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A large amount of data and information is collected in the field of non-destructive testing (NDT) in civil engineering. The weakly structured data are usually evaluated with regard to specific testing tasks (e.g. geometry determination, damage localization, quality assurance). While the data offers great economic potential, i.e. to support construction planning, monitoring and maintenance processes, the evaluation is manual and case-by-case and therefore too inefficient for broader applications. We present recent visions and approaches how these large amounts of data need to be handled in the future and how we aim to make the acquired knowledge accessible to our stakeholders. Building on initiatives in materials research, we stress the importance of further research in the field of semantic data integration particularly motivate why an ontology is needed for the area of NDT in civil engineering.
Zerstörungsfreie Feuchtemessung an Estrichen - ein multi-sensorischer Ansatz mit Methodenvergleich
(2015)
Feuchte und Salz können die Mikrostruktur poröser Baustoffe maßgeblich verändern, oft sind sie die Ursache von sowohl chemischen als auch mechanischen Schädigungen und Korrosion. Im speziellen Fall von Fußbodenestrichen ist gerade die Frage nach der Belegreife für Fußbodenleger von immer wiederkehrender Bedeutung. Dieser Beitrag beschäftigt sich mit den Vor- und Nachteilen verschiedener zerstörungsfreier, feuchtesensitiver Messmethoden und vergleicht die Ergebnisse mit denen der baustellenüblichen zerstörenden Tests. Kapazitive Methoden und Bodenradar scheinen am erfolgversprechensten im Bezug auf flächige Feuchtemessungen, aber auch sie eignen sich derzeit noch nicht als eigenständige, sofort ersetzbare Absolutmessgeräte, sondern eher als schnelle Relativverfahren fürs Monitoring.
In this ongoing research project, we study the influence of moisture damage on Ground Penetrating Radar (GPR) in different floor constructions. For this purpose, a measurement setup with interchangeable layers is developed to vary the screed material (cement or anhydrite) and insulation material (glass wool, perlite, expanded and extruded polystyrene), as well as the respective layer thickness. The evaluation of the 2 GHz common-offset radar measurements is focused on the extraction of distinctive signal features that can be used to classify the underlying case of damage without any further information about the hidden materials or layer thicknesses. In the collected dataset, we analyze the horizontal distribution of A-scan features in corresponding B-scans to detect water in the insulation layer. Furthermore, possible combinations of these features are investigated with the use of multivariate data analysis and machine learning (logistic regression) in order to evaluate the mutual dependencies. In this study, the combination of an amplitude- and frequency-based feature achieved an accuracy of 93.2 % and performed best to detect a damage in floor insulations.
The moisture content of the subfloor has to be determined before installation to avoid damages of the floor covering. Only if the readiness for layering is reached, an installation without damages can be expected in all cases. In general, three different approaches exist to measure the residual water content: determination of the moisture content, determination of the water release, or determination of the corresponding relative humidity. All three approaches are tested in laboratory at eight different screed types including two different samples thicknesses in each case. The moisture content and the water release are measured by sample weighing, the corresponding relative humidity is measured by embedded sensors. All three approaches are compared and correlated to each other. The evaluations show only weak correlation and, in several cases, contradicting results. Samples are considered as being ready for layering and not-being ready for layering at the same time, depending on the chosen approach. Due to these contradicting results, a general threshold for the risk of damage cannot be derived based on these measurements. Furthermore, the experiment demonstrates that the measurement of corresponding relative humidity is independent of the considered screed type or screed composition. This makes the humidity measurement to very promising approach for the installation of material moisture monitoring systems in the future.
Integration of fibre reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel fibre reinforced concrete (FRC) is the deceleration of crack growth and hence it’s improved sustainability due to e.g. decrease of permeability of concrete by aggressive substances. Additional benefits are associated with the structural properties of FRC, where fibres can significantly increase the ductility and the tensile strength of concrete. In some applications, such as tunnel linings or industrial slabs, it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits. Fibre reinforcement can, however, have critical disadvantages and even hinder the performance of concrete, since it can induce an anisotropic material behaviour of the mixture if the fibres are not appropriately oriented. For a safe use of FRC in the future, reliable non-destructive methods need to be identified to assess the fibres’ orientation in hardened concrete. In this study, ultrasonic material testing, electrical impedance testing, and X-ray computer tomography have been investigated for this purpose using specially produced samples with biased or random fibre orientations. This paper demonstrates the capabilities of each of these NDT techniques for fibre orientation measurements and draws conclusions based on these results about the most promising areas for future research and development using these techniques.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
