Automated assessment of monitoring data

Table of contents

1. 1. 1. Project data
      2. openLAB – Eine Forschungsbrücke in der Lausitz
      3. Report from year book 2023
      4. Report from year book 2022

Project data

Titel | Title TP der TU Dresden im Verbundprojekt IDA-KI: Automatisierte Bewertung der Monitoringdaten von Infrastrukturbauwerken | SP of TU Dresden in the joint research project IDA-KI: Automated assessment of monitoring data for infrastructure constructions Förderer | Funding Bundesministerium für Digitales und Verkehr (BMDV) / mFUND Zeitraum | Period 01/2022 – 06/2025 Verbundvorhaben- und Teilprojektleiter | Leader of joint and subproject Prof. Dr.-Ing. Steffen Marx Team | Team Max Herbers, Bertram Richter (ehemalig | former: Jonas Scharf) Partner | Partners Institut für Digitales und Autonomes Bauen, TU Hamburg | Marx Krontal Partner GmbH, Weimar | Hentschke Bau GmbH, Bautzen Assoziierte Partner | Associated partners Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin | Autobahn GmbH, Berlin | Bundesanstalt für Straßenwesen (BASt), Bergisch Gladbach

openLAB – Eine Forschungsbrücke in der Lausitz

As part of the IDA-KI project, a 45 m long research bridge is being built on which a comprehensive monitoring system is being installed. The bridge will be developed as a real laboratory – openLAB – and, after a one-year reference phase, will be loaded to a state of severe damage. Further information: openLAB

Report from year book 2023

A research bridge in Lusatia

A digital twin is a virtual representation of a real object. For engineering structures such as bridges, monitoring systems can be used to provide information on the structure’s condition at any point in time. In the future, it should be possible to identify structural damages at an early stage in order to avoid consequential damage and potential traffic restrictions. However, handling big data is currently a challenge due to the lack of automated approaches for data analysis and evaluation.

This is where the IDA-KI project consortium comes in. Approaches are being developed that enable reliable differentiation between measurement errors and structural damages. For validation, load tests are being carried out on a 45 m long research bridge – the openLAB – up to the state of severe damage. Monitoring data is put into database which contains characteristic signals on damages, measurement errors as well as information on the structure in its undamaged reference state under real environmental conditions.

Some of the sensors on the openLAB were installed in the formwork already prior to concreting the precast prestressed concrete elements so that monitoring could be realized from “hour zero”. In preliminary tests, the suitability of the measurement technology was tested. Of particular interest is the use of distributed fiber optic sensors (DFOS), which form an artificial nervous system of the bridge with a total length of almost 1 km. The fiber optic sensors enable quasi-continuous strain measurement, from which, for example, information on the degree of prestressing or possible crack formation can be derived. The software framework “fosanalysis” was developed for semi-automated evaluation of the large data sets. It is available as free software and is being further developed as part of the project. All information on the structural condition, including the identified damages, will be provided in an as-maintained model.

The openLAB will be completed in February 2024 and will also be available to external research groups willing to test and validate sensor technology and monitoring techniques. Cooperations with the mFUND projects smart_tendon and ANYTWIN are already in place.

Report from year book 2022

Big data? Smart data!

Infrastructure constructions are subjected to constant ageing, which is why today their condition is to be inspected manually at regular intervals. A repair action is planned only when damage is detected during the regular inspection of the structure. This reactive approach means that damage often remains undetected for a long time, resulting in increased maintenance costs. In the future, this problem-oriented approach is to be replaced by a data-based, predictive maintenance management system. The basis for a reliable assessment of building condition in near real time is structural monitoring. However, current monitoring applications require time-consuming manual evaluation, and it is difficult to detect ageing of the measurement system or measurement errors.

In the project IDA-KI, fully automated evaluation algorithms for monitoring data are developed with the help of machine learning and integrated into a monitoring concept spanning the entire service life from hour zero. The algorithms could already be successfully trained for a real data set of a bridge. For fault detection and correction, it is checked which sensors are correlating with each other. If one sensor shows strong deviations compared to its “partners”, this is an indication of a measurement error. The analytical redundancy approach eliminates the need for additional redundant sensors or a numerical model.

A demonstration bridge is currently under planning, which will be equipped with sensors, e.g. with fibre optic sensors, during construction. This allows the monitoring concept and the evaluation algorithms to be validated in fast motion. Employing load tests as well as targeted damage to redundant measurement technology, a real database can be obtained for the first time. In the future, it will therefore be possible to distinguish between measurement errors, influences from ageing of the measurement system and structural changes to the structure. Condition indicators should enable the intuitive interpretability of large amounts of data. The demonstrator, which will be erected in Bautzen, will be expanded as a real laboratory and will also serve as a place for scientific exchange in the coal region after the project period.