About this Research Topic
Natural hazards such as earthquakes and windstorms generate significant risks to infrastructure worldwide. Traditional engineering approaches, while effective, often struggle to keep pace with the growing complexity and frequency of these events. Recent advancements in artificial intelligence (AI) provide new opportunities to enhance our ability to predict, assess, and mitigate the impacts of natural hazards on buildings and infrastructure. AI technologies, including machine learning and data analysis, offer innovative solutions for improving the resilience and sustainability of the built environment.
The primary goal of this research topic is to address the critical need for searching innovative approaches to mitigate the effects of natural hazards on infrastructure. By leveraging AI, we aim to advance our understanding and capabilities in several key areas: predicting the occurrence and severity of natural hazards, assessing the vulnerability of existing structures, optimizing the design and construction of resilient buildings, and enhancing real-time response and recovery efforts. Recent advances in AI, such as deep learning and predictive modeling, have shown promise in transforming earthquake and wind engineering practices. This research topic seeks to gather cutting-edge research that demonstrates the application of AI in these areas, fostering a multidisciplinary dialogue that can lead to more resilient infrastructure systems.
This research topic invites contributions that explore the application of AI in enhancing the resilience of infrastructure subjected to natural hazards. We welcome submissions addressing themes such as AI-driven risk assessment, predictive modeling of natural hazards, resilience enhancement of buildings, and the integration of AI in earthquake and wind engineering. Manuscripts can include original research articles, reviews, case studies, and technical notes. We encourage interdisciplinary approaches and innovative methodologies that push the boundaries of current engineering practices. Authors are expected to present clear methodologies, robust data analyses, and practical implications of their findings.
The primary goal of this research topic is to address the critical need for searching innovative approaches to mitigate the effects of natural hazards on infrastructure. By leveraging AI, we aim to advance our understanding and capabilities in several key areas: predicting the occurrence and severity of natural hazards, assessing the vulnerability of existing structures, optimizing the design and construction of resilient buildings, and enhancing real-time response and recovery efforts. Recent advances in AI, such as deep learning and predictive modeling, have shown promise in transforming earthquake and wind engineering practices. This research topic seeks to gather cutting-edge research that demonstrates the application of AI in these areas, fostering a multidisciplinary dialogue that can lead to more resilient infrastructure systems.
This research topic invites contributions that explore the application of AI in enhancing the resilience of infrastructure subjected to natural hazards. We welcome submissions addressing themes such as AI-driven risk assessment, predictive modeling of natural hazards, resilience enhancement of buildings, and the integration of AI in earthquake and wind engineering. Manuscripts can include original research articles, reviews, case studies, and technical notes. We encourage interdisciplinary approaches and innovative methodologies that push the boundaries of current engineering practices. Authors are expected to present clear methodologies, robust data analyses, and practical implications of their findings.
Keywords: Artificial intelligence, Natural hazard, Risk hazard, Resilience of buildings under natural hazards Earthquake, wind engineering
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
