The Role of Modern Digital Mechanisms in Shaping Building Structures for Sustainable Development: A Systematic Literature Review
持続可能な開発のための建築構造形成における現代デジタルメカニズムの役割：系統的文献レビュー (AI 翻訳)
Anna Szewczyk, J. Dzwierzynska
🤖 gxceed AI 要約
日本語
本レビューは、AI、生成デザイン、BIMが持続可能な建築設計に与える影響を体系的に評価。PRISMA手法を用い、アルゴリズム知能がSDGs（特に9,11,12,13）を支援するエビデンスを統合。AIベースのヒューリスティック手法により材料使用と構造質量を最適化し、体化炭素を削減できることが示された。生成ワークフローやPINNsは循環性とLCA統合の鍵であるが、遺伝的アルゴリズムの応用不足やAIモデルのエネルギー消費などの課題も指摘された。
English
This systematic review evaluates how AI, Generative Design, and BIM contribute to sustainable development in construction. Using the PRISMA protocol, it synthesizes evidence on algorithmic intelligence supporting UN SDGs (9, 11, 12, 13). Findings show that AI-based heuristic methods optimize material use and structural mass, reducing embodied carbon. Performance-driven generative workflows and PINNs enable circularity and early LCA integration, but gaps exist in genetic algorithm applications and AI energy consumption.
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
日本の建設業界では、SSBJや有報でのカーボン削減要求が高まっており、本レビューの知見はAI活用による建築物の体化炭素削減に直接応用可能。特に、日本のゼロエネルギー建築（ZEH）や省エネ基準への適合において、AIを用いた構造最適化はコスト効率の高い手法を提供する。
In the global GX context
Globally, the construction sector is under pressure to decarbonize, and this review provides a comprehensive framework for integrating AI into sustainable design. It aligns with ISSB's emphasis on embodied carbon reporting and TCFD's focus on transition risks in the built environment. The identified gaps, such as algorithm transparency and standardization, are critical for scaling AI adoption in engineering practice.
👥 読者別の含意
🔬研究者:Identifies key AI techniques (GD, PINNs) and research gaps (e.g., genetic algorithms for steel structures) to guide future work in sustainable structural design.
🏢実務担当者:Offers a roadmap for adopting AI-driven design tools to reduce embodied carbon and integrate LCA early in the design process, aiding compliance with green building certifications.
🏛政策担当者:Provides evidence that AI can accelerate decarbonization in construction, supporting policies that incentivize digitalization and performance-based standards.
📄 Abstract（原文）
The global construction sector is undergoing a major shift driven by Construction 4.0, where traditional structural design methods are increasingly complemented or replaced by advanced digital technologies. This systematic review evaluates how Artificial Intelligence (AI), Generative Design (GD), and Building Information Modeling (BIM) contribute to sustainable development in architecture and civil engineering. Using the PRISMA protocol, the study synthesizes current evidence on the role of algorithmic intelligence in supporting UN Sustainable Development Goals (SDGs), particularly Goals 9, 11, 12 and 13. Findings indicate that transitioning from deterministic engineering approaches to AI-based heuristic methods enables significant optimization of material use and structural mass, thereby reducing embodied carbon in the built environment. Performance-driven generative workflows and physics-informed neural networks (PINNs) emerge as key enablers of circularity and early-stage Life Cycle Assessment (LCA) integration. However, the review also identifies gaps, such as limited applications of genetic algorithms in sustainable steel structure design and the substantial energy consumption associated with large-scale AI models. The study concludes that while digital tools provide transformative potential for decarbonizing the construction sector, future research should focus on improving algorithm transparency, reducing black-box limitations, and standardizing performance metrics to support broader adoption in engineering practice. The review can be a framework to help researchers, engineers, and policymakers integrate emerging AI-tools into sustainable design and advancing decarbonized, resilient built environments.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18115428first seen 2026-06-29 06:48:30
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