{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:38:01Z","timestamp":1780709881245,"version":"3.54.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017649","name":"Hong Kong Research Grants Council","doi-asserted-by":"publisher","award":["T22-505\/19-N"],"award-info":[{"award-number":["T22-505\/19-N"]}],"id":[{"id":"10.13039\/501100017649","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52238011"],"award-info":[{"award-number":["52238011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper aims to automatize the performance-based design of fire engineering and the fire risk assessment of buildings with large open spaces and complex shapes. We first establish a database of high-quality fire simulations for diverse building shapes with heights up to 60 m and complex atriums with volumes up to 22\u2009400 m\u00b3. Then, artificial intelligence (AI) models are trained to predict the soot visibility slices for new fire cases in buildings of different atrium shapes, symmetricities, and volumes. Two deep learning models were demonstrated: the pix2pix generative adversarial network (GAN) and image-prompt diffusion model. Compared with high-fidelity computational fluid dynamics fire modeling, the available safe egress time predicted by both models shows a high accuracy of 92% for random atrium shapes that are not distinct from the training cases, proving their performance in actual design practices. The diffusion model reproduces more flow details of the smoke visibility profiles than GAN, but it takes a longer computational time to render the fire scene. This work demonstrates the potential of leveraging AI technologies in building fire safety design, offering significant cost and time reductions and optimal solution identification.<\/jats:p>","DOI":"10.1093\/jcde\/qwae053","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T21:22:35Z","timestamp":1718227355000},"page":"359-373","source":"Crossref","is-referenced-by-count":12,"title":["AI-powered fire engineering design and smoke flow analysis for complex-shaped buildings"],"prefix":"10.1093","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7397-0572","authenticated-orcid":false,"given":"Yanfu","family":"Zeng","sequence":"first","affiliation":[{"name":"Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University , Hong Kong, 999077 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Tsinghua University , Beijing, 100000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University , Hong Kong, 999077 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0584-8452","authenticated-orcid":false,"given":"Xinyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University , Hong Kong, 999077 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3313-7420","authenticated-orcid":false,"given":"Xinzheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Tsinghua University , Beijing, 100000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"2024073108030073500_bib1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/BF02590543","article-title":"Calculation of response time of ceiling-mounted fire detectors","volume":"8","author":"Alpert","year":"1972","journal-title":"Fire Technology"},{"key":"2024073108030073500_bib2","volume-title":"Building code of Australia\u2014Volume one","author":"Australian Building Codes Board","year":"2022"},{"key":"2024073108030073500_bib3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10694-015-0487-9","article-title":"Fire experiments and simulations in a full-scale atrium under transient and asymmetric venting conditions","volume":"52","author":"Ayala","year":"2016","journal-title":"Fire Technology"},{"key":"2024073108030073500_bib4","doi-asserted-by":"crossref","first-page":"11435","DOI":"10.3390\/app112311435","article-title":"Performance evaluation of artificial neural networks (ANN) predicting heat transfer through Masonry walls exposed to fire","volume":"11","author":"Bakas","year":"2021","journal-title":"Applied Sciences"},{"key":"2024073108030073500_bib5","first-page":"012112","article-title":"A review of the contributions of artificial intelligence in fire engineering, in a world rapidly realising the need for sustainable design","volume":"1196","author":"Bakas","year":"2023","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"2024073108030073500_bib6","volume-title":"Code of practice for fire safety in buildings","author":"Building Department","year":"2011"},{"key":"2024073108030073500_doi55_344_105724","first-page":"103895","article-title":"Modelling carbon monoxide transport and hazard from smouldering for building fire safety design analysis","volume-title":"Fire Safety Journal","author":"Cheung","year":"2023"},{"key":"2024073108030073500_bib7","volume-title":"TB 10063-2016: Code for design of fire prevention for railway engineering","author":"China Railway Third Survey and Design Institute