Literature DB >> 33672068

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

Hoang T Nguyen1, Kate T Q Nguyen1, Tu C Le2, Guomin Zhang1.   

Abstract

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

Entities:  

Keywords:  artificial intelligence; chemical kinetics; combustion; flame retardants; machine learning; pyrolysis

Mesh:

Substances:

Year:  2021        PMID: 33672068      PMCID: PMC7919694          DOI: 10.3390/molecules26041022

Source DB:  PubMed          Journal:  Molecules        ISSN: 1420-3049            Impact factor:   4.411


  17 in total

Review 1.  Quantitative structure-property relationship modeling of diverse materials properties.

Authors:  Tu Le; V Chandana Epa; Frank R Burden; David A Winkler
Journal:  Chem Rev       Date:  2012-01-17       Impact factor: 60.622

Review 2.  Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction.

Authors:  Alan R Katritzky; Minati Kuanar; Svetoslav Slavov; C Dennis Hall; Mati Karelson; Iiris Kahn; Dimitar A Dobchev
Journal:  Chem Rev       Date:  2010-10-13       Impact factor: 60.622

Review 3.  Probabilistic machine learning and artificial intelligence.

Authors:  Zoubin Ghahramani
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 4.  Computational advances in combating colloidal aggregation in drug discovery.

Authors:  Daniel Reker; Gonçalo J L Bernardes; Tiago Rodrigues
Journal:  Nat Chem       Date:  2019-04-15       Impact factor: 24.427

5.  Materials Discovery and Properties Prediction in Thermal Transport via Materials Informatics: A Mini Review.

Authors:  Xiao Wan; Wentao Feng; Yunpeng Wang; Haidong Wang; Xing Zhang; Chengcheng Deng; Nuo Yang
Journal:  Nano Lett       Date:  2019-06-04       Impact factor: 11.189

Review 6.  Next-Generation Machine Learning for Biological Networks.

Authors:  Diogo M Camacho; Katherine M Collins; Rani K Powers; James C Costello; James J Collins
Journal:  Cell       Date:  2018-06-07       Impact factor: 41.582

Review 7.  Inverse molecular design using machine learning: Generative models for matter engineering.

Authors:  Benjamin Sanchez-Lengeling; Alán Aspuru-Guzik
Journal:  Science       Date:  2018-07-26       Impact factor: 47.728

8.  Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.

Authors:  Felix A Faber; Luke Hutchison; Bing Huang; Justin Gilmer; Samuel S Schoenholz; George E Dahl; Oriol Vinyals; Steven Kearnes; Patrick F Riley; O Anatole von Lilienfeld
Journal:  J Chem Theory Comput       Date:  2017-10-10       Impact factor: 6.006

Review 9.  The practical implementation of artificial intelligence technologies in medicine.

Authors:  Jianxing He; Sally L Baxter; Jie Xu; Jiming Xu; Xingtao Zhou; Kang Zhang
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

Review 10.  Data-Driven Materials Science: Status, Challenges, and Perspectives.

Authors:  Lauri Himanen; Amber Geurts; Adam Stuart Foster; Patrick Rinke
Journal:  Adv Sci (Weinh)       Date:  2019-09-01       Impact factor: 16.806

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  1 in total

1.  Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques.

Authors:  Wangfei Ding; Abdullah Alharbi; Ahmad Almadhor; Payam Rahnamayiezekavat; Masoud Mohammadi; Maria Rashidi
Journal:  Materials (Basel)       Date:  2022-02-14       Impact factor: 3.623

  1 in total

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