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Organisationseinheit der BAM
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Nuclear magnetic resonance (NMR) with focus on 1H protons is increasingly applied for non-destructive testing applications. Besides mobile NMR, laboratory devices such as the NMR core-analyzing tomograph are used. As their magnetic field is more homogeneous, they enable measurements with higher signal-to-noise ratios (SNR), but with limited sample sizes. The tomograph presented here (8.9 MHz) was constructed for a maximum sample diameter of 70 mm and length of up to 1 m. The resolution, the echo time (min. 50 µs), the SNR and the measurement type can be adjusted by means of exchangable coils. The tomograph enables measurements along the complete sensitive length, slice-selective and even 2- or 3-dimensional measurements. A movable sample lifting system thereby allows a precise positioning of the sample.
Vorstellung der Themen der Nachwuchsgruppe "Materialcharakterisierung und -informatik für die Nachhaltigkeit im Bauwesen" von Prof. Sabine Kruschwitz (TU Berlin und BAM)
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.
In this paper, we demonstrate the value of 1H NMR
relaxometry for studying the hydration of clinker-reduced, more
climate-friendly cementitious binders. The results were obtained
on typical CEM I cements and samples containing two different
reactive agricultural ashes as well as non-reactive biochars as
supplementary cementitious materials. The findings prove that
time-resolved NMR measurements provide valuable additional
information when combined with classical heat flow calorimetry.
Der Vortrag gibt eine Übersicht über aktuelle zerstörungsfreie Prüfmethoden, die verwendet werden können, um eine Zustandsbewertung von Bauteilen im Bestand vorzunehmen und mögliche Wiederverwendungsstrategien zu entwickeln. Ebenfalls zeigt der Vortrag, welche Möglichkeiten es gibt, künstliche Intelligenzmethoden zu verwenden, um aus recykelten Materialien wieder hochwertige, neue Baustoffe zu entwickeln.
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2-relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
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.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.