Database for training predictive AI-based assessment algorithms in Structural Health Monitoring
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Structural engineering and building technology (MPSEB), MSc
Publicerad
2023
Författare
Nässlander, Pär
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Structural health monitoring (SHM) is a useful technique for ensuring integrity and
safety of a structure. Although it is still under development the concept of SHM is to
detect possible errors or weaknesses in an early stage and address suitable maintenance
or strengthening options. According to Berrocal et al. (2021) a new method in the field
of SHM has been established recently by measuring the strain using distributed optical
fiber sensors (DOFS). Furthermore, implementing DOFS in reinforced concrete proves
to be an accurate and reliable tool for measuring crack widths and deflection.
A promising possibility with DOFS is to make automated assessments and predictions
of various structural members by combining DOFS with artificial intelligence (AI). The
learning capability of AI would be based on a training database comprising the strain
data from DOFS and the crack information. Apparently, the economic and
environmental cost to produce many different scenarios in a laboratory would be very
high. One alternative is to simulate the various scenarios in finite element analysis
(FEA) models obtaining artificial strain profiles together with the crack locations.
However, this method requires good compatibility between the strain from FE models
and DOFS which is not the case. Therefore, postprocessing the artificial strains can
hopefully make the two become closely related to each other. If this becomes reality, a
future AI training database can be built by collecting strains from FE models with
different geometries, material properties, loading conditions and support setups.
This thesis aims to examine the problem stated above, i.e., if strains from FE models
can, trough manipulation, become comparable to the strains in DOFS. Attempting to
answer this question the work was divided in two parts, the calibration procedure, and
the strain postprocessing. In the first part, the ambition was to create a digital copy with
similar structural behavior as three beams from an experiment made earlier. As a result,
a major calibration process took place comparing different random field methods using
the JCSS Probabilistic Model Code in concrete. In addition, more detailed comparisons
were made between different bond slip characteristics. In the second part, the
processing methods were implemented with the intention of synchronising the artificial
strains from FE model with the strains from DOFS.
Although the FE model had success of demonstrating identical behavior as the tested
beams it was hard reproduce the shape from the DOFS. The strains from the DOFS
illustrate a rather detailed strain profile, but without the capability of reflecting all the
cracks. Under these circumstances it is difficult to reach accordance between the two
type of strains. The results indicate that a thinner type of DOFS would perhaps give
more satisfying results. This is however left for future research.
Beskrivning
Ämne/nyckelord
Structural health monitoring, distributed optical fiber sensors, finite element analysis, artificial intelligence