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Das maschinelle Lernen (ML) wurde erfolgreich zur Lösung vieler Aufgaben in der zerstörungsfreien Prüfung im Bauwesen (ZfPBau) eingesetzt. Allerdings ist die Erstellung von Referenzdaten in den meisten Fällen extrem teuer und daher viel knapper als in anderen Forschungsbereichen. Auch decken die verfügbaren Daten mitunter nur ein einziges Szenario ab, so dass die Leistungsindikatoren oft nicht die tatsächliche Leistung des ML-Modells in der praktischen Anwendung widerspiegeln. Schätzungen, die die Übertragbarkeit von einem Szenario auf ein anderes quantifizieren, sind erforderlich, um dieser Herausforderung gerecht zu werden und den Weg für Anwendungen in der Praxis zu ebnen.
In diesem Beitrag stellen wir Werkzeuge zur Beschreibung der Unsicherheit von ML in neuen ZfPBau-Szenarien vor. Zu diesem Zweck haben wir einen bestehenden Trainingsdatensatz zur Klassifizierung von Korrosionsschäden der Bewehrung in Beton um eine neue Fallstudie erweitert. Die Messungen wurden an großflächigen Betonproben mit eingebauter chloridinduzierter Korrosion des Bewehrungsstahls durchgeführt. Das Experiment simulierte den gesamten Lebenszyklus von chloridinduzierten Sichtbetonbauteilen im Labor. Unser Datensatz umfasst Potenzialfeld- und Radarmessungen. Die einzigartige Fähigkeit, die Schädigung zu überwachen und eine gezielte Korrosion einzuleiten, ermöglichte es, die Daten zu labeln - was für die Konstruktion von ML-Modellen entscheidend ist. Um die Übertragbarkeit zu untersuchen, erweitern wir unser Modell um Metadaten - wie etwa Konstruktionsmerkmale des Prüfkörpers und Umweltbedingungen. Dies erlaubt es, die Veränderung dieser Merkmale in neuen Szenarien mit statistischen Methoden als Unsicherheiten auszudrücken. Wir vergleichen verschiedene auf Stichproben und statistischer Verteilung basierende Ansätze und zeigen, wie diese Methoden eingesetzt werden können, um Wissenslücken von ML-Modellen in der ZfP zu schließen
ML has been successfully applied to solve many NDT-CE tasks. This is usually demonstrated with performance metrics that evaluate the model as a whole based on a given set of data. However, since in most cases the creation of reference data is extremely expensive, the data used is generally much sparser than in other areas, such as e-commerce. As a result, performance indicators often do not reflect the practical applicability of the ML model. Estimates that quantify transferability from one case to another are necessary to meet this challenge and pave the way for real world applications.
In this contribution we invetigate the uncertainty of ML in new NDT-CE scenarios. For this purpose, we have extended an existing training data set for the classification of corrosion damage by a new case study. Our data set includes half-cell potential mapping and ground-penetrating radar measurements. The measurements were performed on large-area concrete samples with built-in chloride-induced corrosion of reinforcement. The experiment simulated the entire life cycle of chloride induced exposed concrete components in the laboratory. The unique ability to monitor deterioration and initiate targeted corrosion initiation allowed the data to be labelled - which is crucial to ML. To investigate transferability, we extend our data by including new design features of the test specimen and environmental conditions. This allows to express the change of these features in new scenarios as uncertainties using statistical methods. We compare different sampling and statistical distribution-based approaches and show how these methods can be used to close knowledge gaps of ML models in NDT.
Environmentally friendly alternatives to cement are created through the synthesis of numerous base materials. The variation of their proportions alone leads to millions of materials candidates. Identifying suitable materials is very laborious; traditional systematic research in the laboratory consumes a lot of time and effort.
Sequential learning (SL) potentially speeds up the materials research process despite limited but highly complex available information. SL does not make direct predictions of material properties but ranks possible experiments according to their utility. The most promising experiments are prioritized over dead-end experiments and experiments whose outcome is already known.
Our work has shown that SL seems to be promising for cement research. So far, research has mainly focused on materials whose synthesis is faster and whose material properties require less time for development or characterization (allowing many successive experiments). Contrarily, in the case of binders, SL is only useful if few experiments lead to the desired goal, as for example, the determination of the compressive strength alone typically requires 28 days.
In research practice, experimental designs and the availability of resources often determine which data can be used - for example, when some laboratory resources are not available or deemed irrelevant to a task. As a result, new research scenarios are constantly emerging, each of which requires to demonstrate SL’s performance.
We are presenting the SLAMD app to facilitate the exploration of SL methods in numerous research scenarios. The app provides flexible and low-threshold access to AI methods via intuitive and interactive user interfaces. We deliberately pursue a software-based research approach (as opposed to code-, or script-based). On the one hand, the results are more comprehensible since we refer to a common (code) basis (’reproducible science’). On the other hand, the methods are easily accessible to all which accelerates the knowledge transfer into laboratory practice.
An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering
(2021)
Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn.
This paper studies the dispersion of GPR wave’s phase velocity at different wideband frequencies in plywood and concrete with varying moisture content. This study makes use of two GPR antennas with 2 GHz centre frequency operating in wide angle reflection and refraction (WARR) mode and with computation of spectral analysis of the surface wave (SASW). Computation of phase velocities is based on the Acquisition of the cross-power spectrum and phase unwrap of two distorted ground waves at positions Closer to and farther away from the transmitting antenna. The velocities of the ground waves are found to experience greater dispersion in low frequency regimes within the effective frequency bandwidths determined and thresholded by time–frequency analysis (TFA) and coherence plotting of the ground waves.
This study validates not only the methodology, but also identifies the optimal distance between the first (Rx1) and second (Rx2) receivers as k/2, which is based on a fixed transmitter (Tx) minus the first Receiver (Rx1) distance. It serves as an indication of changeable separation distance when other lower frequency GPR is used because the distances of Tx-Rx1 and Rx1-Rx2 are wavelength dependent and thus also frequency dependent. Effects of moisture contents and chloride contamination in concrete were also characterized according to the dispersion plots . that wave traelling in lower frequencies in GPR wave is much decelerated than that in higher frequencies. This research also contributes to the building of the ‘‘GPRWARR machine” suggested in Annan and Jackson (2017) [1], within which the effects of wave Dispersion on phase velocity can be inversely modelled to characterize variations in the material properties of infrastructure as a means of detecting surface damage.
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.
In our current research project „Reincarnate“ we aim to anchor the idea of the circular economy in the European construction industry and significantly extend the life cycle of buildings, construction products and materials through innovative solutions. On the long term, this is an approach reduce construction waste by 80 percent and the CO2 footprint of the construction sector by 70 percent."
This project has received funding from the European Union’s Horizon Europe research and innovation programme and will take you on a tour on what are the drivers, what is the goal, who are the partners and how we want to make the world a better place!
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.