ArticleEnvironmental science and pollution research international2024
Deep learning-based prediction of compressive strength of eco-friendly geopolymer concrete.
Article in Environmental science and pollution research international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Two stage AI framework for strength prediction and generative LLM for geopolymer concrete.Scientific reports · 2026Article
- Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete.Scientific reports · 2025Article
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Authors and funding
1 author.
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Abstract
The greenhouse gases cause global warming on Earth. The cement production industry is one of the largest sectors producing greenhouse gases. The geopolymer is produced with synthesized by the reaction of an alkaline solution and the waste materials such as slag and fly ash. The use of eco-friendly geopolymer concrete decreases energy consumption and greenhouse gases. In this study, the f
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Registered trials
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