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Ingenieurbauwerke wie Brücken, hoch beanspruchte Verkehrsflächen oder auch Hochhäuser und deren Bauwerksteile wie z.B. Hochhausfassaden werden aus technischen und ästhetischen Gründen durch Fugen in Einzelabschnitte unterteilt. Zur Sicherstellung der Gebrauchsfähigkeit und zum Schutz des Gesamtbauwerks, aber in zunehmendem Maße auch zur statisch-konstruktiven Anbindung der Bauwerksteile, werden diese Fugenspalte in aller Regel durch spezielle Fugenfüllsysteme verschlossen. Aufgrund der hohen Sicherheitsrelevanz bei Fugenfüllungen im Glasfassadenbau fordert das Baurecht auch bei diesen Bauprodukten als Voraussetzung für die baupraktische Verwendbarkeit neben dem Nachweis der Funktionsfähigkeit auch den Nachweis der Dauerhaftigkeit. Da die hierfür bekannten Nachweismethoden zur Dauerhaftigkeit keine allgemeine Zulassungsakzeptanz finden, kann das ästhetische, bauphysikalische und ökonomische Potential von modernen geklebten Ganzglasfassaden (sogenannte SSG-Fassaden) in der Baupraxis der Bundesrepublik Deutschland derzeit nicht ausgenutzt werden. Grund dafür sind die ungenügend erfassten und in den Bewertungsverfahren simulierten Wechselwirkungen derartiger Baukonstruktionen mit der Umwelt. In diesem Beitrag soll am Beispiel moderner Fugen im Glasfassadenbau (SSG-Fassaden) eine ganzheitliche gebrauchsbezogene Versuchsmethodik zur kontrollierten Ansprache der Funktionsfähigkeit und Dauerhaftigkeit von Fugensystemen unter realitätsnah und reproduzierbar simulierten Umwelteinwirkungen vorgestellt werden. Dazu werden die maßgebenden Umwelteinflüsse und die Quantifizierung der daraus folgenden Beanspruchungen auf derartige Fugensysteme dargestellt. Basierend darauf werden eine repräsentative Beanspruchungsfunktion zur Nachstellung der maßgebenden Umwelteinflüsse und Beanspruchungen auf das System Tragrahmen - Fugenfüllung- Glasscheibe sowie eine geeignete Probendimensionierung abgeleitet. In der Konsequenz werden die Erkenntnisse in den Aufbau einer neuartigen komplexen Versuchseinrichtung überführt. Funktionsprinzip und Leistungsparameter dieser Anlage zur Umweltsimulation werden vorgestellt. Die Möglichkeiten der Systemkennzeichnung werden vorgestellt. Erste Versuchsergebnisse zeigen das Potential der neuartigen Bewertungsmethodik auf, die Kunst der Fuge im Bauwesen gebrauchsorientiert weiter zu entwickeln.
For the closure of radioactive waste disposal facilities engineered barriers- so called “drift seals” are used. The purpose of these barriers is to constrain the possible infiltration of brine and to prevent the migration of radionuclides into the biosphere. In a rock salt mine a large scale in-situ experiment of a sealing construction made of salt concrete was set up to prove the technical feasibility and operability of such barriers. In order to investigate the integrity of this structure, non-destructive ultrasonic measurements were carried out.
Therefore two different methods were applied at the front side of the test-barrier:
1 Reflection measurements from boreholes
2 Ultrasonic imaging by means of scanning ultrasonic echo methods This extended abstract is a short version of an article to be published in a special edition of ASCE Journal that will briefly describe the sealing construction, the application of the non-destructive ultrasonic measurement methods and their adaptation to the onsite conditions -as well as parts of the obtained results. From this a concept for the systematic investigation of possible contribution of ultrasonic methods for quality assurance of sealing structures may be deduced.
We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other.
We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material.
This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed.
This talk demonstrates the results of the IGSTC-project entitled "NDT-Data Fusion".
Project approach:
Nondestructive testing (NDT) of concrete buildings allows to coordinate efficient repair measures. Multi-sensor platforms collect large data sets. Nevertheless, data analysis is typically performed manually. Data Fusion uses the full potential of a multi-sensory data set in order to:
improve information quality (reliability, robustness, accuracy, clarity, completeness) and enables automated algorithm based data analysis.
We present the project achievements, namely:
- Development of building scanner system for multisensory NDT
- Laboratory multi sensor investigations
- Development of data fusion concept for honeycombing and pitting corrosion
- Field testing