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Projects / Programmes source: ARIS

Data driven structural behaviour modelling in civil engineering

Research activity

Code Science Field Subfield
2.01.03  Engineering sciences and technologies  Civil engineering  Constructions in civil engineering 

Code Science Field
2.01  Engineering and Technology  Civil engineering 
Keywords
large structures, structural identification, Bayesian model updating, vibration tests, structural health monitoring, big data analysis, data-based structural modelling
Evaluation (rules)
source: COBISS
Researchers (14)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  35411  PhD Andrej Anžlin  Civil engineering  Researcher  2020 - 2023  135 
2.  20631  PhD Uroš Bohinc  Civil engineering  Researcher  2020 - 2023  110 
3.  10562  PhD Boštjan Brank  Civil engineering  Head  2020 - 2023  465 
4.  28903  Simon Detellbach    Technical associate  2022 - 2023  191 
5.  26550  PhD Jaka Dujc  Civil engineering  Researcher  2022 - 2023  50 
6.  56282  Tomislav Franković  Civil engineering  Researcher  2022 - 2023 
7.  53602  Luka Gradišar  Civil engineering  Junior researcher  2020 - 2023  18 
8.  17037  Jan Kalin  Civil engineering  Researcher  2020 - 2021  102 
9.  27532  PhD Maja Kreslin  Civil engineering  Researcher  2020 - 2023  162 
10.  54966  Nina Kumer  Civil engineering  Technical associate  2021 
11.  53352  PhD Blaž Kurent  Civil engineering  Junior researcher  2020 - 2023  26 
12.  39204  PhD Marko Lavrenčič  Civil engineering  Researcher  2020 - 2023  34 
13.  54082  Luka Trček  Traffic systems  Researcher  2021 - 2023  26 
14.  56372  PhD Tomo Veldin  Mechanics  Researcher  2022 - 2023 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0792  University of Ljubljana, Faculty of Civil and Geodetic Engineering  Ljubljana  1626981  25,704 
2.  1502  Slovenian National Building and Civil Engineering Institute  Ljubljana  5866324000  10,450 
Abstract
The project proposes: (a) development of procedures for Bayesian finite element model updating and uncertainty quantification for large-scale civil structures under service loading, and (b) application of advanced methods of artificial intelligence for analyses of structural health monitoring data in order to build data models. The related overall objectives of the project are: (a) to choose a few representative civil engineering structures, perform in-situ forced vibrations test, and apply structural identification process in the framework of Bayesian inversion in order to improve fidelity of the finite element models, and (b) to undermine the potential application possibilities of advanced methods of artificial intelligence to ameliorate the integration of vibration tests data and structural health monitoring data into the maintenance, and present this for a large highway bridge. The scientific objectives of the project are: (i) to assess the advantages and disadvantages of the uncertainty quantification, sensitivity analysis and Bayesian finite element model updating for large-scale civil engineering structures, (ii) to bring out possibilities of an automated update process of data driven model when receiving the sensors data continuously, (iii) to develop a method to identify systematic modelling error of the finite element model, and (iv) to test a novel idea of implementing a mixture density network for the finite element model update and compare it with the conventional and recent existing techniques. The plan is to study a few large-scale structures in order to assess existing and novel technologies and ideas. The structural health monitoring data will be available for one large highway bridge.
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