Collaborative Research: CPS: Medium: Mutualistic Cyber-Physical Interaction for Self-Adaptive Multi-Damage Monitoring of Civil Infrastructure

该项目旨在为互惠和自适应网络物理系统（CPS）实现网络损害预测和物理可重构感知的互惠互动。从生物学中的互惠主义中获得灵感，两个物种以一种互利的方式相互作用，网络和物理以一种同时受益并相互贡献的方式相互作用，以增强CPS预测、重新配置和适应的能力。这种交互是一般化的，允许它增强各个领域中的CPS应用程序。在民用基础设施系统领域，互惠互动CPS将允许重新配置单一类型的传感器，自适应地基于损害预测，监测多种类型的基础设施损害，从而通过减少所需传感器的类型和数量，提高多损害基础设施监测的成本效益，同时最大限度地提高损害评估和预测的及时性和准确性。实现具有成本效益的多重损害监测有望实现更安全、更有弹性和可持续基础设施的跨越式发展，这将刺激经济增长和社会福利，造福国家和人民。该项目还将通过开发创新、跨学科和包容性的BPE项目，为NSF扩大工程（BPE）参与的承诺做出贡献，以吸引、培训和奖励下一代工程研究人员和实践者，他们是CPS技术的有能力的创造者，而不仅仅是被动的消费者，从而增强美国的经济、安全和福祉。设想的CPS包括三个集成组件：(1)基于数据驱动、基于知识的广义损伤预测深度学习方法，用于预测基础设施损伤的发生和传播，为可重构感知提供目标损伤信息；(2)基于信号差异最大化理论的可重构感知方法，用于优化和物理控制传感器的配置，以主动寻求监测每一个预测的目标损伤；(3)质量感知边缘云计算方法，从原始传感信号中高效提取损伤信息，作为损伤预测和可重构传感之间的桥梁。建议的CPS将使用基于模拟和实际的CPS原型在桥梁的多重损伤监测中进行测试，并将在未来推广到监测其他民用基础设施。所提出的CPS方法有可能改变我们设计、创建和操作CPS的方式，使下一代CPS具有更强的预测能力、可重构性和适应性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to enable mutualistic interaction of cyber damage prognostics and physical reconfigurable sensing for mutualistic and self-adaptive cyber-physical systems (CPS). Drawing inspiration from mutualism in biology where two species interact in a way that benefits both, the cyber and the physical interact in a way that they simultaneously benefit from and contribute to each other to enhance the ability of the CPS to predict, reconfigure, and adapt. Such interaction is generalizable, allowing it to enhance CPS applications in various domains. In the civil infrastructure systems domain, the mutualistic interaction-enabled CPS will allow for reconfiguring a single type of sensor, adaptively based on damage prognostics, to monitor multiple classes of infrastructure damages – thereby improving the cost-effectiveness of multi-damage infrastructure monitoring by reducing the types and number of sensors needed and maximizing the timeliness and accuracy of damage assessment and prediction at the same time. Enabling cost-effective multi-damage monitoring is promising to leapfrog the development of safer, more resilient, and sustainable infrastructure, which would stimulate economic growth and social welfare for the benefit of the nation and its people. This project will also contribute to NSF’s commitment to broadening participation in engineering (BPE) by developing innovative, interdisciplinary, and inclusive BPE programs to attract, train, and reward the next-generation engineering researchers and practitioners who are capable creators of CPS technology and not only passive consumers, thereby enhancing the U.S. economy, security, and well-being.The envisioned CPS includes three integrated components: (1) data-driven, knowledge-informed deep learning methods for generalizable damage prognostics to predict the onset and propagation of infrastructure damages, providing information about target damages to inform reconfigurable sensing, (2) signal difference maximization theory-based reconfigurable sensing methods to optimize and physically control the configurations of the sensors to actively seek to monitor each of the predicted target damages, providing damage-seeking feedback to inform damage prognostics, and (3) quality-aware edge cloud computing methods for efficient and effective damage information extraction from raw sensing signals, serving as the bridge between damage prognostics and reconfigurable sensing. The proposed CPS will be tested in multi-damage monitoring of bridges using simulation-based and actual CPS prototypes, and would be generalized to monitoring other civil infrastructure in the future. The proposed CPS methods have the potential to transform the way we design, create, and operate CPS to enable the next-generation CPS that have greater predictive ability, reconfigurability, and adaptability.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.