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Federal and state departments of transportation, the US Army Corps of Engineers, electric utility companies, and other decision-makers need accurate and timely information about the condition of infrastructure to prioritize investment decisions. Currently, there are no broadly applicable automated tools to provide timely information about structural health. Artificial intelligence (AI) provides a forward-looking perspective to conceptualize and implement a data-driven and physics-informed structural health monitoring (SHM) strategy to overcome some of the challenges in traditional approaches. In September 2020, the National Science Foundation funded a project to demonstrate the proof-of-concept of an AI-driven SHM platform. The project team interacted with potential end-users and decision-makers to identify important aspects to consider in an AI-driven SHM platform. This paper summarizes the feedback received from the stakeholders and presents the project's preliminary results that serve as proof of concept. 

ABSTRACT: This paper explores the use of cyber-physical systems (CPS) for optimal design in wind engineering. The approach combines the accuracy of physical wind tunnel testing with the ability to efficiently explore a solution space using numerical optimization algorithms. The approach is fully automated, with experiments executed in a boundary layer wind tunnel (BLWT), sensor feedback monitored by a high-performance computer, and actuators used to bring about physical changes in the BLWT. Because the model is undergoing physical change as it approaches the optimal solution, this approach is given the name “loop-in-the-model” testing. The building selected for this study is a low-rise structure with a parapet wall of variable height. Parapet walls alter the location of the roof corner vortices, alleviating large suction loads on the windward facing roof corner and edges and setting up an interesting optimal design problem. In the BLWT, the model parapet height is adjusted using servo-motors to achieve a particular design. The model surface is instrumented with pressure taps to measure the envelope pressure loading. The taps are densely spaced on the roof to provide sufficient resolution to capture the change in roof corner vortex formation. Experiments are conducted using a boundary BLWT located at the University of Florida Natural Hazard Engineering Research Infrastructure (NHERI) Experimental Facility. The proposed CPS approach enables the optimal solution to be found quicker than brute force methods, in particular for complex structures with many design variables. The parapet wall provides a proof-of-concept study with a single design variable that has a non-monotonic influence on a structure’s wind load. This study focuses on envelope load effects, seeking the parapet height that minimizes roof and parapet wall suction loading. Implications are significant for more complex structures where the optimal solution may not be obvious and cannot be reasonably determined with traditional experimental or computational methods. KEYWORDS: Cyber-physical systems, optimization, boundary-layer wind tunnel, parapet wall, NHERI