ABSTRACT

This Faculty Early Career Development (CAREER) grant will support research to understand the physical performance of civil infrastructure under extreme loads, such as earthquakes and windstorms, and the interactions among materials, structures, systems, and community needs under such loading. Existing knowledge regarding the performance of conventional materials and structures under normal operating and various hazardous loading conditions has been amassed over years; and data on the performance under earthquake and windstorm loading increasingly are being captured as a result of improved instrumentation, experimentation, and observations. Innovations in new materials and structural design are being created to respond to these extreme loads. To maintain pace with these innovations, while continuing to provide robust and resilient structures, there is a need for a rapid and reliable approach to understanding the behavior of new materials and structural designs under these more extreme loads. The convergence of artificial intelligence (AI) into the civil engineering domain provides the capability to learn the highly nonlinear, complex relationships between material, structural, and load characteristics and a structure?s performance or community?s response. This research will leverage the power of AI and the existing wealth of physics-based performance data to transfer knowledge concerning conventional, well-studied, structural components and loading mechanisms to make performance predictions for out-of-sample cases and innovations where little data is available. This can reduce the reliance on experimental testing and computationally expensive analytical evaluations, and mitigate the catastrophic effects of natural disasters on communities. In tandem, this award will support a multidisciplinary educational and outreach plan, STEM in Motion, which will focus on the development of technologically-forward and active learning-focused activities for undergraduate and graduate civil engineering courses. Data generated from this project will be archived and made publicly available in the Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https:/www.DesignSafe-c.org). This award contributes to the National Science Foundation's role in the National Earthquake Hazards Reduction Program (NEHRP).

This research will employ available experimental data to accurately, robustly, and quickly predict the seismic performance of common structures as well as structures which are exceptionally susceptible to hazardous loads. With a highly accurate AI approach to modeling the behavior of existing structures under well-known material and loading constraints (e.g., reinforced concrete buildings under currently considered seismic loads) where big data is available, the inherent knowledge in these models can be robustly translated across domains and at varying scales (e.g., material, component, system, and load) to reduce uncertainty and lead to enhanced, near-real-time understanding of the physical behavior of new structures. With the ability to derive these relationships directly from existing datasets, the opportunity arises for innovative material and structural modeling procedures and designs uniquely suited for specific performance requirements. Specifically, this research will investigate the following: (1) the dataset features or characteristics necessary to establish relevance between datasets, (2) the mathematical construction and physics-based constraints needed for the algorithmic development to reduce the impact of sample bias in transfer learning problems, (3) verification and validation of the AI model when the model itself is not easily interpretable, and (4) the impact of a multidisciplinary, team-focused environment on the retention of fundamental engineering principles and learning of new concepts by engineering students.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.