Filtern
Erscheinungsjahr
Dokumenttyp
- Zeitschriftenartikel (42)
- Vortrag (34)
- Beitrag zu einem Tagungsband (32)
- Posterpräsentation (15)
- Beitrag zu einem Sammelband (10)
- Dissertation (1)
- Video (1)
- Sonstiges (1)
- Forschungsdatensatz (1)
Schlagworte
- Moisture (15)
- Feuchte (13)
- Spectral induced polarization (13)
- Radar (12)
- Non-destructive testing (11)
- NMR (9)
- Concrete (8)
- LIBS (7)
- Nuclear magnetic resonance (7)
- Sandstone (7)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (55)
- 8.0 Abteilungsleitung und andere (50)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (18)
- 7 Bauwerkssicherheit (7)
- 7.1 Baustoffe (3)
- 7.4 Baustofftechnologie (3)
- 7.3 Brandingenieurwesen (2)
- 7.6 Korrosion und Korrosionsschutz (2)
- 8.5 Röntgenbildgebung (2)
- 6 Materialchemie (1)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (34)
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
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.
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.
Over the last 15 years significant advancements in induced polarization (IP) research have taken place, particularly with respect to spectral IP (SIP), concerning the understanding of the mechanisms of the IP phenomenon, the conduction of accurate and broadband laboratory measurements, the modelling and inversion of IP data for imaging purposes and the increasing application of the method in near-surface investigations. We summarize here the current state of the science of the SIP method for near-surface applications and describe which aspects still represent open issues and should be the focus of future research efforts. Significant progress has been made over the last decade in the understanding of the microscopic mechanisms of IP; however, integrated mechanistic models involving different possible polarization processes at the grain/pore scale are still lacking. A prerequisite for the advances in the mechanistic understanding of IP was the development of improved laboratory instrumentation, which has led to a continuously growing data base of SIP measurements on various soil and rock samples. We summarize the experience of numerous experimental studies by formulating key recommendations for reliable SIP laboratory measurements. To make use of the established theoretical and empirical relationships between SIP characteristics and target petrophysical properties at the field scale, sophisticated forward modelling and inversion algorithms are needed. Considerable progress has also been made in this field, in particular with the development of complex resistivity algorithms allowing the modelling and inversion of IP data in the frequency domain. The ultimate goal for the future are algorithms and codes for the integral inversion of 3D, time-lapse and multi-frequency IP data, which defines a 5D inversion problem involving the dimensions space (for imaging), time (for monitoring) and frequency (for spectroscopy). We also offer guidelines for reliable and accurate measurements of IP spectra, which are essential for improved understanding of IP mechanisms and their links to physical, chemical and biological properties of interest. We believe that the SIP method offers potential for subsurface structure and process characterization, in particular in hydrogeophysical and biogeophysical studies.
Since nuclear magnetic resonance with focus on 1H protons is highly sensitive to pore filling fluids, it is nowadays often applied for the investigation of porous media. Mainly in materials research and especially in the field of non-destructive testing in civil engineering it is increasingly used. Scientific questions about and based on NMR meanwhile cover a broad spectrum. To give an overview, we have reviewed various studies dealing with the determination of moisture contents and parameters such as the pore-size distribution, surface relaxivity, porosity, etc. In some papers, the monitoring of moisture transport in connection with degradation processes or admixtures was the main objective. In other papers, NMR was used for pore space analysis or even applied on site to assess the state of conservation of cultural heritage. Building materials that have been investigated in the presented studies are for example cement, concrete, woods, sandstones etc. In this paper, short descriptions and the significant results of the reviewed articles are summarized and their measurement problems and discrepancies are pointed out. A special feature of this review article is the concise tabular compilation of determined 𝑇1 and 𝑇2 relaxation times, as well as of surface relaxivity values for various materials and components. Finally, relevant aspects are summed up and conclusions about the increasing potential of NMR relaxometry for investigations of porous building materials are drawn, followed by an outlook about future applications and the need for technical development.
The determination of water and salt distribution in brickwork and stonework is a frequent problem in cultural heritage protection. Conventional but reliable methods are usually based on the investigation of core samples and provide only punctual information. To avoid the destruction of valuable building fabric, a non-destructive approach using a combination of investigation methods is needed. A case study, demonstrating the possibilities of almost non-destructive testing methods and their combination is presented in this article.