction errors? The ultimate goal is to develop a systematic, scientific framework that can be generally applied to “break and conquer” real-world application problems, thus being capable to lend timely, effective, and quantitative decision support for engineering and management professionals in CEM. New knowledge to be created will substantially enrich existing CEM education curricula in regards to teaching quantitative methods on both graduate and undergraduate levels. The proposed research program will train highly qualified personnel along with delivering game-changing solutions that will make significant impact in the industry. Other related areas involving data-driven decision making will also benefit from the proposed grant.

回归分析导致简单方程式可以充分代表建筑工程与管理中的现实世界系统（CEM），可以有效地用于解决传统的“历史数据”问题，以及与计算快速发展（移动，社交，云），传感器技术，参数设计数据库的“大数据”问题出现的“大数据”问题。但是，回归无法赶上快速技术的进步和实际应用需求。 在现实世界中，问题可能是令人难以置信的，数据通常包含噪音或丢失的信息，而问题解决方法的计算简单，快速校准，直接校准，可以简单地解释推理逻辑，并且随着新数据的可用性而易于保持最新。为了获得可接受且真正有效的，CEM中基于数据的，基于分析的决策支持系统的用户体验不得被视为敲击“黑匣子”或需要太多的“反复试验”。 提出的研究计划将增强基于线性回归的分析，以支持CEM中生产率和成本表现的建模，预测和提高。与对简单性的追求同时，该研究将解决以下挑战：（1）如何增强基于线性回归的分析的社会化和智能，以与CEM中最复杂的应用程序问题相匹配？ （2）如何通过在保持模型精度的同时选择最主的输入特征来简化高维回归方程？ （3）如何通过分析表征模型预测误差来定义与点值预测相关的不确定性？ 最终目标是开发一个系统的，科学的框架，通常可以应用于“打破和征服”现实世界中的应用问题，从而有能力为CEM的工程和管理专业人员提供及时，有效和定量的决策支持。要创建的新知识将大大丰富现有的CEM教育课程，以在研究生和本科级别的教学定量方法方面。拟议的研究计划将培训高素质的人员，并提供改变游戏规则的解决方案，从而对行业产生重大影响。涉及数据驱动决策的其他相关领域也将从拟议的赠款中受益。