Pub Date : 2023-10-02DOI: 10.1080/10168664.2023.2239013
Ignacio Paya-Zaforteza, Stefan Zmigrodzki, Flavio Stochino
{"title":"From Flames to Resilience: New Perspectives for the Design of Structures Against Fire Hazards","authors":"Ignacio Paya-Zaforteza, Stefan Zmigrodzki, Flavio Stochino","doi":"10.1080/10168664.2023.2239013","DOIUrl":"https://doi.org/10.1080/10168664.2023.2239013","url":null,"abstract":"","PeriodicalId":51281,"journal":{"name":"Structural Engineering International","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/10168664.2023.2265186
{"title":"IABSE Congress 2024 San Jose","authors":"","doi":"10.1080/10168664.2023.2265186","DOIUrl":"https://doi.org/10.1080/10168664.2023.2265186","url":null,"abstract":"","PeriodicalId":51281,"journal":{"name":"Structural Engineering International","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/10168664.2023.2267348
{"title":"A Report on IABSE New Delhi Congress 2023","authors":"","doi":"10.1080/10168664.2023.2267348","DOIUrl":"https://doi.org/10.1080/10168664.2023.2267348","url":null,"abstract":"","PeriodicalId":51281,"journal":{"name":"Structural Engineering International","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/10168664.2023.2267349
"Launch of the Case Studies 4: Case Studies on Failure Investigations in Structural and Geotechnical Engineering." Structural Engineering International, 33(4), pp. 729–730
“个案研究4:结构及岩土工程的破坏调查个案研究”土木工程学报，33(4)，pp. 729-730
{"title":"Launch of the Case Studies 4: Case Studies on Failure Investigations in Structural and Geotechnical Engineering","authors":"","doi":"10.1080/10168664.2023.2267349","DOIUrl":"https://doi.org/10.1080/10168664.2023.2267349","url":null,"abstract":"\"Launch of the Case Studies 4: Case Studies on Failure Investigations in Structural and Geotechnical Engineering.\" Structural Engineering International, 33(4), pp. 729–730","PeriodicalId":51281,"journal":{"name":"Structural Engineering International","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractConventional evaluation of the overall mechanical properties and ultimate flexural capacity of prestressed hollow core slabs after a fire exposure depends heavily on the inversion of fire scene temperature. To avoid this drawback, this paper presents a new methodology which combines a generalized regression neural network (GRNN) with conventional non-destructive testing technology. Thereby, a neural network model for predicting the material performance parameters after fire exposure is obtained based on conventional testing indices. A hollow core slab bridge is used as an example, and the applicability of the trained network model is confirmed using numerical simulation and a field failure test. Results show that the overall relative error of GRNN in predicting the key performance parameters of the bridge after fire exposure is less than 10%. Further, because of the good thermal inertia of the concrete, the relative error in predicting the material performance parameters of steel after a fire is less than 5%. Moreover, the ultimate flexural capacity of the prestressed hollow core slab after a fire can be accurately evaluated by feeding the material performance parameters predicted by GRNN neural network into the finite element (FE) model.Keywords: firehollow core slabmachine learningneuronic networkultimate bearing capacity Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementSome or all data, models, or codes generated or used during the study are available from the corresponding author by request.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [grant number 2017YFE0103000]; Science and Technology Plan Project of Shandong Provincial Department of Transportation [grant number 2017B62]; Central Research Institutes of Basic Research and Public Service Special Operations [grant number 2021-9060a].
{"title":"Applications of Machine Learning to Predict the Flexural Bearing Capacity of Hollow Core Slabs After Fire Exposure","authors":"Chaowei Hao, Baoyao Lin, Mingfa Wang, Laiyong Wang, Dejin Xing","doi":"10.1080/10168664.2023.2211591","DOIUrl":"https://doi.org/10.1080/10168664.2023.2211591","url":null,"abstract":"AbstractConventional evaluation of the overall mechanical properties and ultimate flexural capacity of prestressed hollow core slabs after a fire exposure depends heavily on the inversion of fire scene temperature. To avoid this drawback, this paper presents a new methodology which combines a generalized regression neural network (GRNN) with conventional non-destructive testing technology. Thereby, a neural network model for predicting the material performance parameters after fire exposure is obtained based on conventional testing indices. A hollow core slab bridge is used as an example, and the applicability of the trained network model is confirmed using numerical simulation and a field failure test. Results show that the overall relative error of GRNN in predicting the key performance parameters of the bridge after fire exposure is less than 10%. Further, because of the good thermal inertia of the concrete, the relative error in predicting the material performance parameters of steel after a fire is less than 5%. Moreover, the ultimate flexural capacity of the prestressed hollow core slab after a fire can be accurately evaluated by feeding the material performance parameters predicted by GRNN neural network into the finite element (FE) model.Keywords: firehollow core slabmachine learningneuronic networkultimate bearing capacity Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementSome or all data, models, or codes generated or used during the study are available from the corresponding author by request.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [grant number 2017YFE0103000]; Science and Technology Plan Project of Shandong Provincial Department of Transportation [grant number 2017B62]; Central Research Institutes of Basic Research and Public Service Special Operations [grant number 2021-9060a].","PeriodicalId":51281,"journal":{"name":"Structural Engineering International","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135536661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/10168664.2023.2254325
