Convergence Accelerator Phase I (RAISE): Civil Infrastructure Systems Open Knowledge Network (CIS-OKN)

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。该融合加速器第一阶段项目重点关注通过创建民用基础设施系统开放知识网络(CIS-OKN)来恢复和改善美国国家城市基础设施的重大挑战。该系统将提供收集和分析基础设施数据的工具，以改进我们基础设施系统的评估、规划、设计、建设和运营。该项目包括一个由土木工程、数据科学、计算机科学和社会科学专家组成的多学科和多机构团队，并利用行业和政府合作伙伴关系来识别和利用必要的数据，以实现更安全、更高效和更具成本效益的美国基础设施建设、运营和维护。独联体-OKN有可能改变民用基础设施系统、决策者和利益攸关方与以前孤立和不同种类的数据互动和使用的方式。该项目旨在提高对导致基础设施恶化的因素的了解，并帮助决策者选择必要的操作并确定其优先顺序，以维持美国基础设施系统的可靠性和改善其可持续性。项目团队-S与行业和政府机构的战略合作伙伴关系应该使他们能够产生一个开放的知识网络，可以对民用基础设施系统实践产生影响，从而帮助满足对弹性和可持续基础设施系统的需求。数据分析和机器学习方面的最新进展创造了一个独特的机会，可以从过去和目前的情况中学习，以更好地评估和预测民用基础设施系统的条件和可持续性。通过链接、集成和分析现有的丰富数据，CIS-OKN将促进与建设、维护和投资决策相关的洞察。研究目标是开发和测试一个开放、共享、公共的网络基础设施，用于定位和访问来自多个来源和不同格式的民用基础设施系统数据，以促进知识发现、预测性分析和数据驱动的决策。这项工作将包括从语义上理解不同类型的数据彼此之间以及与物理基础设施组件之间的内容和关系；从非结构化数据源中提取有关基础设施系统状况的信息；将来自不同来源、不同格式、具有不同技术和描述性细节的数据链接并融合为统一的知识表示；以及为这些不同类型的数据开发综合的查询、推理和学习工具。独联体-OKN具有实现数据驱动发现的新模式的潜力，将孤立的数据转换为能够跨学科和机构边界进行综合数据分析的形式，潜在地增强了民用基础设施系统领域和外部的创新。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。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. This Convergence Accelerator Phase I project focuses on the grand challenge of restoring and improving the U.S. national urban infrastructure by creating a Civil Infrastructure Systems Open Knowledge Network (CIS-OKN). This system will provide tools to assemble and analyze infrastructure data that can improve evaluation, planning, design, construction, and operation of our infrastructure systems. The project includes a multidisciplinary and multi-institutional team of civil engineering, data science, computer science, and social science experts and leverages industry and government partnerships to identify and harness the necessary data to enable safer, more efficient, and cost-effective construction, operation, and maintenance of U.S. infrastructure. The CIS-OKN has the potential to transform the way civil infrastructure systems decision-makers and stakeholders interact with and use what were previously isolated and heterogeneous data. The project seeks to improve understanding of the factors contributing to infrastructure deterioration and to help decision-makers select and prioritize the operations necessary to maintain the reliability and improve the sustainability of the U.S. infrastructure system. The project team?s strategic partnerships with industry and government agencies should enable them to produce an open knowledge network that can have an impact on civil infrastructure systems practices and thereby help meet the need for resilient and sustainable infrastructure systems. Recent advances in data analytics and machine learning have created a unique opportunity to learn from past and current conditions to better assess and predict the conditions and sustainability of civil infrastructure systems. By linking, integrating, and analyzing the wealth of data that exist, the CIS-OKN will facilitate insights relevant to construction, maintenance, and investment decision-making. The research objective is to develop and test an open, shared, public cyberinfrastructure for locating and accessing civil infrastructure systems data from multiple sources and in heterogeneous formats to facilitate knowledge discovery, predictive analytics, and data-driven decision making. The effort will include semantically understanding the content and relationships of different types of data to each other and to the physical infrastructure components; extracting information about infrastructure system conditions from unstructured data sources; linking and fusing data from disparate sources, in different formats, and with different levels of technical and descriptive detail into a unified knowledge representation; and developing integrated querying, reasoning, and learning tools for these heterogeneous types of data. The CIS-OKN has the potential to enable new modes of data-driven discovery, transforming isolated data into forms that enable integrative data analytics across disciplines and institutional boundaries, potentially enhancing innovation across the civil infrastructure systems domain and beyond.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.