Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques
実験分析と機械学習技術を統合した低炭素ジオポリマーコンクリートのCO2排出データ駆動予測 (AI 翻訳)
Muhannad Alasiri
🤖 gxceed AI 要約
日本語
本論文は、フライアッシュと高炉スラグを用いた低炭素ジオポリマーコンクリートのCO2排出量を実験と機械学習（ANN、RF、XGBoost）で予測する手法を開発。XGBoostが最高精度（R²>0.95）を示し、従来のOPCコンクリート比で45-65%の排出削減可能。建設分野の脱炭素設計支援に貢献。
English
This paper develops an experimental and machine learning framework to predict CO2 emissions of low-carbon geopolymer concrete using fly ash and GGBS. XGBoost achieves best accuracy (R²>0.95), showing 45-65% emission reduction vs. OPC concrete. Offers a decision-support tool for sustainable construction.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では建設分野のCO2排出削減が急務。本手法はSSBJや有報でのGHG排出量算定（特にScope3）に活用可能で、低炭素建材の採用判断をデータ駆動で支援する。
In the global GX context
Globally, construction accounts for ~40% of CO2 emissions. This ML-driven prediction framework supports low-carbon concrete design, aligning with ISSB and SEC climate disclosure requirements for embodied carbon reporting.
👥 読者別の含意
🔬研究者:Provides a validated ML pipeline for predicting CO2 emissions in geopolymer concrete, enabling further optimization of binder compositions.
🏢実務担当者:Use XGBoost model to approximate emissions of geopolymer mixes, aiding low-carbon material selection without full LCA.
🏛政策担当者:Supports development of benchmarks for low-carbon concrete in building codes and green procurement standards.
📄 Abstract（原文）
The construction sector is a huge contributor to carbon emissions in the world, mostly because of the heavy applications of Ordinary Portland Cement (OPC). Geopolymer concrete with low carbon content has become a viable alternative to concrete; nevertheless, accurate prediction and evaluation of its CO2 emissions have not been fully achieved, especially through combined experimental and data analysis. The literature on this study focuses mainly on mechanical performance, and there is limited understanding of environmental emission modeling. To measure and forecast CO2 emissions from low-carbon geopolymer concrete, this paper develops an experimental and machine-learning system. An array of geopolymer blends comprising fly ash and ground granulated blast furnace slag (GGBS) was developed at different binder proportions, alkaline activator ratios, and curing conditions. The amount of CO2 was determined using a cradle-to-gate evaluation method (kg CO2/m3). Data were subsequently trained on and tested on machine learning models, such as Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) using experimental data. The R², RMSE, and MAE measures were used to assess model performance. Findings show that the XGBoost model has the best prediction accuracy (R² > 0.95), indicating good generalization ability. The highest CO2 emission reduction was 45–65 percent for geopolymer mixtures compared to traditional OPC concrete. The construct can offer a trustworthy decision support system in geopolymer concrete design that is environmentally friendly and further sustainability in construction methods with predictive modeling of emissions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54740/ros.2026.024first seen 2026-06-11 05:24:56
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