Junhu Gong, Jiacheng Feng, Shiqiang Qin
AbstractTraditional medium-low speed Maglev track separated beam structures have drawbacks such as large structural height and neglect of F-type rail stiffness. This study proposes a new integrated track beam for medium-low speed maglev transportation. Finite element analysis is employed to compare the strength, stiffness, and natural frequencies of the integrated track beam with the existing separated track beam. The influence of beam height on the overall mechanical performance of the integrated track beam is analyzed. The ultimate bearing capacity of the steel-concrete composite joint in the integrated track beam is investigated through full-scale model testing. The results demonstrate that the proposed integrated track beam exhibits a 28% increase in flexural stiffness. The mid-span deflection is reduced by 19.9% under static and live loads. The first-order vertical natural frequency increases by 13.6%. The main factor governing the minimum beam height of the integrated track beam is the deflection limit under static and live loads. The beam height can be optimized from 2.1 m to 1.6 m. The model testing reveals that the F-type rail is controlled by torsional stiffness and can withstand 1.3 times the design load. The ultimate bearing capacity of the steel-concrete composite joint is 4.5 times the design load, providing sufficient load reserves.Keywords: Maglev transitintegrated track beammechanical propertiessteel concrete jointbearing capacitymodel test Data Availability StatementThe authors confirm that the data supporting the findings of this study are available within the article.Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Major Science and Technology project of China Railway Construction Co., Ltd. [2018-A01].
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Pub Date : 2023-09-15DOI: 10.1080/10168664.2023.2249937
J. Rocha Segundo, R. A. M. Silveira, A. R. D. Silva, R. C. Barros, Í. J. M. Lemes
AbstractWhen solving nonlinear algebraic equations that arise from discretization using the finite element method (FEM), it is often observed that the standard Newton-Raphson (N-R) iteration either fails to converge or necessitates a large number of iterations in the vicinity of critical points. This work proposes an additional numerical strategy, known as the Potra-Pták iterative cycle, to improve the efficiency of solving highly nonlinear structural problems. Therefore, the focus here is on making the nonlinear solver more robust and efficient, allowing the analysis of more complex nonlinear structures. In the Potra-Pták iterative cycle, two corrections of the objective function (energy function) are performed. The introduction of a second correction in the iterative cycle makes the Potra-Pták strategy more efficient than the standard or modified N-R iterations. This numerical strategy was implemented in the homemade Computational System for Advanced Structural Analysis (CS-ASA) program. The program is based on the FEM and is capable of performing static and dynamic nonlinear analysis of steel, concrete, and composite structures, and its efficiency is then verified through the analysis of slender frames and arches. The algorithm details for solving the nonlinear structural problem, characterized by the Potra-Pták scheme, are provided.Keywords: Potra-Pták iterative cycleNewton-Raphson methodCS-ASA programnonlinear structure problemfinite element method AcknowledgementsThe authors thank CNPq and CAPES (Brazil Federal Research Agencies), FAPEMIG (Minas Gerais State Research Agency), PROPEC/UFOP, PROPPI/UFOP and UFLA for their support in the development of this research.Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementThe data that support the findings of this study are available from the corresponding author, RAM Silveira, upon reasonable request.Additional informationFundingThis work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico: [Grant Number 307898/2019-9]; Fundação de Amparo à Pesquisa do Estado de Minas Gerais: [Grant Number TEC-PPM-00221-18].
摘要在用有限元法求解由离散化引起的非线性代数方程时，经常发现标准牛顿-拉夫森(N-R)迭代要么不能收敛，要么需要在临界点附近进行大量迭代。这项工作提出了一个额外的数值策略，称为Potra-Pták迭代周期，以提高求解高度非线性结构问题的效率。因此，这里的重点是使非线性求解器更加鲁棒和高效，从而能够分析更复杂的非线性结构。在Potra-Pták迭代周期中，对目标函数(能量函数)进行两次修正。在迭代周期中引入第二次修正使得Potra-Pták策略比标准或修改的N-R迭代更有效。该数值策略在自制的高级结构分析计算系统(CS-ASA)程序中实现。该程序基于有限元法，能够对钢结构、混凝土结构和组合结构进行静力和动力非线性分析，并通过对细长框架和拱的分析验证了其有效性。给出了求解以Potra-Pták方案为特征的非线性结构问题的算法细节。关键词:Potra-Pták迭代循环牛顿-拉夫森方法cs - asa程序非线性结构问题有限元法致谢感谢CNPq和CAPES(巴西联邦研究机构)，FAPEMIG(米纳斯吉拉斯州研究机构)，PROPEC/UFOP, PROPPI/UFOP和UFLA对本研究发展的支持。披露声明作者未报告潜在的利益冲突。数据可用性声明支持本研究结果的数据可根据合理要求从通讯作者RAM Silveira处获得。本研究得到了Conselho Nacional de Desenvolvimento Científico e Tecnológico的支持:[资助号307898/2019-9];米纳斯吉拉斯州财产保护基金:[批准号TEC-PPM-00221-18]。
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