Research Topic

Applications of Soft Computing Techniques for Vulnerability Assessment of Buildings

About this Research Topic

The importance of earthquake study, research, and damage prevention has increased after the world's most destructive earthquakes. In the pre-earthquake period, the identification and assessment of earthquake resistance of existing buildings is a very significant task, which must be accomplished quickly, in a simple, economical, and efficient manner.

Nonetheless, performing a more detailed construction analysis and attaining comprehensive knowledge of its geometry, features, and materials may lead to a very accurate nonlinear seismic assessment. However, such an approach would entail unprecedented difficulties when an urban scale mitigation campaign exhibits a high dispersion level. Therefore, a fast and reliable method of identifying unsafe buildings is urgently needed. Rapid Visual Screening (RVS) is a qualitative procedure that estimates structural scores for buildings, widely used in many situations/ applications. For instance, in high-seismic areas, RVS is used as a practical and simple tool for ranking the buildings based on seismic vulnerability considerations.

That being said, conventional RVS methods cannot deal with uncertainties and vagueness, and it is necessary to develop a more accurate method that takes these variables into consideration while handling vast amounts of data. Soft computing techniques (e.g., Artificial Neural Networks, Fuzzy logic, or weighted-based models) show significant efficiency and applicability in the vulnerability assessment of buildings. The results can be quicker, more accurate, digitalized, and efficient.

This Research Topic aims to collect and discover the applied soft computing techniques and their utilization in evaluating buildings' earthquake hazard safety, including but not limited to the following subtopics:

· Machine learning techniques, Neural networks, and fuzzy logic for damage estimation
· Developing techniques for Rapid Visual Hazard Evaluation of Existing Buildings
· Multi-Criteria Decision Making (MCDM), Stochastics and Risk Assessment
· Seismic design and assessment of buildings
· Deterministic and probabilistic analysis methods
· Numerical modeling vs. experimental data
· Earthquake Engineering
· Structural Engineering
· Vulnerability evaluation and retrofitting
· Seismic risk analysis and mitigation strategies
· Seismic hazard analysis
· Flood hazard analysis
· Natural hazards disasters
· Flood vulnerability assessment and management


Keywords: Fuzzy logic, Soft computing, Seismic risk analysis, Artificial Neural Networks


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.

The importance of earthquake study, research, and damage prevention has increased after the world's most destructive earthquakes. In the pre-earthquake period, the identification and assessment of earthquake resistance of existing buildings is a very significant task, which must be accomplished quickly, in a simple, economical, and efficient manner.

Nonetheless, performing a more detailed construction analysis and attaining comprehensive knowledge of its geometry, features, and materials may lead to a very accurate nonlinear seismic assessment. However, such an approach would entail unprecedented difficulties when an urban scale mitigation campaign exhibits a high dispersion level. Therefore, a fast and reliable method of identifying unsafe buildings is urgently needed. Rapid Visual Screening (RVS) is a qualitative procedure that estimates structural scores for buildings, widely used in many situations/ applications. For instance, in high-seismic areas, RVS is used as a practical and simple tool for ranking the buildings based on seismic vulnerability considerations.

That being said, conventional RVS methods cannot deal with uncertainties and vagueness, and it is necessary to develop a more accurate method that takes these variables into consideration while handling vast amounts of data. Soft computing techniques (e.g., Artificial Neural Networks, Fuzzy logic, or weighted-based models) show significant efficiency and applicability in the vulnerability assessment of buildings. The results can be quicker, more accurate, digitalized, and efficient.

This Research Topic aims to collect and discover the applied soft computing techniques and their utilization in evaluating buildings' earthquake hazard safety, including but not limited to the following subtopics:

· Machine learning techniques, Neural networks, and fuzzy logic for damage estimation
· Developing techniques for Rapid Visual Hazard Evaluation of Existing Buildings
· Multi-Criteria Decision Making (MCDM), Stochastics and Risk Assessment
· Seismic design and assessment of buildings
· Deterministic and probabilistic analysis methods
· Numerical modeling vs. experimental data
· Earthquake Engineering
· Structural Engineering
· Vulnerability evaluation and retrofitting
· Seismic risk analysis and mitigation strategies
· Seismic hazard analysis
· Flood hazard analysis
· Natural hazards disasters
· Flood vulnerability assessment and management


Keywords: Fuzzy logic, Soft computing, Seismic risk analysis, Artificial Neural Networks


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

15 July 2021 Abstract
15 October 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

15 July 2021 Abstract
15 October 2021 Manuscript

Participating Journals

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

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