Group","year":"2016"},{"key":"2024073108030073500_bib8","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume":"11","author":"Dhariwal","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2024073108030073500_bib9","doi-asserted-by":"crossref","first-page":"104619","DOI":"10.1016\/j.autcon.2022.104619","article-title":"Knowledge-enhanced generative adversarial networks for schematic design of framed tube structures","volume":"144","author":"Fei","year":"2022","journal-title":"Automation in Construction"},{"key":"2024073108030073500_bib10","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1111\/mice.13094","article-title":"Knowledge-enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings","volume":"39","author":"Fei","year":"2024","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"2024073108030073500_bib11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/JSENDH.STENG-12206","article-title":"Intelligent generative design for shear wall cross-sectional size using rule-embedded generative adversarial network","volume":"149","author":"Feng","year":"2023","journal-title":"Journal of Structural Engineering"},{"key":"2024073108030073500_bib12","doi-asserted-by":"crossref","DOI":"10.1111\/mice.13236","article-title":"Intelligent design of shear wall layout based on diffusion models","author":"Gu","year":"2024","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"2024073108030073500_bib13","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0379-7112(84)90005-5","article-title":"Engineering relations for fire plumes","volume":"7","author":"Heskestad","year":"1984","journal-title":"Fire Safety Journal"},{"key":"2024073108030073500_bib14","first-page":"1","article-title":"Denoising diffusion probabilistic models","volume-title":"Advances in neural information processing systems (NeurIPS 2020)","author":"Ho","year":"2020"},{"key":"2024073108030073500_bib15","doi-asserted-by":"crossref","first-page":"156","DOI":"10.52842\/conf.acadia.2018.156","article-title":"Architectural drawings recognition and generation through machine learning","volume-title":"Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA)","author":"Huang","year":"2018"},{"key":"2024073108030073500_bib16","doi-asserted-by":"crossref","first-page":"5967","DOI":"10.1109\/CVPR.2017.632","article-title":"Image-to-image translation with conditional adversarial networks","volume-title":"Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)","author":"Isola","year":"2017"},{"key":"2024073108030073500_bib17","first-page":"1","article-title":"Estimation of fire temperature rise curves in concrete buildings and its application","volume":"13","author":"Kawagoe","year":"1963","journal-title":"Bulletin of Japan Association for Fire Science and Engineering"},{"key":"2024073108030073500_bib18","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/978-1-4939-2565-0_36","article-title":"Combustion characteristics of materials and generation of fire products","volume-title":"SFPE handbook of fire protection engineering","author":"Khan","year":"2016","edition":"5th ed."},{"key":"2024073108030073500_bib19","doi-asserted-by":"crossref","first-page":"118530","DOI":"10.1016\/j.eswa.2022.118530","article-title":"Intelligent generative structural design method for shear wall building based on \u201cfused-text-image-to-image\u201d generative adversarial networks","volume":"210","author":"Liao","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"2024073108030073500_bib20","doi-asserted-by":"crossref","first-page":"105187","DOI":"10.1016\/j.autcon.2023.105187","article-title":"Generative AI design for building structures","volume":"157","author":"Liao","year":"2024","journal-title":"Automation in Construction"},{"key":"2024073108030073500_bib21","doi-asserted-by":"crossref","first-page":"103931","DOI":"10.1016\/j.autcon.2021.103931","article-title":"Automated structural design of shear wall residential buildings using generative adversarial networks","volume":"132","author":"Liao","year":"2021","journal-title":"Automation in Construction"},{"key":"2024073108030073500_bib22","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.1002\/eqe.3862","article-title":"Base-isolation design of shear wall structures using physics-rule-co-guided self-supervised generative adversarial networks","volume":"52","author":"Liao","year":"2023","journal-title":"Earthquake Engineering & Structural Dynamics"},{"key":"2024073108030073500_bib23","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1002\/eqe.3632","article-title":"Intelligent structural design of shear wall residence using physics-enhanced generative adversarial networks","volume":"51","author":"Lu","year":"2022","journal-title":"Earthquake Engineering and Structural Dynamics"},{"key":"2024073108030073500_bib24","doi-asserted-by":"crossref","first-page":"e001","DOI":"10.48130\/emst-0024-0001","article-title":"Review and application of engineering design models for building fire smoke movement and control","volume":"4","author":"Luo","year":"2024","journal-title":"Emergency Management Science and Technology"},{"key":"2024073108030073500_bib25","article-title":"Fire dynamics simulator user\u2019s guide","volume-title":"NIST special publication 1019","author":"McGrattan","year":"2019","edition":"6th ed."