Application of Artificial Intelligence on Structures Subjected to Natural Hazards

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2164

Special Issue Editors


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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán C.P. 80040, Mexico
Interests: earthquake engineering; seismology; artificial intelligence; natural hazards; structural engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán C.P. 80040, Mexico
Interests: earthquake engineering; seismology; artificial intelligence; natural hazards; structural engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán C.P. 80040, Mexico
Interests: earthquake engineering; seismology; artificial intelligence; natural hazards; structural engineering

Special Issue Information

Dear Colleagues,

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. AI technologies, including machine learning and data analysis, offer innovative solutions for improving the resilience and sustainability of the built environment.

This publication 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, the predictive modeling of natural hazards, the 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 the development interdisciplinary approaches and innovative methodologies that push the boundaries of current engineering practices. Authors are expected to present clear approaches, robust data analyses, and practical implications of their findings.

Dr. Eden Bojórquez
Dr. Juan Bojórquez
Dr. Robespierre Chavez-Lopez
Guest Editors

Manuscript Submission Information

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Keywords

  • earthquake engineering
  • seismic hazard
  • wind hazard
  • neural networks
  • metaheuristic techniques

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Published Papers (1 paper)

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Research

34 pages, 8070 KB  
Article
AI-Enhanced Rescue Drone with Multi-Modal Vision and Cognitive Agentic Architecture
by Nicoleta Cristina Gaitan, Bianca Ioana Batinas and Calin Ursu
AI 2025, 6(10), 272; https://doi.org/10.3390/ai6100272 - 20 Oct 2025
Viewed by 1928
Abstract
In post-disaster search and rescue (SAR) operations, unmanned aerial vehicles (UAVs) are essential tools, yet the large volume of raw visual data often overwhelms human operators by providing isolated, context-free information. This paper presents an innovative system with a novel cognitive–agentic architecture that [...] Read more.
In post-disaster search and rescue (SAR) operations, unmanned aerial vehicles (UAVs) are essential tools, yet the large volume of raw visual data often overwhelms human operators by providing isolated, context-free information. This paper presents an innovative system with a novel cognitive–agentic architecture that transforms the UAV from an intelligent tool into a proactive reasoning partner. The core innovation lies in the LLM’s ability to perform high-level semantic reasoning, logical validation, and robust self-correction through internal feedback loops. A visual perception module based on a custom-trained YOLO11 model feeds the cognitive core, which performs contextual analysis and hazard assessment, enabling a complete perception–reasoning–action cycle. The system also incorporates a physical payload delivery module for first-aid supplies, which acts on prioritized, actionable recommendations to reduce operator cognitive load and accelerate victim assistance. This work, therefore, presents the first developed LLM-driven architecture of its kind, transforming a drone from a mere data-gathering tool into a proactive reasoning partner and demonstrating a viable path toward reducing operator cognitive load in critical missions. Full article
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