Building Fire Prediction and Suppression

Dear Colleagues,

We are delighted to announce a Special Issue on “Building Fire Prediction and Suppression”. Complex buildings and underground infrastructures are both products of modern society and play important roles in economic development. However, they come with increasing fire risks, resulting in severe damage on people, society, and the environment. The challenges in complex buildings’ fire design, assessment, and rescue are mainly caused by their unconventional architectural structure (e.g., high-rise and atriums) and specific purpose (e.g., logistic warehouse and cold storage). Similarly, long, narrow spaces and huge traffic loads in underground infrastructures (such as tunnels and metros) increase difficulties in fire prediction, evacuation, and suppression. Therefore, more advanced theories and intelligent technologies are desperately needed in these domains.

This Special Issue aims to highlight original findings related to building fire prediction and suppression in experimental, simulation, or machine learning methods. Additionally, potential perspectives for future investigations are also encouraged.

In this Special Issue, original research articles and reviews are welcome on areas that may include (but are not limited to) the following:

• Fire suppression:
• Fundamental research on the interactions of fire dynamics and the characteristics of suppression agents;
• Fire suppression performance and optimization of water-based firefighting systems, such as water sprinkler, water spray, water mist, and foam systems;
• Fire suppression in lithium-ion battery fires;
• Integration of fire suppression systems with tunnel ventilation strategies.
• Complex Building and Tunnel Fire Safety:
• Smoke movement and control strategies under various ventilation conditions;
• Fire behavior and smoke propagation in tunnels with different slopes and ventilation systems;
• Fire safety design and risk assessment for utility corridors and metro stations;
• Evacuation modeling and occupant behavior in complex fire scenarios.
• Smart Firefighting Technologies:
• Machine learning and AI applications for fire detection and prediction;
• Sensor networks for real-time fire monitoring and data analysis;
• Decision support systems for optimizing firefighting strategies.

We look forward to receiving your contributions.

Dr. Zhi Tang
Prof. Dr. Tianshui Liang
Dr. Yuanjun Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Fire is an international peer-reviewed open access monthly 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 suppression
• complex building fire
• smoke control
• smart firefighting
• tunnel fire
• metro station safety
• fire dynamics modeling
• evacuation modeling
• artificial intelligence