},{"key":"2024073108030073500_bib26","article-title":"Fire dynamics simulator technical reference guide volume 3: Validation","volume-title":"NIST special publication 1018","author":"McGrattan","year":"2021","edition":"6th ed."},{"key":"2024073108030073500_bib27","article-title":"Fire dynamics simulator technical reference guide volume 2: Verification","volume-title":"NIST special publication 1018-2","author":"McGrattan","year":"2023","edition":"6th ed."},{"key":"2024073108030073500_bib28","article-title":"Fire engineering design guide","volume-title":"Tribology international","author":"New Zealand Centre for Advanced Engineering","year":"2008","edition":"3rd ed."},{"key":"2024073108030073500_bib29","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.3390\/molecules26041022","article-title":"Review on the use of artificial intelligence to predict fire performance of construction materials and their flame retardancy","volume":"26","author":"Nguyen","year":"2021","journal-title":"Molecules"},{"key":"2024073108030073500_bib30","doi-asserted-by":"crossref","first-page":"102529","DOI":"10.1016\/j.jobe.2021.102529","article-title":"Smart performance-based design for building fire safety: Prediction of smoke motion via AI","volume":"43","author":"Su","year":"2021","journal-title":"Journal of Building Engineering"},{"key":"2024073108030073500_bib31","doi-asserted-by":"crossref","first-page":"105258","DOI":"10.1016\/j.engappai.2022.105258","article-title":"A spatial temporal graph neural network model for predicting flashover in arbitrary building floorplans","volume":"115","author":"Tam","year":"2022","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2024073108030073500_bib32","doi-asserted-by":"crossref","first-page":"4115","DOI":"10.1016\/j.proci.2022.07.062","article-title":"Predicting real-time fire heat release rate by flame images and deep learning","volume":"39","author":"Wang","year":"2023","journal-title":"Proceedings of the Combustion Institute"},{"key":"2024073108030073500_bib33","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s10694-022-01218-1","article-title":"Numerical modeling of compartment fires: Ventilation characteristics and limitation of Kawagoe\u2019s law","volume":"60","author":"Wang","year":"2024","journal-title":"Fire Technology"},{"key":"2024073108030073500_bib34","doi-asserted-by":"crossref","first-page":"103823","DOI":"10.1016\/j.jobe.2021.103823","article-title":"Predicting transient building fire based on external smoke images and deep learning","volume":"47","author":"Wang","year":"2022","journal-title":"Journal of Building Engineering"},{"key":"2024073108030073500_bib35","first-page":"1285","article-title":"Fire engineering analysis for complex geometry building based on BIM integrated simulation platform","volume-title":"Mechanisms and machine science","author":"Wong","year":"2024"},{"key":"2024073108030073500_bib36","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10694-020-00985-z","article-title":"Smart detection of fire source in tunnel based on the numerical database and artificial intelligence","volume":"57","author":"Wu","year":"2021","journal-title":"Fire Technology"},{"key":"2024073108030073500_bib37","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s12273-021-0775-x","article-title":"A real-time forecast of tunnel fire based on numerical database and artificial intelligence","volume":"15","author":"Wu","year":"2022","journal-title":"Building Simulation"},{"key":"2024073108030073500_bib38","doi-asserted-by":"crossref","DOI":"10.1109\/ICFSFPE48751.2019.9055794","article-title":"Numerical study of the performance of automatic sprinkler system in dense equipment room","volume-title":"Proceedings of the 2019 9th International Conference on Fire Science and Fire Protection Engineering (ICFSFPE)","author":"Zeng","year":"2019"},{"key":"2024073108030073500_bib39","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-3-031-48161-1_5","article-title":"Artificial intelligence powered building fire safety design analysis","volume-title":"Intelligent building fire safety and smart firefighting","author":"Zeng","year":"2024"},{"key":"2024073108030073500_bib40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/B978-0-12-824073-1.00011-3","article-title":"Smart building fire safety design driven by artificial intelligence","volume-title":"Interpretable machine learning for the analysis, design, assessment, and informed decision making for civil infrastructure","author":"Zeng","year":"2024"},{"key":"2024073108030073500_bib41","doi-asserted-by":"crossref","first-page":"107858","DOI":"10.1016\/j.jobe.2023.107858","article-title":"Smart