Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Hurricanes threaten the petroleum industry in the United States and are expected to be influenced by climate change. This study presents an integrated framework for hurricane risk assessment of petroleum infrastructure under changing climatic conditions, calculating risk in terms of monetary loss. Variants of two synthetic probabilistic storms and one historical storm (Hurricane Ike) are simulated using the SWAN+ADCIRC model, representing a range of potential scenarios of impacts of a changing climate on hurricane forward speed and sea-level rise given uncertainties in climate projections. Model outputs inform an infrastructure impact and cascading economic loss analysis that incorporates various sources of uncertainty to estimate five types of losses sustained by petroleum facilities in surge events: land value loss, process-unit damage loss, cost of spill clean-up and repair of aboveground storage tanks, productivity loss, and civil fines. The proposed risk assessment framework is applied as a case study to seven refineries along the Houston Ship Channel (HSC), a densely-industrialized corridor in Texas. The results reveal that either an increase in mean sea level or a decrease in storm forward speed increases the maximum water elevations in the HSC for storms that produce maximum wind setup in Galveston Bay (FEMA 33 and FEMA 36), resulting in larger economic loss estimates. The role of refinery features such as storage capacity and average elevation of the refinery and its critical equipment in the refinery response to hurricane hazards is studied, and the probability distribution of refinery total loss and the loss risk profile in different hurricane scenarios are discussed. Loss estimates are presented, demonstrating the effects of hurricane forward speed and sea level on the losses for the refineries as well as the HSC. Such a framework can enable hurricane risk assessment and loss estimation for petroleum infrastructure to inform future policies and risk mitigation strategies. Potential policy implications for a region like the HSC are highlighted herein as an illustration.

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