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

该项目旨在实现网络破坏防御和物理可重构感测的互利互动，以实现互利和自适应的网络物理系统（CPS）。从生物学中的互利共生中汲取灵感，两个物种以一种对双方都有利的方式相互作用，网络和物理以一种同时受益于彼此并相互促进的方式相互作用，以增强CPS预测，重新配置和适应的能力。这种交互是可推广的，使其能够增强CPS在各个领域的应用。在民用基础设施系统领域，互利互动的CPS将允许重新配置一种类型的传感器，自适应地基于损坏统计学，以监测多种类型的基础设施损坏-从而通过减少所需传感器的类型和数量，同时最大限度地提高损坏评估和预测的及时性和准确性，提高多损坏基础设施监测的成本效益。实现具有成本效益的多重损害监测有望推动更安全、更具弹性和可持续的基础设施的发展，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的、跨学科的和包容性的工程参与（BPE）计划，吸引、培训和奖励下一代工程研究人员和从业人员，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，从而提高美国的经济、安全和福祉，从而促进NSF对扩大工程参与（BPE）的承诺。（1）数据驱动的、基于知识的深度学习方法，用于可推广的损伤统计学，以预测基础设施损伤的发生和传播，提供关于目标损伤的信息以通知可重构感测，（2）基于信号差最大化理论的可重构感测方法，以优化和物理控制传感器的配置，以主动寻求监测每个预测的目标损伤，提供损伤搜索反馈以告知损伤识别，以及（3）质量感知边缘云计算方法，用于从原始传感信号中高效和有效地提取损伤信息，作为损伤识别和可重构传感之间的桥梁。建议的CPS将测试在多损伤监测桥梁使用基于模拟和实际CPS原型，并将推广到监测其他民用基础设施的未来。建议的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NSF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.