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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.
Attenuation of ultrasonic signals in concrete has the potential to carry much information about the microstructure of the material. In this work a series of concrete specimens of varying porosities and pore size distributions were internally imaged with x-ray computed tomography (CT), and then subsequently examined with throughtransmission ultrasound. The CT images were used to quantify both capillary porosity of cement paste as well as internal interfaces that are likely to produce elastic wave scattering. Ultrasound signals were represented as a diffusion process such that absorption and scattering attenuation could be isolated. As implemented, the diffusion model was a poor predictor of capillary porosity, while diffusivity was a reasonable predictor of scattering interfaces. Diffusivity was found to scale extremely well with small scale porosity, which made it a good predictor of compressive strength.
The propagation of ultrasonic waves in concrete is affected by its micro- and macro-structure, geometry and properties as well as external influences as stress, temperature or moisture. In addition, age and degradation have a strong influence. Therefore, Ultrasound has been used to monitor concrete samples and structures since decades. However, early applications using conventional techniques as time-of flight or changes in amplitudes have been limited to detect changes in a late stage close to serviceability or ultimate load states.
Around 2000, several new, more sensitive techniques adopted from geophysics or other field of material sciences have been introduced to research in ultrasonic monitoring of concrete. The most discussed methodologies are coda wave interferometry, a technique which allows to detect very subtle changes from repeated ultrasonic measurements. Nonlinear acoustic techniques help to identify e. g. cracks even in an inhomogeneous background. Both techniques can be combined.
This paper reviews methods and results achieved so far on the laboratory scale and with full scale models the directions for future research and application is given as well.
The propagation of ultrasonic waves in concrete is affected by its micro- and macro-structure, geometry and properties as well as external influences as stress, temperature or moisture. In addition, age and degradation have a strong influence. Therefore, Ultrasound has been used to monitor concrete samples and structures since decades. However, early applications using conventional techniques as time-of flight or changes in amplitudes have been limited to detect changes in a late stage close to serviceability or ultimate load states.
Around 2000, several new, more sensitive techniques adopted from geophysics or other field of material sciences have been introduced to research in ultrasonic monitoring of concrete. The most discussed methodologies are coda wave interferometry, a technique which allows to detect very subtle changes from repeated ultrasonic measurements. Nonlinear acoustic techniques help to identify e. g. cracks even in an inhomogeneous background. Both techniques can be combined.
This paper reviews methods and results achieved so far on the laboratory scale and with full scale models the directions for future research and application is given as well.
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.
Acoustic emission and ultrasonic monitoring of a prestressed concrete bridge in its final years
(2021)
Many European bridges built in the 1950s, 60s and 70s must be re-placed in the next decade due to structural deficiencies, insufficient load capacity or other issues. However, the existing bridges must be used for another couple of years even if flaws and damages already have been detected.
In Germany, several prestressed concrete bridges have been instrumented with acoustic emission detection systems to detect wire breaks and to provide early warning signs before failure. To evaluate and interpret the consequences of wire breaks additional instrumentation and accompanying measures as finite element modeling are required.
At a bridge in southern Germany we have complemented such a system with active ultrasonic monitoring. Repeated ultrasonic measurements are evaluated with a very sensible algorithm called coda wave interferometry. This method, inspired by seismology, has been shown to deliver early warning signs in lab experiments. Large volumes of concrete can be monitored with a limited network of ultrasonic transducers. We will report on the installation, capabilities, and lim-itations as well as first results.
Fatigue is one of the most prevalent issues, which directly influences the service life expectancy of concrete structures. Fatigue has been investigated for years for steel structures. However, recent findings suggest that concrete structures may also be significantly subjected to the fatigue phenomena that could lead to premature failure of certainfatigue prone structural elements. To date, fatigue of reinforced concrete has been given little focus. and Kknowledge on the influence factors and durability/capacity effects on this material should be improved. Current technological means to measure fatigue in civil structures like bridges and wind turbines (both onshore and offshore) are outdated, imprecise and inappropriate.
