Green Concrete Autonomous Designer: Single and Multiobjective Optimization of Mechanical Performance, Economic Viability, and Carbon Footprint
グリーンコンクリート自律設計：機械的性能、経済性、カーボンフットプリントの単目的・多目的最適化 (AI 翻訳)
Mohammad Khaled al-Bashiti, M.Z. Naser
🤖 gxceed AI 要約
日本語
機械学習（ベイズ最適化、NSGA-II/III）を用いて、コンクリート配合の最適化を自律的に行う手法を開発。2,300以上の実配合データを分析し、従来設計よりCO2を約40％、材料費を約30％削減可能であることを実証。さらに、コード不要のWebインターフェースを提供し、実務での大規模適用を可能とした。
English
This paper presents an autonomous ML approach using Bayesian optimization and NSGA-II/III to optimize green concrete mixtures. Analyzing over 2,300 real mixes, it achieves an average 40% reduction in CO2 emissions and 30% reduction in material cost while maintaining strength. A web interface enables practitioners to use the tool without coding.
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脱炭素が急務であり、コンクリートのCO2排出量削減は重要課題。本手法は、SSBJや有報での環境負荷開示が進む中、日本の生コン・建材メーカーが低炭素配合を効率的に開発するための実用的な手段を提供する。
In the global GX context
This work directly addresses the global need to reduce embodied carbon in construction materials. The ML-based optimization framework offers a scalable solution for concrete producers to meet ISSB/CSRD disclosure requirements and achieve decarbonization targets in the built environment.
👥 読者別の含意
🔬研究者:Demonstrates ML optimization for multi-objective trade-offs in sustainable materials; useful for extending similar approaches to other construction materials.
🏢実務担当者:Provides a web-based tool for concrete mix optimization that reduces both carbon footprint and material cost without sacrificing strength.
🏛政策担当者:Offers evidence that ML-driven optimization can significantly cut construction sector emissions, supporting policies for low-carbon concrete standards and procurement.
📄 Abstract（原文）
Abstract This paper presents an autonomous machine learning (ML) approach to optimizing green concrete mixtures by balancing mechanical performance, economic viability, and carbon footprint through single and multiobjective optimization. This approach builds upon a comprehensive database of physical tests that were analyzed via Bayesian optimization and non-dominated sorting genetic algorithm (NSGA)-II and NSGA-III algorithms. Then, the effectiveness of this approach was examined through several case studies across normal-strength, high-strength, and ultrahigh-performance concrete. Based on an analysis of over 2,300 real concrete mixes, our findings indicate that traditionally designed mixtures tend to have higher <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>CO</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> emissions and monetary costs, which suggests substantial inefficiencies in traditionally adopted baseline mixes. On a more positive note, the developed approach successfully overcomes such inefficiencies by attaining an average reduction of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>CO</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> and material cost that can be approximated at 40% and 30%, respectively (while maintaining similar strength performance). Finally, the proposed approach was packaged into a web interface to allow engineers and concrete fabricators with little-to-no coding experience to arrive at optimal mix designs. The study highlights the potential for ML-based optimization not only as a theoretical tool to create green concrete but also as a scalable tool for practical (mass-scale) applications.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1061/jmcee7.mteng-23218first seen 2026-07-26 05:33:15
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