Data driven structural behaviour modelling in civil engineering

Code

J2-2490 (B) - included in ARIS records

Head

PhD Boštjan Brank

Period

11/1/2020 - 10/31/2023

Range in 2023

1.34 FTE

Science

Engineering sciences and technologies (13)
Other (1)

Reseacher status

Researcher (13)
Junior expert or technical associate (1)

Education

Doctor's degree (8)
Other (6)

Sex

Woman (2)
Man (12)

Status

Employed at RO and RRD (11)
No data on employment in RO (3)

No. of publications

1–9 (2)
10–99 (6)
100–999 (6)

Projects / Programmes source: ARIS

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

Evaluation of bibliographic research performance indicators according to ARIS methodology

Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases

source: WoS

source: Scopus

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	143
2.	20631	PhD Uroš Bohinc	Civil engineering	Researcher	2020 - 2023	113
3.	10562	PhD Boštjan Brank	Civil engineering	Head	2020 - 2023	471
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	6
7.	53602	Luka Gradišar	Civil engineering	Junior researcher	2020 - 2023	18
8.	17037	Jan Kalin	Civil engineering	Researcher	2020 - 2021	104
9.	27532	PhD Maja Kreslin	Civil engineering	Researcher	2020 - 2023	167
10.	54966	Nina Kumer	Civil engineering	Technical associate	2021	3
11.	53352	PhD Blaž Kurent	Civil engineering	Junior researcher	2020 - 2023	29
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	10

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	25,924
2.	1502	Slovenian National Building and Civil Engineering Institute	Ljubljana	5866324000	10,259

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.