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Results for “CONCRETE”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A green ultra-lightweight chemically foamed concrete for building exterior: A feasibility study
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Copper entrapment and immobilization during cement hydration in concrete mixtures containing copper
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Environmental full cost accounting of alternative materials used for railroad ties: Treated-wood and concrete case study
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Life cycle assessment of ultra-tall wind turbine towers comparing concrete additive manufacturing to conventional manufacturing
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Application of Sorel cement containing magnesium carbonate as concrete fine aggregate for sequestering CO2: material and structural performance
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A predictive continuum framework for concrete subjected to high-velocity impact loading
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Properties of concrete with untreated and treated crumb rubber – A review
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A study on heat transfer performance of recycled aggregate thermal insulation concrete
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Onsite strength determination for early-opening decision making of high early strength concrete pavement
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Does biological self-healing concretes have lower environmental impacts than conventional crack repair solutions?
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Temporal Evaluation of ASR Cracking in Concrete Specimens Using Acoustic Emission
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Characterization of Ultrahigh-Performance Concrete Materials for Application in Modular Construction
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Effect of Carbon Nanofibers on the Hydration of Ultrahigh-Performance Concrete: Experimental Study and Model Development
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Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures
Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.
Analytical solutions of moisture-chloride ion coupled transport in unsaturated concrete
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