ERI: A Hybrid Mechanics-Guided Machine Learning-Based Predictive Framework for the Performance of Rocking Foundations During Earthquake Loading

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Recent research findings reveal that rocking foundations during earthquake loading perform as efficient geotechnical seismic isolation mechanisms and have the potential to eliminate or reduce the damage to the building and bridge structures they support. The objective of this project is to develop a novel, hybrid predictive framework for the performance of rocking foundations by combining the mechanics that governs the physics of the problem with the knowledge discovered from the use of big data and machine learning techniques. This Engineering Research Initiation (ERI) award is the first attempt to combine physics with data science to model the seismic performance of structures supported by rocking foundations, and hence the project directly advances the current knowledge and state of the art in modeling the seismic performance of structural systems. The introduction of rocking foundations in civil engineering design and practice will improve the resiliency and sustainability of civil infrastructure and reduce the human and economic losses resulting from the failures of buildings and bridges, thus directly benefitting society. The core idea of this project is to combine mechanics-based models with machine learning algorithms to develop a hybrid mechanics-guided machine learning-based predictive framework that will ensure better generalizability and accuracy of predictions, as well as consistency of results. In order to achieve this objective, the following research tasks will be carried out: (i) development of machine learning models for performance prediction of rocking structure-foundation-soil systems using centrifuge and shaking table experimental data available in a rocking foundations database; (ii) numerical simulations of rocking systems using mechanics-based models available in the OpenSees finite element framework; (iii) development of hybrid models for prediction of performance of rocking systems by effectively combining the models developed in tasks (i) and (ii). The major outcome of this project will be a hybrid modeling and prediction framework for rocking systems that is both science-driven and data-driven, and has the potential to continuously learn, adapt and improve in the future.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.