{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T01:22:24Z","timestamp":1772673744382,"version":"3.50.1"},"reference-count":25,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2019,6,10]],"date-time":"2019-06-10T00:00:00Z","timestamp":1560124800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSFE"],"published-print":{"date-parts":[[2019,6,10]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Fire degradation is an extremely important risk that threatens timber structures. It is therefore normal that timber design codes include provisions for the design and verification of structures under fire loading. Eurocode 5 is no exception to this, but the simplified methods presented in the code show some inconsistencies, and the advanced method is not practical to use for design purposes. Artificial neural networks (ANNs) have the ability to model complex problems and have been used in a variety of construction engineering problems. They can learn from a subset of data, and then they can be used to predict the results for other input parameters. The purpose of this study is to present the possibility of the use of ANNs for the prediction of temperatures in rectangular timber cross sections, under fire exposure.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this work, a multilayer feedforward ANN has been trained to predict the temperatures within a timber cross section, using as input the size of the cross section, the timber density, the time of exposure and the coordinates of the point within the cross section.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results obtained clearly indicate that ANN can be used to predict the temperatures in a timber cross section subjected to fire.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>ANNs have not been used for the prediction of temperatures in timber cross sections. The use of ANN makes the temperature prediction under a standard fire loading in a cross section extremely easy to implement in any code. These results can be used to calculate the strength of the elements after fire.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jsfe-06-2018-0012","type":"journal-article","created":{"date-parts":[[2019,1,23]],"date-time":"2019-01-23T03:12:19Z","timestamp":1548213139000},"page":"233-244","source":"Crossref","is-referenced-by-count":8,"title":["ANN prediction of fire temperature in timber"],"prefix":"10.1108","volume":"10","author":[{"given":"Paulo","family":"Cachim","sequence":"first","affiliation":[]}],"member":"140","reference":[{"issue":"9","key":"key2020092511400408700_ref001","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.conbuildmat.2005.01.047","article-title":"Prediction of shear strength of steel fiber RC beams using neural networks","volume":"20","year":"2006","journal-title":"Construction and Building Materials"},{"issue":"4","key":"key2020092511400408700_ref002","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/S0379-7112(01)00065-0","article-title":"Prediction of fire resistance of concrete filled tubular steel columns using neural networks","volume":"37","year":"2002","journal-title":"Fire Safety Journal"},{"issue":"6","key":"key2020092511400408700_ref003","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.1016\/j.conbuildmat.2008.12.003","article-title":"Neural networks for predicting compressive strength of structural light weight concrete","volume":"23","year":"2009","journal-title":"Construction and Building Materials"},{"issue":"2","key":"key2020092511400408700_ref004","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.commatsci.2007.07.011","article-title":"Predicting the compressive strength of steel fiber added lightweight concrete using neural network","volume":"42","year":"2008","journal-title":"Computational Materials Science"},{"issue":"11\/12","key":"key2020092511400408700_ref005","first-page":"663","article-title":"Using neural networks to predict workability of concrete incorporating metakaolin and fly ash","volume":"34","year":"2003","journal-title":"Advances in Engineering Software"},{"issue":"5","key":"key2020092511400408700_ref006","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.advengsoft.2008.05.005","article-title":"Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network","volume":"40","year":"2009","journal-title":"Advances in Engineering Software"},{"issue":"3","key":"key2020092511400408700_ref007","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1002\/fam.985","article-title":"Comparison between the charring rate model and the conductive model of Eurocode 5","volume":"33","year":"2009","journal-title":"Fire and Materials"},{"issue":"5","key":"key2020092511400408700_ref008","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.conbuildmat.2007.01.029","article-title":"Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings","volume":"22","year":"2008","journal-title":"Construction and Building Materials"},{"key":"key2020092511400408700_ref009","volume-title":"EN 1991-1-2:2002. Eurocode 1: Actions on Structures \u2013 Part 1-2: General actions \u2013 Actions on Structures Exposed to Fire","author":"CEN","year":"2002"},{"key":"key2020092511400408700_ref010","volume-title":"EN 