{"title":"采用机器分级指示器对大型GLT梁进行弯曲试验","authors":"G. Fink, Phillipp Stadelmann, A. Frangi","doi":"10.1080/20426445.2021.1969166","DOIUrl":null,"url":null,"abstract":"ABSTRACT This paper presents large-scale bending test, up to 19 m length, on glued laminated timber (GLT) beams with well-known beam setup and a model to predict the tensile strength of finger joint connections (FJ). Machine graded timber boards, with information about the dynamic stiffness and local knottiness were used to fabricate 12 GLT beams. The position of the timber boards were tracked throughout the GLT fabrication. To quantify the quality of the FJ additional FJ were fabricated and tested in tension. Based on the results, a model to predict their strength properties based on the grading information of the involved timber boards was developed using maximum likelihood estimation for censored data.","PeriodicalId":14414,"journal":{"name":"International Wood Products Journal","volume":"12 1","pages":"258 - 266"},"PeriodicalIF":1.3000,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Bending test on large-scale GLT beams with well-known beam setup using machine grading indicator\",\"authors\":\"G. Fink, Phillipp Stadelmann, A. Frangi\",\"doi\":\"10.1080/20426445.2021.1969166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT This paper presents large-scale bending test, up to 19 m length, on glued laminated timber (GLT) beams with well-known beam setup and a model to predict the tensile strength of finger joint connections (FJ). Machine graded timber boards, with information about the dynamic stiffness and local knottiness were used to fabricate 12 GLT beams. The position of the timber boards were tracked throughout the GLT fabrication. To quantify the quality of the FJ additional FJ were fabricated and tested in tension. Based on the results, a model to predict their strength properties based on the grading information of the involved timber boards was developed using maximum likelihood estimation for censored data.\",\"PeriodicalId\":14414,\"journal\":{\"name\":\"International Wood Products Journal\",\"volume\":\"12 1\",\"pages\":\"258 - 266\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2021-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Wood Products Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/20426445.2021.1969166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, PAPER & WOOD\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Wood Products Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/20426445.2021.1969166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, PAPER & WOOD","Score":null,"Total":0}
Bending test on large-scale GLT beams with well-known beam setup using machine grading indicator
ABSTRACT This paper presents large-scale bending test, up to 19 m length, on glued laminated timber (GLT) beams with well-known beam setup and a model to predict the tensile strength of finger joint connections (FJ). Machine graded timber boards, with information about the dynamic stiffness and local knottiness were used to fabricate 12 GLT beams. The position of the timber boards were tracked throughout the GLT fabrication. To quantify the quality of the FJ additional FJ were fabricated and tested in tension. Based on the results, a model to predict their strength properties based on the grading information of the involved timber boards was developed using maximum likelihood estimation for censored data.