AI-Enabled System-of-Systems Decision Support: BIM-Integrated AI-LCA for Resilient and Sustainable Fiber-Reinforced Façade Design
AI対応システム・オブ・システムズ意思決定支援：持続可能で強靭な繊維強化外壁設計のためのBIM統合AI-LCA (AI 翻訳)
M. Al-Jamal, Ayooub Alsarhan, Wafa' Q. Al-Jamal, M. Aljamal, B. Khassawneh, A. Nuaim, Abdullah Al Nuaim
🤖 gxceed AI 要約
日本語
本研究は、BIMとAI強化LCAを統合したデジタルツイン対応意思決定フレームワークを提案。機械学習サロゲートモデル（ランダムフォレスト、勾配ブースティング、ANN）を用いて繊維強化コンクリート外壁の性能とライフサイクル指標（CO2排出量、体積エネルギー、水使用量）を高精度（最大99.2%）で予測。最適化により体積炭素の削減とエネルギー効率の向上を同時達成する。
English
This study presents a digital-twin-ready decision-support framework integrating BIM and AI-enhanced LCA. Machine learning surrogate models (Random Forest, Gradient Boosting, ANN) predict mechanical performance and lifecycle indicators (CO2, embodied energy, water use) of fiber-reinforced concrete façades with up to 99.2% accuracy. Scenario analysis shows optimized configurations reduce embodied carbon while improving energy efficiency, enabling carbon-aware façade selection.
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、建築物のライフサイクル全体でのCO2排出量開示（ライフサイクルカーボン）が注目されており、本フレームワークはBIMとの連携により、設計段階での迅速なカーボン評価を可能にする。特にSSBJ基準や有報でのTCFD開示にも資する可能性がある。
In the global GX context
Globally, this framework addresses the growing demand for data-driven lifecycle assessment in building design, aligning with ISSB standards and EU taxonomy for sustainable construction. The AI-LCA approach enables scalable, rapid optimization of façade systems, supporting carbon-neutral building targets.
👥 読者別の含意
🔬研究者:The AI surrogate modeling methodology for LCA prediction is a novel contribution, offering a template for integrating ML with building performance simulation.
🏢実務担当者:Façade designers and sustainability engineers can use the framework to rapidly evaluate material combinations and optimize for carbon and energy performance at early design stages.
🏛政策担当者:The study provides evidence for updating building codes to incorporate AI-driven LCA tools for embodied carbon reduction targets.
📄 Abstract（原文）
Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO2 emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/info17020126first seen 2026-06-29 06:56:52
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。