[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121082-en":3,"doc-seo-121082-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121082,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Application of machine learning techniques to predict fire development in an ISO 9705 room","Machine learning, particularly artificial neural networks (ANNs), offers a way to improve computational fire modelling while addressing the high cost of conventional computational fluid dynamics (CFD). The study predicts heptane fire development in an ISO 9705 compartment by varying heat release rates (100–3000 kW) and ventilation areas (0.16–4.8 m²). Models were trained on computational data with optimisation through training-to-validation ratios and hidden-layer neuron tuning. Optimised ANNs achieved under 7% error for heat release rate and 1.5% for ventilation size, with computational time reduced by over 104×, supporting safer, faster fire engineering workflows.","Central Lancashire Online Knowledge (CLoK)  \n\n| Title | Application of machine learning techniques to predict fire development inan ISO 9705 room |\n| --- | --- |\n| Type | Article |\n| URL | [https://clok.uclan.ac.uk/53855/](https://clok.uclan.ac.uk/53855/) |\n| DOI | [https://doi.org/10.1088/1742-6596/2885/1/012101](https://doi.org/10.1088/1742-6596/2885/1/012101) |\n| Date | 2024 |\n| Citation | Nasar, Lareb, Dixon, Harriet Grace, Drosopoulos, Georgios and Asimakopoulou, Eleni (2024) Application of machine learning techniques to predict fire development in an ISO 9705 room. Journal of Physics: Conference Series, 2885 (1) . ISSN 1742-6596 |\n| Creators | Nasar, Lareb, Dixon, Harriet Grace, Drosopoulos, Georgios and Asimakopoulou, Eleni |\n\nIt is advisable to refer to the publisher’s version if you intend to cite from the work. [https://doi.org/10.1088/1742-6596/2885/1/012101](https://doi.org/10.1088/1742-6596/2885/1/012101)  \nFor information about Research at UCLan please go to [http://www.uclan.ac. uk/research/](http://www.uclan.ac. uk/research/)  \n[All outputs in CLoK are protected by Intellectual Property Rights law](All outputs in CLoK are protected by Intellectual Property Rights law), including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the  \n[http://clok.uclan.ac.uk/policies/](http://clok.uclan.ac.uk/policies/)  \nCentral Lancashire Online Knowledge (CLoK)  \n\n| Title | Application of machine learning techniques to predict fire development inan ISO 9705 room |\n| --- | --- |\n| Type | Article |\n| URL | [https://clok.uclan.ac.uk/53855/](https://clok.uclan.ac.uk/53855/) |\n| DOI | [https://doi.org/10.1088/1742-6596/2885/1/012101](https://doi.org/10.1088/1742-6596/2885/1/012101) |\n| Date | 2024 |\n| Citation | Nasar, Lareb, Dixon, Harriet, Drosopoulos, Georgios and Asimakopoulou, Eleni (2024) Application of machine learning techniques to predict fire development in an ISO 9705 room. Journal of Physics: Conference Series, 2885 (1) . ISSN 1742-6596 |\n| Creators | Nasar, Lareb, Dixon, Harriet, Drosopoulos, Georgios and Asimakopoulou, Eleni |\n\nIt is advisable to refer to the publisher’s version if you intend to cite from the work. [https://doi.org/10.1088/1742-6596/2885/1/012101](https://doi.org/10.1088/1742-6596/2885/1/012101)  \nFor information about Research at UCLan please go to [http://www.uclan.ac. uk/research/](http://www.uclan.ac. uk/research/)  \n[All outputs in CLoK are protected by Intellectual Property Rights law](All outputs in CLoK are protected by Intellectual Property Rights law), including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the  \n[http://clok.uclan.ac.uk/policies/](http://clok.uclan.ac.uk/policies/)  \nJournal of Physics: Conference Series 2885 (2024) 012101 doi:10.1088/1742-6596/2885/1/012101  \nApplication of machine learning techniques to predict fire development in an ISO 9705 room  \nLareb Nasar1,2, Harriet Dixon1,2, Georgios Drosopoulos2, Eleni Asimakopoulou2, *  \n1 London Fire Brigade, 169 Union St, London SE1 0LL  \n2 School of Engineering, University of Central Lancashire, Fylde Road, Preston, PR1 2HE, UK  \n*[EAsimakopoulou@uclan.ac.uk](EAsimakopoulou@uclan.ac.uk)  \nAbstract. Machine learning, a subset of artificial intelligence, shows potential for enhancing computational fire modelling compared to traditional methods such as computational fluid dynamics. This study explored using artificial neural networks to predict heptane fire development within a compartment, varying heat release rates from 100 to 3000 kW and ventilation areas from 0.16 to 4.8 m2. Artificial neural networks (ANNs) were trained using computational data from an ISO 9705 room. Network optimisation involved adjusting trainin","cbCainxExqrHRUK5","https://ap.wps.com/l/cbCainxExqrHRUK5","pdf",856949,1,"English","en",105,"# Abstract\n# Introduction\n## Motivation: CFD limitations and experimental constraints\n## Background: ANN concepts and network architecture","[{\"question\":\"What problem does the study address in fire engineering modelling?\",\"answer\":\"It targets the need to predict fire behaviour when experimental data are limited and traditional CFD modelling is computationally expensive.\"},{\"question\":\"How was the fire scenario represented for prediction?\",\"answer\":\"The models predict heptane fire development in an ISO 9705 room while varying heat release rates from 100 to 3000 kW and ventilation areas from 0.16 to 4.8 m².\"},{\"question\":\"How effective were the optimised ANN models?\",\"answer\":\"After optimisation, the ANNs achieved less than 7% error for predicting heat release rate and about 1.5% error for ventilation size, along with more than a 104-fold reduction in computational cost.\"}]","Application of machine learning techniques to predict fire development in an ISO 9705 room | 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