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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.
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.
Vorstellung der Themen der Nachwuchsgruppe "Materialcharakterisierung und -informatik für die Nachhaltigkeit im Bauwesen" von Prof. Sabine Kruschwitz (TU Berlin und BAM)
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.
This contribution summarizes actual developments and draft fundamental teaching topics in the field of nondestructive testing in civil engineering (NDT-CE). It is based on the first memorandum on teaching and research in the field of NDT-CE at German speaking universities and provides an overview of the academic education and highlights possible focuses, especially in teaching but also takes into account noteworthy developments and topics in research in the field of NDT-CE.
Suggestions are given for the development and advancement of the teaching curricula in regards to a comprehensive and sound professional education of students in civil engineering and adjacent disciplines.
Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T1) and transverse (T2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T1 relaxation data, unbiased SiS NMR results for T2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below 0.05s. This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content >0.2s is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the µm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks.
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.
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.
Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. The growing diversity of construction and demolition waste is leading to increasing difficulties in separating the individual materials. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions.
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) and visual (VIS)/ near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic/technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.
First LIBS measurements show promising results in distinguishing various material types. A meaningful validation shall be achieved with further practical samples. Future works will investigate the combination of LIBS and VIS/NIR spectroscopy in a fully automated measurement setup with conveyor belt speeds of 3 m/s.