8 Zerstörungsfreie Prüfung
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Four cement-based and four calcium-sulphate-based screed types are investigated. The samples have a diameter of 300 mm and a height of 35 or 70 mm. Up to ten humidity sensors are embedded directly during the concreting of the screed samples. Thus, the humidity over the sample height is monitored during hardening, hydration, evaporation, and oven drying. Furthermore, the screed samples are weighed during every measurement to determine the total mass and the corresponding moisture loss.
To define the pore system precisely, mercury intrusion porosimetry as well as gas adsorption is performed. According to the data, the entire pore volume distribution is known. The measured pore diameters range from 0.8 nm to 100 µm and the total porosity of the examined screeds ranges between 11% and 22%.
Based on these measurement data, moisture transport, pore saturation as well as sorption isotherms and their hysteresis may be calculated quantitatively as described in “Monitoring of the absolute water content in porous materials based on embedded humidity sensors” (Strangfeld and Kruschwitz, 1921).
Half-Cell-Potential Mapping (HP) is the most popular non-destructive testing (NDT) method for the detection of active corrosion in reinforced concrete. HP is influenced by parameters such as moisture and chloride gradients in the component. The sensitivity to the spatially small, but dangerous pitting is low. In this study we show how additional measurement information can be used with multi-sensor data fusion to improve the detection performance and to automate data evaluation. The fusion is based on supervised machine learning (SML). SML are methods that recognize relationships in (sensor) data based on given labels. We use SML to distinguish "defective" and "intact" labeled areas in our dataset. It consists of 18 measurement - each contains HP, ground radar, microwave moisture and Wenner resistance data. Exact labels for changing environmental conditions were available in a laboratory study on a reinforced concrete slab, which deteriorated controlled and accelerated. The deterioration progress was monitored continuously and corrosion was generated targeted at a predefined location. The detection results are quantified and statistically evaluated. The SML results shows a significant improvement over the best single method (HP).
Monitoring of the absolute water content in porous materials based on embedded humidity sensors
(2018)
Moisture transport monitoring may indicate the onset of deterioration in porous building materials Prior to damage occurring. Most moisture measurement systems provide only qualitative values, require extensive calibration, or are destructive. Thus, non-destructive and calibration-free monitoring Systems are required. Our approach of moisture monitoring is to embed sensors that measure the relative humidity.
In our experiment, screed samples are monitored during the Hydration and evaporation process. Every test sample is equipped with 10 embedded sensors which measure the relative humidity across the sample thickness. Based on Hillerborg’s approach, the relative humidity is converted into the corresponding pore saturation. In our study, the free water is computed without knowledge of the Sorption isotherm. The free water in the pore system is predicted and validated. The predicted weight decrease corresponds conclusively to gravimetrically measured weights. The embedded sensors yield the absolute liquid water content and enable an experimental, non-destructive monitoring of liquid water in porous materials.
Four cement-based and four calcium-sulphate-based screed types are investigated. The samples have a diameter of 300 mm and a height of 35 or 70 mm. Up to ten humidity sensors are embedded directly during the concreting of the screed samples. Thus, the humidity over the sample height is monitored during hardening, hydration, evaporation, and oven drying. Furthermore, the screed samples are weighted during every measurement to determine the total mass and the corresponding moisture loss.
To define the pore system precisely, mercury intrusion porosimetry as well as gas adsorption is performed. According to the data, the entire pore volume distribution is known. The measured pore diameters range from 0.8 nm to 100 μm and the total porosity of the examined screeds ranges between 11 % and 22 %.
Based on these measurement data, moisture transport, pore saturation as well as sorption isotherms and their hysteresis may be calculated quantitatively as described by Strangfeld et al.
Potentialfeldmessung (PM) ist die beliebteste Methode der Zerstörungsfreien Prüfung (ZfP) zur Lokalisierung von aktiver Betonstahlkorrosion. PM wird durch Parameter wie z. B. Feuchtigkeits- und Chloridgradienten im Bauteil beeinflusst, so dass die Sensitivität gegenüber der räumlich sehr begrenzten, aber gefährlichen Lochkorrosion gering ist. Wir zeigen in dieser Studie, wie zusätzliche Messinformationen mit Multisensor-Datenfusion genutzt werden können, um die Detektionsleistung zu verbessern und die Auswertung zu automatisieren. Die Fusion basiert auf überwachtem maschinellen Lernen (ÜML). ÜML sind Methoden, die Zusammenhänge in (Sensor-) Daten anhand vorgegebener Kennzeichnungen (Label) erkennen. Wir verwenden ÜML um „defekt“ und „intakt“ gelabelte Bereiche in einem Multisensordatensatz zu unterscheiden. Unser Datensatz besteht aus 18 Messkampagnen und enthält jeweils PM-, Bodenradar-, Mikrowellen-Feuchte- und Wenner-Widerstandsdaten. Exakte Label für veränderliche Umweltbedingungen wurden in einer Versuchsanordnung bestimmt, bei der eine Stahlbetonplatte im Labor kontrolliert und beschleunigt verwittert. Der Verwitterungsfortschritt wurde kontinuierlich überwacht und die Korrosion gezielt erzeugt. Die Detektionsergebnisse werden quantifiziert und statistisch ausgewertet. Die Datenfusion zeigt gegenüber dem besten Einzelverfahren (PM) eine deutliche Verbesserung. Wir beschreiben die Herausforderungen datengesteuerter Ansätze in der zerstörungsfreien Prüfung und zeigen mögliche Lösungsansätze.