Dear Colleagues,

Artificial Intelligence (AI), Machine Learning (ML), and Numerical Simulations (NS) in Fire Engineering and Sciences are opening the door to new and exciting opportunities in this domain. This special issue aims at showcasing the potential of convergence between AI, ML and NS to deliver unique solutions that can bypass bottlenecks associated with traditional methods. At the same time, this special issue hopes to converge machine learning and traditional methods to distill their advantages and overcome their individual limitations. In a way, the goal of this special issue is to foster works targeting the following potential areas of (as well as those related to such areas):

1. in fire engineering
2. in structural fire design
3. in fire detection, suppression and mitigation
4. in fire-related sensor and software design
5. in materials (e.g., construction, fire-resistive etc.) investigation
6. in decision making for fire engineering applications

Dr. Carlos Viegas
Dr. João Ruivo Paulo
Dr. Jérôme Mendes
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• fire
• machine learning
• structures
• design
• materials