Meanwhile, this topic has got much more attention as dynamic loading on concrete structures plays an increasing role, e.g. in bridges with increasing traffic and heavier trucks, in wind energy production due to inherent vibrations e.g. in offshore wind turbine support structures affected by wind and waves.
The European Innovative Training Networks (ITN) Marie Skłodowska-Curie Actions project INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures - Training for Assessment of Risk) provides research training for 12 PhD students. The project aims to improve knowledge for optimizing the design of new structures as well as for more realistic verification of structural safety and more accurate prediction of the remaining fatigue lifetime of existing structures.
First, the INFRASTAR research framework is detailed. Then it will be exemplified through the presentation of the major results of the four PhD students involved in the work package dealing with auscultation and monitoring. This includes the development and improvement of Fiber Optics (FO) and Coda Wave Interferometry (CWI) for crack sizing and imagery, new sensor technologies and integration, information management, monitoring strategy for fatigue damage investigation and lifetime prediction.
Fatigue is one of the most prevalent issues, which directly influences the service life expectancy of concrete structures. Fatigue has been investigated for years for steel structures. However, recent findings suggest that concrete structures may also be significantly subjected to fatigue phenomena that could lead to premature failure of certain structural elements. To date, fatigue of reinforced concrete has been given little focus. Knowledge on the influence factors and durability/capacity effects on this material should be improved. Current technological means to measure fatigue in civil structures like bridges and wind turbines (both onshore and offshore) are outdated, imprecise and inappropriate.
Meanwhile, this topic has got much more attention as time-variant loading on concrete structures plays an increasing role, e.g. in bridges with increasing traffic and heavier trucks, and for wind turbines for renewable energy production, e.g. for offshore wind turbine support structures affected by wind and waves.
The European Innovative Training Networks (ITN) Marie Skłodowska-Curie Actions project INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures - Training for Assessment of Risk) provides research training for 12 PhD students. The project aims to improve knowledge for optimizing the design of new structures as well as for more realistic verification of structural safety and more accurate prediction of the remaining fatigue lifetime of existing concrete structures.
First, the INFRASTAR research framework is detailed. Then it will be exemplified through the presentation of the major results of the four PhD students involved in the work package dealing with auscultation and monitoring. This includes the development and improvement of Fiber Optics (FO) and Coda Wave Interferometry (CWI) for crack sizing and imagery, new sensor technologies and integration, information management, monitoring strategy for fatigue damage investigation and lifetime prediction.
Advanced ultrasonic imaging for concrete: Alternative imaging conditions for reverse time migration
(2018)
Ultrasound echo is a widely used NDT technique for determining the internal geometry of structures. Reverse-time migration (RTM) has been recently introduced to NDT applications, as an imaging method for ultrasound data, to overcome some of the limitations (e.g. imaging steeply dipping reflector) experienced by the Synthetic Aperture Focusing Technique (SAFT), the most commonly used imaging algorithm for these measurements.
The standard implementation of RTM also experiences some drawbacks caused by its imaging condition, which is based on the zero-lag of the cross-correlation between source and receiver wavefields and generates high-amplitude low-frequency artifacts. Three alternative imaging conditions, developed for seismic data applications, were tested for their ability to provide better images than the standard cross-correlation: illumination compensation, deconvolution and wavefield decomposition. A polyamide specimen was chosen for the simulation of a synthetic experiment and for real data acquisition. The migrations of both synthetic and real data were performed with the software Madagascar. The illumination imaging condition was able to reduce the low-frequency noise and had a good performance in terms of computing time. The deconvolution improved the resolution in the synthetic tests, but did not showed such benefit for the real experiments. Finally, as for the wavefield decomposition, although it presented some advantages in terms of attenuating the low-frequency noise and some unwanted reflections, it was not able to image the internal structure of the polyamide as well as the cross-correlation did. Suggestions on how to improve the cost-effectiveness of the implementation of the deconvolution and wavefield decomposition were presented, as well as possible investigations that could be carried out in the future, in order to obtain better results with those two imaging conditions.