{"title":"修正:使用火灾试验、统计方法和人工智能的砌体抗压强度的广义温度相关材料模型","authors":"Aditya Daware, M. Z. Naser, Ghada Karaki","doi":"10.1007/s44150-022-00023-2","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"2 2","pages":"231 - 231"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correction to: Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence\",\"authors\":\"Aditya Daware, M. Z. Naser, Ghada Karaki\",\"doi\":\"10.1007/s44150-022-00023-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":100117,\"journal\":{\"name\":\"Architecture, Structures and Construction\",\"volume\":\"2 2\",\"pages\":\"231 - 231\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Architecture, Structures and Construction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s44150-022-00023-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Architecture, Structures and Construction","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s44150-022-00023-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Correction to: Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence