Performance-Based Earthquake Engineering 2.0: Machine-Learning and Artificial Intelligence Algorithms for seismic hazard and vulnerability.

这个博士研究项目将极大地推进基于性能的地震工程（PBEE）领域的最先进的革命，生产出坚固、快速、灵活和智能的新工具。该项目属于EPSRC工程研究领域，重点关注人工智能（EPSRC区域1）如何集成到土木工程中，特别强调地震工程、结构工程（EPSRC区域2）和地面工程（EPSRC区域3）。背景：全球每年经历150次6级以上的地震，过去十年的灾难性事件强调了它们可能造成的生命损失和经济损失。尽管地震事件具有重要的社会和经济意义，但地震引起的地面运动的当代预测仍然是基于历史观测的统计回归的经验模型，很少有潜在物理学的输入。地震工程问题的几个方面也需要有能力的数据处理/数据挖掘工具来理解由全球各地的各种地面运动测量仪器记录的大量数据集。机器学习（ML）领域在过去几年中迅速发展，有望改变数据科学在各个学科中的作用。ML在处理复杂问题、提高计算效率、传播和处理不确定性以及促进决策方面具有优势。随着机器学习的发展，它的应用已经不仅仅局限于人工智能（AI）领域，而且已经扩展到科学和工程的多个领域。ML已经成为一种有前途的数据推进工具，可以处理大量创建的数据集，并解决地震工程中的各种具有挑战性的问题。机器学习在地震工程中的一些应用已经被一些研究人员进行了实践。相比之下，许多应用，如地面运动的实时预测和地震损伤检测，主要尚未开发。因此，加速它们在地震工程领域的应用仍有很大的机会。因此，需要对机器学习和地震工程的交叉领域进行更多的研究，以拥抱下一代数据共享和传感器技术，实现更先进的机器学习技术，并开发物理指导的机器学习模型。今后，这项研究的目的是了解在地震工程问题中实现ML的未来。研究目标与成果：本研究旨在统一和协调新兴的ML工具与经典地震工程方法，以创建现代基于性能的地震工程设计和评估方法。本研究将探讨各种机器学习应用/算法。目的和目标：本研究的预期结果包括：地震危害分析。通过综合稀疏观测和模拟来实时预测地面运动。地震易损性评估。图像检测，开发下一代基础设施漏洞数据集。抗震结构控制。基于高性能计算仿真的元建模。识别和损伤检测。预测地震中地面塌陷的运动。

This PhD research project would significantly advance the state-of-the-art revolutionising the field of performance-based earthquake engineering (PBEE), producing new tools that are robust, fast, flexible, and smart. This project falls within the EPSRC Engineering research area, focusing on how artificial intelligence (EPSRC Area1) can be integrated into civil engineering with a specific emphasis on earthquake engineering, structural engineering (EPSRC Area 2) and Ground Engineering (EPSRC Area 3). Context: Globally, 150 earthquakes over magnitude six are experienced yearly, and catastrophic events in the past decade have emphasised the loss of life and economic damage they can produce. Despite the social and economic importance of seismic events, contemporary prediction of earthquake-induced ground motions is still based on empirical models via statistical regression of historical observations with little input from the underlying physics. Several aspects of Earthquake Engineering problems also require capable data-handling/data-mining tools to understand huge volumes of datasets recorded by various ground motions measuring instruments placed across the globe. The field of Machine Learning (ML) has rapidly evolved over the past few years with the promise to modify the role of data science in various disciplines. ML offers advantages in handling complex problems, providing computational efficiency, propagating and treating uncertainties, and facilitating decision-making. With the advancement of ML, its application is not only limited to the field of Artificial Intelligence (AI) but has expanded to several fields of science and engineering. ML has emerged as a promising data-advancement tool to handle vast volumes of datasets created and solve various challenging problems in earthquake engineering. Some applications of machine learning in Earthquake Engineering have already been in practice by several researchers. In contrast, many applications, such as real-time prediction of ground motions and damage detection of earthquakes, have been mainly untapped. Hence, significant opportunities still exist to accelerate their applications in this field of Earthquake Engineering. Therefore, more research on the cross-field of ML and earthquake engineering is needed to embrace the next generation of data sharing and sensor technologies, implement more advanced ML techniques, and develop physics-guided ML models. Henceforth, this research is directed towards understanding the future of implementing ML into Earthquake Engineering problems. Research Objectives & Outcomes: This research aims to unify and harmonise the emerging ML tools with classical Earthquake Engineering approaches to create a modern Performance-Based Earthquake Engineering design and assessment approach. Various ML applications/algorithms will be explored in this research study. Aims and Objectives: Some of the expected outcomes from this study include: Seismic Hazard Analysis. Real-time prediction of ground motion via synthesis of sparse observations and simulations. Seismic fragility assessment. Image Detection to develop next-generation datasets of infrastructure vulnerability. Structural control for earthquake mitigation. Meta-modelling with High-Performance-Computing-based simulation. Identification and Damage Detection. Predicting the movement expected from ground failure during an earthquake.