Research Topic

Artificial Intelligence-powered Methodologies and Applications in Earthquake and Structural Engineering

About this Research Topic

Earthquake disasters have caused enormous casualties and economic losses so far, threatening the social and economic development of humanity. At present, artificial intelligence (AI) technology is one of the frontiers and central issues in both, academic research, and engineering practice.

AI refers to the branch of computer science that develops machines and software with human-like intelligence. In recent years, AI techniques are developing rapidly and have been widely adopted in several engineering disciplines. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention. These techniques are establishing themselves as a new class of powerful and intelligent methods for use in earthquake and structural engineering with proven effectiveness, as shown in recent studies.

This Research Topic welcomes cutting-edge research combining AI with various scientific fields in solving engineering and science problems. The range of appropriate contributions is broad and includes papers on developing advanced machine learning algorithms, appropriate mathematical models, and AI-based structural or earthquake engineering applications. These can be in all areas of the aforementioned research fields, such as seismic ground motion studies, structural and city-scale seismic risk, structural system identification and damage detection, structural control under seismic action, structural health monitoring, and quantification of uncertainty, etc.

In the future, with the improvement of computational power and data accumulation, the feasibility and necessity of AI-driven technologies are expected to grow quickly. This Research Topic welcomes cutting-edge research combining AI with various scientific fields including, but not limited to:
• seismic ground motion studies
• structural and city-scale seismic risk
• computational methods in structural engineering
• structural system identification and damage detection
• structural control under seismic action
• structural health monitoring

If a paper presents novel techniques, some comparison with known advanced methods is necessary. If a paper is to provide substantial new insights into advanced methods, then this can be achieved by strong numerical experiments, some mathematical analysis, and/or comparisons with well-designed physical test data. In either case, the paper must contribute to advancing the state of the art research in this area.


Keywords: Artifical intelligence, Seismic risk and damage assessment, System identification, Structural dynamics and control, Structural health monitoring and damage detection


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.

Earthquake disasters have caused enormous casualties and economic losses so far, threatening the social and economic development of humanity. At present, artificial intelligence (AI) technology is one of the frontiers and central issues in both, academic research, and engineering practice.

AI refers to the branch of computer science that develops machines and software with human-like intelligence. In recent years, AI techniques are developing rapidly and have been widely adopted in several engineering disciplines. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention. These techniques are establishing themselves as a new class of powerful and intelligent methods for use in earthquake and structural engineering with proven effectiveness, as shown in recent studies.

This Research Topic welcomes cutting-edge research combining AI with various scientific fields in solving engineering and science problems. The range of appropriate contributions is broad and includes papers on developing advanced machine learning algorithms, appropriate mathematical models, and AI-based structural or earthquake engineering applications. These can be in all areas of the aforementioned research fields, such as seismic ground motion studies, structural and city-scale seismic risk, structural system identification and damage detection, structural control under seismic action, structural health monitoring, and quantification of uncertainty, etc.

In the future, with the improvement of computational power and data accumulation, the feasibility and necessity of AI-driven technologies are expected to grow quickly. This Research Topic welcomes cutting-edge research combining AI with various scientific fields including, but not limited to:
• seismic ground motion studies
• structural and city-scale seismic risk
• computational methods in structural engineering
• structural system identification and damage detection
• structural control under seismic action
• structural health monitoring

If a paper presents novel techniques, some comparison with known advanced methods is necessary. If a paper is to provide substantial new insights into advanced methods, then this can be achieved by strong numerical experiments, some mathematical analysis, and/or comparisons with well-designed physical test data. In either case, the paper must contribute to advancing the state of the art research in this area.


Keywords: Artifical intelligence, Seismic risk and damage assessment, System identification, Structural dynamics and control, Structural health monitoring and damage detection


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.

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Submission Deadlines

27 October 2020 Abstract
29 January 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

27 October 2020 Abstract
29 January 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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