Exhibition of paintings by Rainer Kurm in Gallery A

VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Moment–curvature analysis of concrete beams reinforced with steel bars and fibres“ prepared at VILNIUS TECH by Amarjeet Kumar. The dissertation was prepared in 2021–2026. Scientific consultant – Prof. Dr Habil. Gintaris Kaklauskas. The dissertation was defended at the public meeting of the Dissertation Defence Council of the Scientific Field of Civil Engineering in the Aula Doctoralis Meeting Hall of Vilnius Gediminas Technical University at 2 p.m. on 16 September 2026. This dissertation presents a simplified curvature-based analytical framework for predicting the cracking behaviour of reinforced and steel fibre-reinforced concrete (R/SFRC) beams subjected to flexural loading. Existing analytical and code-based approaches rely on experimentally determined residual tensile-strength parameters and simplified constitutive assumptions, resulting in inconsistent deformation predictions. Therefore, this research develops an analytical model to predict the moment–curvature response of R/SFRC members without direct dependence on residual tensile strength testing. The First Chapter examines the mechanisms governing deformation and cracking in R/SFRC beams with and without steel bars subjected to bending. Various analytical approaches for evaluating serviceability performance are reviewed, with particular emphasis on curvature-based analysis as a fundamental parameter for describing structural response. Existing curvature models and the theoretical backgrounds are critically discussed, followed by a survey of previously proposed curvature formulations for deformation and cracking assessment. The Second Chapter presents the development of an enhanced analytical moment–curvature model for R/SFRC members by extending the formulation proposed by Kaklauskas et al. (2024). The proposed model incorporates fibre contribution at both cracking and fully cracked stages through two parameters, γ₁ and γ₂. In addition, the ACI-based cracking moment formulation was replaced with a Eurocode 2 approach to improve compatibility with European design standards. Regression-based expressions for these parameters were established using an extensive experimental database and validated against existing analytical models and code-based formulations, including Model Code 2020 and RILEM 162 TDF. The proposed model demonstrated improved predictive accuracy, achieving an overall prediction error of approximately 1.5% with a coefficient of variation of 0.228. The Third Chapter presents an experimental and analytical investigation of R/SFRC beams reinforced with conventional and dual hooked-end steel fibres. The proposed framework successfully described the tension stiffening behaviour of the tested members. Beams reinforced with dual hooked-end fibres exhibited significantly higher γ₂ values, indicating enhanced post-cracking behaviour, extended tension-stiffening response, and improved serviceability performance. Comparisons with standard code-based formulations showed that Model Code 2020 generally provided more accurate moment–curvature predictions than RILEM 162 TDF, while code-based approaches tended to overestimate stiffness, particularly for beams with lower reinforcement ratios. Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.

Are you looking for eBooks for research and study? We invite you to use the VILNIUS TECH eBook platform ebooks.vilniustech.lt. VILNIUS TECH eBooks – More than 700 VILNIUS TECH eBooks published in a wide range of academic disciplines. You will be able to: Choose books based on your interests and relevant topics. Purchase an electronic version of a book for a week, a month or a semester. Purchase a printed copy of the VILNIUS TECH book. Are you a member of the VILNIUS TECH community? The VILNIUS TECH community members can access all eBooks on ebooks.vilniustech.lt free of charge while using the university network (e.g., at University) or working/studying remotely and using VPN. Where can you read eBooks? You can read eBooks on your PC (Windows, Mac), smartphone, and tablet. Where can you find your eBook subscription? Purchased or subscribed eBooks can be found in the Bookshelf tab. Where can you find all eBooks? All VILNIUS TECH eBooks can be found in the Catalogue tab. How do you search? You can search for the eBook by entering a title or keyword in the search box or using Advanced Search. You can select where to search for your keyword in the Advanced Search: Catalogue - will search for books in the catalogue; Description - will search for the keyword in full-text documents (PDF). If you select Phrase Match, you can search for the exact phrase you need. You can also narrow your search by author, eISBN, year, or category. How do you filter eBooks? You can filter eBooks by Publication date and Categories using the Filter by function. Printed books are available for purchase. It is important for the reader to make sure that a book contains all the information they need when planning to buy it. It is possible to search for the required information (words, phrases, or sentences) among all books available on ebooks.vilniustech.lt. In addition, before purchasing a book, it is possible to browse the table of contents and read excerpts. All VILNIUS TECH eBooks have an individual DOI provided in URL format. If you encounter such a link in another source of information (books, articles, websites, etc.), type it into your web browser, and you will be redirected to the specific eBook. If you have questions about eBook acquisition and subscriptions, please contact ebooks@vilniustech.lt. * The VILNIUS TECH community members can access the eBooks through the University's computer network and from home by connecting to the VILNIUS TECH network through a VPN. The VILNIUS TECH community can connect to an institutional account. To read eBooks offline, users need to download the Mon`k offline app by navigating to the home page and accessing the download section. To install the app on your phone, download the Mon`k app from the Google Play Store or Apple App Store.