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This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material's pore space. Specific NMR features are then integrated into the material design model, with the model's performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating that NMR-based, low-fidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.
Oxidative aging induces significant stiffening of asphalt binders that leads to a pronounced reduction in the overall durability of asphalt pavements. The strategic implementation of antioxidant additives provides a potential solution to alleviate this issue. This work presents results from the second phase of the global consortium for antioxidants research aimed at investigating the effectiveness of potential antioxidants in increasing the durability of asphalt binders. Sixteen laboratories around the world participated in this effort and a total of 28 binders from diverse geographical regions were tested. Two promising antioxidants, namely zinc diethyldithiocarbamate (ZDC) and kraft lignin were evaluated in this phase and blended with the binders at specific proportions. Subsequently, a comprehensive investigation was conducted to assess rheological characteristics and chemical properties of the various blends, utilising Dynamic Shear Rheometer (DSR) measurements and Fourier Transform Infrared (FTIR) Spectroscopy. The findings indicate that additives such as ZDC hold considerable promise as an effective antioxidant, particularly when considering a wide diversity of binders. In general, its incorporation does not compromise the rutting performance of the binders and significantly improves fatigue performance. Therefore, research efforts should be focused on exploring additional facets to assess its practical applicability in field.
Ground Granulated Blast-Furnace Slag (GGBFS), a by-product of the iron-making process, has gained significant attention as a supplementary cementitious material and has become increasingly popular in recent years due to its remarkable properties. GGBFS can significantly reduce the environmental impact of cement production when it comes to building concrete structures. GGBFS can either be blended with ordinary Portland cement (OPC) (up to a 90% replacement), or it can be used in the production of alkali-activated materials (AAMs). However, a comprehensive understanding of the pore solution composition is necessary for understanding various aspects of cementitious materials and their durability, including corrosion behavior, passivation of steel, and resistance to deteriorative processes. In the present work, the pore solutions of seven different GGBFS-containing cements (alkali-activated slag, alkali-activated slag/fly ash blends, a hybrid alkaline cement, CEM III/C, and CEM III/B) were extracted and analysed by inductively coupled plasma-optical emission spectroscopy, ion chromatography, pH, redox potential, and conductivity measurements. For comparison, a Portland cement pore solution was analysed similarly. The Concentrations of reduced sulfur were noteworthy in all GGBFS-containing cements, particularly in alkali-activated cements, where concentrations were notably higher compared to standard cements. The redox potentials of the pore solutions were primarily dictated by the concentrations of reduced sulfur, although other factors may contribute. Additionally, sulfur species in the pore solutions had an impact on pH, electrical conductivity, and other properties pertinent to the corrosion of reinforcements.
The corrosion behavior of galvanized steels and zinc components under atmospheric exposure depends mostly on the corrosion product‐based cover layer formation under the prevailing conditions. The use of agar‐based gel electrolytes makes it possible to use electrochemical methods to obtain a characteristic value from these cover layers that describe their current and future protective capacity. It is shown here that different states of galvanized steel can be distinguished very well under laboratory conditions and that this method is also suitable for use under practical conditions. Based on the characteristic values and assuming future time of wetness, it is very easy to draw up a forecast for the future corrosion rate, which provides plausible values.
While alkali-activated binders offer certain advantages over traditional Portland cement binders, particu¬larly in terms of resistance against chemical attacks and potentially environmental footprint, their degra¬dation mechanisms remain incompletely understood at present, specifically carbonation. Thus, this study investigates the impact of natural and accelerated carbonation (1% and 4% CO2) on three different compositions of alkali-activated concretes: 100% calcined clay (C100) binder, 100% ground blast furnace slag (S100) binder, and a 40% calcined clay and 60% blast furnace slag blend (C40S60). C100 concretes exhibit faster carbonation kinetics, with five times greater natural carbonation depths than S100. This difference diminishes under accelerated carbonation, showing a 1.5 times difference for 1% CO2 and complete carbonation for 4% CO2 at 90 days. The results thus confirm that accelerated carbonation testing of low-Ca alkali-activated concretes yields results that are not representative of natural carbonation. The C40S60 sample demonstrates a carbonation profile similar to a CEM I concrete, i.e., a high carbonation resistance. Microstructure analysis indicates the formation of three polymorphs of calcium carbonate for S100 (calcite, vaterite, and aragonite), with a higher CO2 concentration favouring aragonite over vaterite. C100 exhibits no calcium carbonates; instead, sodium carbonates form, including trona for 4% CO2 and natrite for both accelerated tests. C40S60 shows calcium and sodium carbonates, although to a lesser extent, containing predominantly calcite and minor signals of natrite. Suitable compositions, like C40S60, prove as effective during natural or accelerated carbonation tests as conventional Portland cement concrete.
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust. In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSswere deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https:// this http URL), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements.
Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction
(2024)
In this paper, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed bysolving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling
Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model’s performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non-separation yields the best approximation to the full order model.
The output of C&D waste is increasing year by year, among which low-quality recycled brick aggregates (RBAs) have not been well utilized and brought environmental burden. The durability of steam-cured concrete (HCC) is often compromised due to the detrimental effects of this curing regime on the long-term performance and microstructure development of the concrete material. To address this issue, this study investigates the potential of incorporating RBAs to improve the long-term durability of HCC. The results demonstrate that the incorporation of a small amount of RBA (10–20%) not only enhances the 28-d strength of HCC by 2.5–11.3%, but also improves its impermeability by mitigating heat damage effects. The combined application of fine and coarse RBA was found to effectively balance the negative effects of coarse RBA on the performance of HCC. Furthermore, the utilization of RBA in HCC was shown to have economic and environmental benefits. The results of this study demonstrate a simple and effective approach to improve the long-term durability of HCC while promoting the high-value utilization of solid waste.