fire detection analysis in complex building floorplans powered by GAN","volume":"79","author":"Zeng","year":"2023","journal-title":"Journal of Building Engineering"},{"key":"2024073108030073500_bib42","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1007\/s10694-023-01461-0","article-title":"Revisiting Alpert\u2019s correlations: Numerical exploration of early-stage building fire and detection","volume":"59","author":"Zeng","year":"2023","journal-title":"Fire Technology"},{"key":"2024073108030073500_bib43","doi-asserted-by":"crossref","first-page":"102483","DOI":"10.1016\/j.csite.2022.102483","article-title":"Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces","volume":"40","author":"Zeng","year":"2022","journal-title":"Case Studies in Thermal Engineering"},{"key":"2024073108030073500_bib44","doi-asserted-by":"crossref","first-page":"103579","DOI":"10.1016\/j.firesaf.2022.103579","article-title":"Real-time forecast of compartment fire and flashover based on deep learning","volume":"130","author":"Zhang","year":"2022","journal-title":"Fire Safety Journal"},{"key":"2024073108030073500_bib45","doi-asserted-by":"crossref","first-page":"105363","DOI":"10.1016\/j.jobe.2022.105363","article-title":"Building Artificial-Intelligence Digital Fire (AID-Fire) system: A real-scale demonstration","volume":"62","author":"Zhang","year":"2022","journal-title":"Journal of Building Engineering"},{"key":"2024073108030073500_bib46","doi-asserted-by":"crossref","first-page":"104631","DOI":"10.1016\/j.tust.2022.104631","article-title":"Smart real-time forecast of transient tunnel fires by a dual-agent deep learning model","volume":"129","author":"Zhang","year":"2022","journal-title":"Tunnelling and Underground Space Technology"},{"key":"2024073108030073500_bib47","doi-asserted-by":"crossref","first-page":"102190","DOI":"10.1016\/j.aei.2023.102190","article-title":"Design-condition-informed shear wall layout design based on graph neural networks","volume":"58","author":"Zhao","year":"2023","journal-title":"Advanced Engineering Informatics"},{"key":"2024073108030073500_bib48","doi-asserted-by":"crossref","first-page":"105499","DOI":"10.1016\/j.jobe.2022.105499","article-title":"Intelligent beam layout design for frame structure based on graph neural networks","volume":"63","author":"Zhao","year":"2023","journal-title":"Journal of Building Engineering"},{"key":"2024073108030073500_bib49","doi-asserted-by":"crossref","first-page":"115170","DOI":"10.1016\/j.engstruct.2022.115170","article-title":"Intelligent design of shear wall layout based on attention-enhanced generative adversarial network","volume":"274","author":"Zhao","year":"2023","journal-title":"Engineering Structures"},{"key":"2024073108030073500_bib50","doi-asserted-by":"crossref","first-page":"105223","DOI":"10.1016\/j.autcon.2023.105223","article-title":"Beam layout design of shear wall structures based on graph neural networks","volume":"158","author":"Zhao","year":"2024","journal-title":"Automation in Construction"},{"key":"2024073108030073500_bib51","doi-asserted-by":"crossref","first-page":"104838","DOI":"10.1016\/j.jobe.2022.104838","article-title":"Intelligent design method for beam and slab of shear wall structure based on deep learning","volume":"57","author":"Zhao","year":"2022","journal-title":"Journal of Building Engineering"},{"key":"2024073108030073500_bib52","first-page":"222","article-title":"Fire safety design strategy of special space at the top of super high-rise building","volume-title":"Advances in transdisciplinary engineering","author":"Zheng","year":"2022"},{"key":"2024073108030073500_bib54","doi-asserted-by":"crossref","first-page":"107207","DOI":"10.1016\/j.engappai.2023.107207","article-title":"A text classification-based approach for evaluating and enhancing the machine interpretability of building codes","volume":"127","author":"Zheng","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2024073108030073500_bib53","doi-asserted-by":"crossref","first-page":"104524","DOI":"10.1016\/j.autcon.2022.104524","article-title":"Knowledge-informed semantic alignment and rule interpretation for automated compliance checking","volume":"142","author":"Zheng","year":"2022","journal-title":"Automation in Construction"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/advance-article-pdf\/doi\/10.1093\/jcde\/qwae053\/58230887\/qwae053.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/11\/3\/359\/58697940\/qwae053.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/11\/3\/359\/58697940\/qwae053.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:43:18Z","timestamp":1722476598000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/11\/3\/359\/7693131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,1]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwae053","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,6]]},"published":{"date-parts":[[2024,5,1]]}}}