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

ERI：基于混合力学引导的机器学习预测框架，用于地震加载期间摇摆基础的性能

• 批准号: 2138631
• 负责人: Sivapalan Gajan
• 金额: $ 19.86万
• 依托单位: SUNY Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138631&HistoricalAwards=false
• 关键词: ERI; Hybrid; Mechanics; Guided; Machine

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.

该奖项的全部或部分资金均根据《2021 年美国救援计划法案》（公法 117-2）提供。最近的研究结果表明，地震荷载期间的摇动地基可作为有效的岩土隔震机制，并有可能消除或减少地震的影响。对它们支撑的建筑物和桥梁结构的损坏。该项目的目标是通过将控制问题物理的力学与使用大数据和机器学习技术发现的知识相结合，开发一种新颖的混合预测框架，用于摇摆地基的性能。该工程研究启动 (ERI) 奖是首次尝试将物理学与数据科学相结合，对摇动地基支撑的结构的抗震性能进行建模，因此该项目直接推进了地震性能建模的当前知识和技术水平。结构系统。在土木工程设计和实践中引入摇摆基础将提高土木基础设施的弹性和可持续性，减少建筑物和桥梁失效造成的人员和经济损失，从而直接造福社会。该项目的核心思想是将基于力学的模型与机器学习算法相结合，开发一个混合力学引导的基于机器学习的预测框架，以确保更好的预测的通用性和准确性以及结果的一致性。为了实现这一目标，将开展以下研究任务：（i）开发机器学习模型，利用摇摆基础数据库中提供的离心机和振动台实验数据来预测摇摆结构-基础-土壤系统的性能； (ii) 使用 OpenSees 有限元框架中提供的基于力学的模型对摇摆系统进行数值模拟； (iii) 通过有效结合任务（i）和（ii）中开发的模型来开发用于预测摇摆系统性能的混合模型。该项目的主要成果将是一个用于摇摆系统的混合建模和预测框架，该框架既是科学驱动的，也是数据驱动的，并且具有在未来不断学习、适应和改进的潜力。该奖项反映了 NSF 的法定使命和通过使用基金会的智力优点和更广泛的影响审查标准进行评估，该项目被认为值得支持。

项目成果

Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms

使用机器学习算法对摇摆浅基础的峰值旋转和翻倒稳定性进行数据驱动建模

• DOI: 10.3390/geotechnics2030038
• 发表时间: 2022
• 期刊: Geotechnics
• 作者: Gajan, Sivapalan

Modeling of Rocking Induced Permanent Settlement of Shallow Foundations Using Machine Learning Algorithms

使用机器学习算法对摇摆引起的浅地基永久沉降进行建模

• DOI: 10.1061/9780784484685.061
• 发表时间: 2023
• 期刊: Geo-Congress 2023
• 作者: Gajan, Sivapalan

Data-Driven Modeling of Seismic Energy Dissipation of Rocking Foundations Using Decision Tree-Based Ensemble Machine Learning Algorithms

使用基于决策树的集成机器学习算法对摇摆基础的地震能量耗散进行数据驱动建模

• DOI: 10.1061/9780784484692.031
• 发表时间: 2023
• 期刊: Geo-Congress 2023
• 作者: Gajan, Sivapalan;Banker, Wakeley;Bonacci, Alexander
• 通讯作者: Bonacci, Alexander

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

使用机器学习和深度学习模型预测摇摆基础的加速度放大率

• DOI: 10.3390/app132312791
• 发表时间: 2023
• 期刊: Applied Sciences
• 作者: Gajan, Sivapalan