Workshop: Transforming Civil & Environmental Engineering Curriculum: Big Data, Machine Learning, and AI Innovations Impact on CEE Education; Alexandria, Virginia; 10 December 2

在过去的几年里，机器学习、大数据和人工智能的研究和开发展示了许多机会，可以创造出对几乎每个行业和经济部门都具有持久和变革性影响的新概念、应用程序、工具和系统。随着土木工程和环境工程师采用新的计算机技术，计算机化的数据变得越来越容易获得。通过开发框架和算法来支持获取、建模、管理和分析这些不同的面向基础设施的数据，存在大量机会来利用这些海量数据并从中提取知识。随着传感器无处不在，土木工程和环境工程领域正在经历我们生活和旅行所处的物理和自然系统的变革性变化。这些传感器产生的海量(大数据)为一系列以前只能想象的可能性提供了巨大的机会。因此，土木工程师和环境工程师传统上依赖于小数据(通过实验、调查和实验室等产生)。在利用新产生的大数据充分利用机遇方面面临新的挑战。本次研讨会将召集来自大学、行业和土木工程与环境工程、计算机科学、机器学习、数据分析和人工智能领域的不同利益相关者，制定行动方案，推动土木工程与环境工程(CEE)课程的变革，并为CEE社区迎接无处不在的传感器、物联网设备以及由此产生的海量数据带来的机遇和挑战做好充分准备。工作坊将致力于为中东欧部门和研究界带来以下具体影响：(1)深入了解土木工程和环境工程系目前在大数据、机器学习和人工智能方面的专业知识；(2)提高对土木工程和环境工程中与大数据、数据分析、机器学习和人工智能相关的不同专业领域的新兴教育需求的了解；(3)指导土木工程和环境工程系根据当前的专业需求调整课程；(4)为专门针对土木工程与环境工程课程需求的机器学习、大数据和人工智能教程模块的未来发展提供领导和指导。在更广泛的层面上，讲习班的成果将成为将先进计算技术带入土木工程和环境工程以及其他工程学科的灯塔，支持新课程的开发。研讨会将提供开发所需工具的路线图和领导力，这些工具将增强未来工程毕业生的能力，提高工程专业的竞争力，并改善工程教育。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Over the last several years, research and development on Machine Learning, Big Data, and Artificial Intelligence has demonstrated many opportunities for creating novel concepts, applications, tools and systems with lasting and transformative impacts on nearly every industry and economic sector. As Civil and Environmental Engineers adopt new computer technologies, computerized data are becoming more and more available. There exist numerous opportunities to exploit and extract knowledge from this vast amount of data through the development of frameworks and algorithms that support the acquisition, modeling, management, and analysis of such diverse infrastructure-oriented data. With sensors everywhere, the field of Civil and Environmental Engineering is undergoing transformative changes in the physical and natural systems we live in and travel through. The massive (big) data generated from these sensors presents tremendous opportunities for a wide range of possibilities that could only be imagined before. Consequently, Civil and Environmental engineers who have traditionally relied upon small data (generated through experiments, surveys, and labs etc.) face novel challenges in utilizing the newly-generated big data to fully exploit opportunities. This workshop will convene a diverse group of participants from universities, industry and stakeholders in civil and environmental engineering, computer science, machine learning, data analytics and artificial intelligence to chart a course of action to enable the transformation of Civil and Environmental Engineering (CEE) curricula and fully prepare the CEE community for both the opportunities and challenges brought by sensors everywhere, internet of things devices, and the massive amount of resulting data. The workshop will seek to deliver the following specific impacts to CEE departments and to the research community: (1) Provide insight into the current availability of expertise on big data, machine learning, and artificial intelligence within Civil and Environmental Engineering departments; (2) Enhance understanding of the emerging educational needs for different areas of expertise within Civil and Environmental Engineering related to big data, data analytics, machine learning, and artificial intelligence; (3) Guide Civil and Environmental Engineering departments in aligning their curriculum with current needs for the profession; (4) Provide the leadership and guidance for the future development of machine learning, big data, and artificial intelligence tutorial modules specifically aligned to Civil and Environmental Engineering curriculum needs. At a broader level, it is intended that the workshop output will serve as a beacon for bringing advanced computing techniques to Civil and Environmental Engineering, as well as other Engineering disciplines, supporting novel curriculum development. The workshop will provide a roadmap and the leadership for the development of the required tools that will enhance the capabilities of future engineering graduates enhancing competitiveness of the engineering profession and improving engineering education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.