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Zahlreiche Schädigungsprozesse in Baustoffen stehen im engen Zusammenhang mit Feuchteeintrag und Feuchtetransport. Als Beispiel sind die schädigende Alkali-Kieselsäurereaktion (AKR) von Beton sowie Frost-Tauwechsel induzierte Gefügeschäden genannt. Zur zerstörungsfreien, ortsaufgelösten Feuchtemessung eignet sich die im Bereich der Geophysik etablierte, aber im Bauingenieurwesen noch wenig verbreitete, Messmethode der Nuklear Magnetischen Resonanz (NMR). Unter Verwendung der NMR-Relaxometrie sind sowohl Aussagen zum Feuchtegehalt und dessen räumliche Verteilung als auch die Charakterisierung der Porengrößen möglich, die das Transportverhalten eines porösen Materials maßgeblich beeinflussen.
Zur Erfassung der Mikro- und Mesoporen in Baustoffen ist dabei die Auflösung von kurzen T2 Relaxationszeiten unabdingbar. Bisher ist es mit gängigen NMR-Laborgeräten nur begrenzt möglich, solche kurzen T2-Zeiten schichtselektiv zu erfassen. Vor diesem Hintergrund wurde ein speziell für die Messung an mineralischen Baustoffen optimierter NMR-Tomograph beschafft. Dieser ermöglicht sowohl schichtselektive Messungen von Bohrkernen mit Durchmessern von bis zu 70 mm als auch bildgebende Untersuchungen an Proben mit Durchmessern ≤ 40 mm.
Erste Untersuchungsergebnisse an Sandstein, Tuffstein und Beton zeigen die Leistungsfähigkeit des neuen NMR-Tomographen zur Erfassung der porengrößenspezifischen Feuchteverteilung. In diesem Beitrag werden erste Ergebnisse verschiedenartiger Laborversuche exemplarisch dargestellt.
Für Sandsteine mit unterschiedlichsten Porengrößenverteilungen wurden mit dem neuen NMR-Tomographen und einem herkömmlichen NMR-System vergleichbare T2 Zeitenverteilungen ermittelt. Am sehr heterogenen Tuffstein konnten unterschiedlich poröse (und feuchte) Bereiche räumlich aufgelöst werden. Im Beton ließ sich der Feuchtetransport im Zementstein porengrößenspezifisch, schichtselektiv und zeitlich aufgelöst verfolgen.
Die bisher gewonnenen Ergebnisse zeigen das breite Anwendungsspektrum des neuen NMR Tomographen auf und ermöglichen ein besseres Verständnis des Feuchtetransports und der oft damit einhergehenden Schädigungsprozesse in Baustoffen.
In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.
We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.
The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.
Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.
One of the most common causes of damage is the ingress of harmful ions into the concrete, which can lead to deterioration processes and affect structural performance. Therefore, the increasingly aging infrastructure is regularly inspected to assess durability. Regular chemical analysis can be useful to determine the extent and evolution of ion ingress and to intervene in a timely manner. This could prove more economical than extensive repairs for major damage, particularly for critical infrastructure. In addition to already established elemental analysis techniques in civil engineering such as potentiometric titration or X-ray fluorescence analysis, laser-induced breakdown spectroscopy (LIBS) can provide further important complementary information and benefits. The possibilities of LIBS are demonstrated using the example of a drill core taken from a parking garage.
Laser Induced Breakdown Spectroscopy – A Tool for Imaging the Chemical Composition of Concrete
(2022)
One of the most common causes of damage is the ingress of harmful ions into the concrete, which can lead to deterioration processes and affect structural performance. Therefore, the increasingly aging infrastructure is regularly inspected to assess durability. Regular chemical analysis can be useful to determine the extent and evolution of ion ingress and to intervene in a timely manner. This could prove more economical than extensive repairs for major damage, particularly for critical infrastructure. In addition to already established elemental analysis techniques in civil engineering such as potentiometric titration or X-ray fluorescence analysis, laser-induced breakdown spectroscopy (LIBS) can provide further important complementary information and benefits. The possibilities of LIBS are demonstrated using the example of a drill core taken from a parking garage.
The knowledge of the thermal parameters of a particular concrete is essential for thermal design of a building, but also could help to identify and assess the state of a concrete structure. Active thermography has the potential to be applied onsite and to provide a fast investigation of thermal properties. In this work, three different concrete samples were investigated by active thermography in reflection and in transmission setup. It was found that this method yields the same results without direct contact as the Transient Plane Source (TPS) method as an established inspection tool.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
SLAMD-FIB-Case-Study
(2022)
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders part of the energy-intensive clinker production process can be dispensed with. However, because numerous chemicals are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced cement formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of cement formulations can significantly accelerate research. The „Sequential Learning App for Materials Discovery“ (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings. Our material database already includes more than 120,000 data points of alternative cements and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials. Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic method for detecting the chemical composition of optically accessible surfaces. In principle, the measurement of all elements of the periodic table is possible. System calibrations allow the quantification of element concentrations. In combination with scanner systems, the two-dimensional element distribution can be determined. Even rough surfaces can be measured by online adjustment of the laser focus. To detect element ingress into the concrete, typically cores are taken, cut in half, and LIBS measurements are performed on the cross-section. The high spatial resolution as well as the simultaneous multi-element analysis enables a separate evaluation of the binder-matrix and aggregates. Therefore, the element concentrations can be determined directly related to the cement paste. LIBS measurements are applicable in the laboratory, on-site and also over a distance of several meters.
Common applications include the investigation of material deterioration due to the ingress of harmful ions and their interaction in porous building materials. LIBS is able to provide precise input parameters for simulation and modelling of the remaining lifetime of a structure. Besides the identification of materials, also their composition can be determined on hardened concrete, such as the type of cement or type of aggregate. This also involves the identification of environmentally hazardous elements contained in concrete. Another possible application is the detection of the composition of material flows during dismantling. Non-contact NDT for “difficult to assess” structures as an example application through safety glass or in combination with robotics and automation are also possible.
This work presents the state of the art concerning LIBS investigations on concrete by showing exemplary laboratory and on-site applications.