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The RILEM technical committee TC 247-DTA ‘Durability Testing of Alkali-Activated Materials’ conducted a round robin testing programme to determine the validity of various durability testing methods, originally developed for Portland cement based-concretes, for the assessment of the durability of alkali-activated concretes. The outcomes of the round robin tests evaluating sulfate resistance, alkali-silica reaction (ASR) and freeze–thaw resistance are presented in this contribution. Five different alkali-activated concretes, based on ground granulated blast furnace slag, fly ash, or metakaolin were investigated. The extent of sulfate damage to concretes based on slag or fly ash seems to be limited when exposed to an Na2SO4 solution. The mixture based on metakaolin showed an excessive, very early expansion, followed by a dimensionally stable period, which cannot be explained at present. In the slag-based concretes, MgSO4 caused more expansion and visual damage than Na2SO4; however, the expansion limits defined in the respective standards were not exceeded. Both the ASTM C1293 and RILEM AAR-3.1 test methods for the determination of ASR expansion appear to give essentially reliable identification of expansion caused by highly reactive aggregates. Alkali-activated materials in combination with an unreactive or potentially expansive aggregate were in no case seen to cause larger expansions; only the aggregates of known very high reactivity were seen to be problematic. The results of freeze–thaw testing (with/without deicing salts) of alkali-activated concretes suggest an important influence of the curing conditions and experimental conditions on the test outcomes, which need to be understood before the tests can be reliably applied and interpreted.
Bitumen is a crucial building material in road construction, which is exposed to continuously higher stresses due to higher traffic loads and changing climatic conditions. Therefore, various additives are increasingly being added to the bitumen complicating the characterisation of the bituminous binder, especially concerning the reuse of reclaimed asphalt.
Therefore, this work aimed to demonstrate that the combination of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflexion (ATR) technique and multivariate evaluation is a very wellsuited method to reliable identify and quantify additives in bituminous binders. For this purpose, various unmodified and modified binders, directly and extracted from laboratory and reclaimed asphalts, were investigated with FTIR-ATR spectroscopy. The determined spectra, pre-processed by standard normal variate (SNV) transformation and the determination of the 1st derivation, were evaluated using factor Analysis (FA), linear discriminant analysis (LDA) and partial least square regression (PLSR). With this multivariate evaluation, first, a significant model with a very high hit rate of over 90% was developed allowing for the identification of styrene-butadiene copolymers (SBC), ethylene-copolymer bitumen (ECB) and different waxes (e.g. amide and Fischer-Tropsch wax) even if the additives do not show any additional peaks or the samples are multi-modified. Second, a quantification of the content is possible for SBC, ECB, and Amide wax with a mean error of RMSE B 0.4 wt% and a
coefficient of determination of R2[90%. Based on these results, FTIR identification and quantification of additives in bituminous binders is a very promising method with a great 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.
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.
Composition and microstructure stability of cement compound under cyclic hydrothermal condition
(2020)
There have been many researches focused on the performance improvement of ultra-high performance concrete (UHPC) by autoclaving treatment. The goal of autoclaving is to increase the pozzolanic reaction, and to densify the cement stone and the transition zone which originates from the incorporation of supplementary cementitious materials (SCMs), such as silica fume, fly ash and blast furnace slag. Due to the superior properties, UHPC can also be utilized under high mechanical load and aggressive condition, for example, the fabrication of water tanks for thermal storage which is of great significance for saving energy and reducing CO2 emission. It is known that mineral stability of the hydration products of an inorganic binder is highly related to the temperature and pressure of the environment. A certain stable composition at room temperature, however, may undergo a phase transformation at high temperature and the performance decrease under this severe condition will generally be more severe. In this way, the rationale behind this deterioration under long-term hydrothermal condition involving many cycles and long duration has to be clarified, and then appropriate optimizing methods will be performed in order to obtain a kind of construction with high durability under aggressive environment. For this purpose, different types and amounts of SCMs are introduced into the standard mixture of UHPC and the phase compositions after autoclaving at 200 °C and 15.5 bar are determined by combined X-ray diffraction and scanning electron microscope. Mercury intrusion porosimeter is used to characterise the microstructure of the samples. In order to establish the relationship between microstructure and macroscopic properties, compressive and flexural strength are also investigated.
The paper descibes a novel method to detoxify pine wood (Pinus silvestris L.) treated with CCB-, CCF- CC-, and Cu-HDO-type salt preservatives. In the process of biological detoxification organic acids produced by strains of Antrodia vaillantii and other brown rot fungi are used for the dissolution of the previously fixed inorganic compounds. These findings are the basis for applying an acid pulping process (FORMACELL) developed by Nimz and Schone (1992) at the BFH, Hamburg, to detoxify salt impregnated wood waste with a mixture of acetic and formic acid. First results achieved with wood chips from treated poles after approximately 20 years of service life show that the obtained pulp contains less than 100 ppm of Cr and Cu. The pulp properties were neither influenced by the Cr and Cu ions nor by the age of the poles. The extracted quantities of Cr and Cu remain with the lignin whereas the acids are evaporated and recycled in the pulping process.
The device performance of polycrystalline chalcogenide thin film solar cells is strongly influenced by different kinds of defects within the material. The presence of sodium or other alkali metals like potassium during the deposition process is well known to influence the electronic properties of the solar cell and thus to improve the efficiency of the final device. Structural analysis of neutron powder diffraction data collected at low temperatures and subsequent profile analysis by the LeBail and Rietveld method demonstrates the impact of sodium on the point defect characteristics in off stoichiometric CuInSe2. The analyzed materials are powder and thin film solar absorber material with addition of NaF and free of sodium. It is illustrated, the so called “sodium effect” cannot be reduced to one single origin. A range of effects, the reduction of InCu donors with a followed increase of VCu acceptors is possible. The main effect is an increased ordered character of the chalcopyrite crystal structure at off stoichiometric composition, when containing sodium.
The bond between polymer fibers and the surrounding cementitious matrix is essential for the development of concrete reinforcement. The single fiber pull-out test (SFPT) is the standard characterization technique for testing the bond strength. However, the different phases of debonding cannot be distinguished by the SFPT. This study investigates the debonding of different polymer fibers from the surrounding cementitious matrix with a modified SFPT and proposes methods to change the SFPT setup to generate more valuable information on the debonding mechanism. The SFPT was equipped with linear variable differential transformers (LVDT), digital image correlation (DIC) and acoustic emission (AE) analysis. The results demonstrate that the modified SFPT allows a better understanding of the different phases of debonding during fiber pull-out. Furthermore, bond strength values calculated by different methods reveal that the chemical bond of the investigated polymers is not different as reported by previous studies. Deformation measurements performed using LVDTs and DIC are suitable measuring techniques to characterize the debonding mechanism in SFPT. A correlation between recorded AE and debonding phases was not found.