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

回归分析产生简单的方程，以充分代表建筑工程与管理（CEM）中的真实的世界系统，其可以有效地应用于解决传统的“历史数据”问题以及与计算的快速发展有关的新兴的“大数据”问题（移动的、社交的、云）、传感器技术、建筑信息模型（BIM）底层的参数化设计数据库以及物联网。然而，回归一直未能赶上快速的技术进步和实际应用需求。 在真实的世界中，问题可能是最令人难以置信的，数据通常包含噪音或丢失的信息，而解决问题的方法应该是计算简单，快速校准，直接解释推理逻辑，并且容易在新数据可用时保持最新。为了使用户能够接受并真正有效，CEM中基于数据、分析驱动的决策支持系统的用户体验绝不能被认为是在敲“黑匣子”或需要进行大量的“试错”。 拟议的研究计划将加强基于线性回归的分析，以支持建模，预测和提高CEM的生产力和成本效益。在追求简单性的同时，研究将解决以下关键挑战：（1）如何提高基于线性回归的分析的复杂性和智能性，以便与CEM中最复杂的应用问题所固有的“非线性”相匹配？(2)如何通过选择最主要的输入特征来简化高维回归方程，同时保持模型的准确性？(3)如何通过分析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，可以普遍适用于“突破和征服”现实世界的应用问题，从而能够借给及时，有效的，定量的决策支持工程和管理专业人员在CEM。新的知识将大大丰富现有的CEM教育课程，在研究生和本科生水平上教授定量方法。拟议的研究计划将培养高素质的人才沿着提供改变游戏规则的解决方案，将在行业中产生重大影响。涉及数据驱动决策的其他相关领域也将受益于拟议的赠款。

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.