1995-1-2:2004: Eurocode 5: Design of Timber Structures \u2013 Part 1-2: General \u2013 Structural Fire Design","author":"CEN","year":"2004"},{"key":"key2020092511400408700_ref011","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.conbuildmat.2016.03.214","article-title":"A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks","volume":"114","year":"2016","journal-title":"Construction and Building Materials"},{"issue":"7","key":"key2020092511400408700_ref012","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1016\/j.conbuildmat.2007.04.004","article-title":"Prediction of elastic modulus of normal and high strength concrete by artificial neural networks","volume":"22","year":"2008","journal-title":"Construction and Building Materials"},{"issue":"7","key":"key2020092511400408700_ref013","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/S0950-0618(01)00006-X","article-title":"Neural networks for predicting properties of concretes with admixtures","volume":"15","year":"2001","journal-title":"Construction and Building Materials"},{"issue":"1","key":"key2020092511400408700_ref014","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.aei.2007.07.001","article-title":"Towards the next generation of artificial neural networks for civil engineering","volume":"22","year":"2008","journal-title":"Advanced Engineering Informatics"},{"issue":"2","key":"key2020092511400408700_ref015","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/fam.819","article-title":"Charring rates and temperature profiles of wood sections","volume":"27","year":"2003","journal-title":"Fire and Materials"},{"issue":"3","key":"key2020092511400408700_ref016","doi-asserted-by":"crossref","first-page":"143","DOI":"10.62913\/engj.v42i3.856","article-title":"SAFIR. A thermal\/Structural program modelling structures under Fire","volume":"42","year":"2005","journal-title":"Engineering Journal"},{"issue":"2","key":"key2020092511400408700_ref017","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0950-0618(97)00007-X","article-title":"Concrete strength prediction by means of neural network","volume":"11","year":"1997","journal-title":"Construction and Building Materials"},{"issue":"9","key":"key2020092511400408700_ref018","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.advengsoft.2009.01.005","article-title":"Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete","volume":"40","year":"2009","journal-title":"Adv. Eng. Softw"},{"issue":"9","key":"key2020092511400408700_ref019","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.conbuildmat.2005.01.054","article-title":"Predicting the compressive strength and slump of high strength concrete using neural network","volume":"20","year":"2006","journal-title":"Construction and Building Materials"},{"issue":"3","key":"key2020092511400408700_ref020","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.conbuildmat.2006.08.015","article-title":"Dynamic soil-structure interaction analysis of buildings by neural networks","volume":"22","year":"2008","journal-title":"Construction and Building Materials"},{"issue":"1","key":"key2020092511400408700_ref021","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.conbuildmat.2008.01.014","article-title":"Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN","volume":"23","year":"2009","journal-title":"Construction and Building Materials"},{"issue":"9","key":"key2020092511400408700_ref022","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.advengsoft.2008.12.008","article-title":"Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic","volume":"40","year":"2009","journal-title":"Advances in Engineering Software"},{"issue":"5","key":"key2020092511400408700_ref023","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.advengsoft.2008.05.002","article-title":"Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks","volume":"40","year":"2009","journal-title":"Advances in Engineering Software"},{"issue":"10","key":"key2020092511400408700_ref024","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1016\/j.matdes.2008.04.005","article-title":"Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic","volume":"29","year":"2008","journal-title":"Materials and Design"},{"key":"key2020092511400408700_ref025","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.firesaf.2013.01.006","article-title":"Prediction of temperature of tubular truss under fire using artificial neural networks","volume":"56","year":"2013","journal-title":"Fire Safety Journal"}],"container-title":["Journal of Structural Fire Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JSFE-06-2018-0012\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JSFE-06-2018-0012\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:23:17Z","timestamp":1753395797000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/jsfe\/article\/10\/2\/233-244\/252211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,10]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,6,10]]}},"alternative-id":["10.1108\/JSFE-06-2018-0012"],"URL":"https:\/\/doi.org\/10.1108\/jsfe-06-2018-0012","relation":{},"ISSN":["2040-2317"],"issn-type":[{"value":"2040-2317","type":"print"}],"subject":[],"published":{"date-parts":[[2019,6,10]]}}}