初期設計段階の内包炭素評価に向けた半自動BIM-LCA統合:5階建てオフィスビルのケーススタディ
Semi-Automated BIM-LCA Integration for Early-Stage Embodied Carbon Assessment: A Case Study of a Five-Story Office Building (原題)
Fahmioui Hiba, Dong-Tao Xia, Shu-Jun Yan
🤖 gxceed AI 要約
日本語
本研究は、EN 15978およびISO 14040に準拠し、初期設計段階の建物内包炭素(A1–A3)を評価する半自動BIM-LCAワークフローを提示する。VBAによる材料名クリーニング・キーワード分類・排出係数自動割当により、手作業時間を74.8分から4.5分へ94%削減した。5階建てオフィスビルの事例では金属系材料が内包炭素の82%を占め、特に金属スタッドが81.6%と支配的で、非構造部材の重要性を示した。
English
This study presents a semi-automated BIM-LCA workflow for early-stage whole-building embodied carbon (A1–A3) per EN 15978 and ISO 14040. Using VBA-based material cleaning, keyword classification, and automated emission-factor assignment, manual processing time dropped from 74.8 to 4.5 minutes (94% reduction). In a five-story office case, metal materials contributed 82% of embodied carbon—metal stud framing alone 81.6%—highlighting the overlooked role of non-structural elements.
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
建設業のScope 3(カテゴリ1・2)やSSBJ・有報での炭素開示が進む中、設計初期に内包炭素を定量化する実務手法は日本企業の脱炭素設計・サプライチェーン開示に直結する。非専門家でも使える透明な手法は、中小建設・設計事務所の対応力向上に寄与しうる。
In the global GX context
As CSRD, ISSB, and TCFD push building-sector Scope 3 and embodied-carbon disclosure, this transparent, non-proprietary BIM-LCA workflow offers a practical route to early-stage quantification without specialized LCA expertise. It also broadens disclosure scholarship by showing how non-structural materials can dominate a building's carbon profile.
👥 読者別の含意
🔬研究者:BIM-LCA自動化と非構造部材の炭素寄与に関する実証的知見を提供する。
🏢実務担当者:設計初期に内包炭素を迅速に算定し、低炭素設計判断とScope 3開示に活用できる。
🏛政策担当者:建築物の内包炭素規制や開示制度設計において、非構造部材を含む評価手法の必要性を示唆する。
📄 Abstract(原文)
The integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) for early-stage building design is often limited by inconsistent material naming, manual classification, and time-consuming carbon calculations. Existing BIM-LCA approaches typically focus on structural components such as concrete and steel, while non-structural elements including partitions, finishes, and insulation remain underexplored despite their potential contribution to total embodied carbon. This study presents a semi-automated BIM-LCA workflow for whole-building embodied carbon assessment during cradle-to-gate (A1–A3) stages, developed in accordance with EN 15978:2011 and ISO 14040:2006. The workflow integrates automated material name cleaning, keyword-based classification with priority logic, and embedded density and emission factor assignment within a unified Excel-based framework using Visual Basic for Applications (VBA). A five-story office building modelled in Autodesk Revit at Level of Development (LOD) 300 serves as the case study, with material quantities extracted from the BIM model and processed through the VBA-based classification system; density values were obtained from the Engineering ToolBox and emission factors from the ICE Database Version 3.0. The workflow reduces manual processing time from 74.8 minutes to 4.5 minutes (94% reduction) through rule-based material classification and automated emission factor assignment. Results indicate that metal-related materials contribute 82% of total embodied carbon, with metal stud framing alone accounting for 81.6% due to its extensive use in partition walls across all five floors. Pareto analysis further confirms this concentration, with the top five materials responsible for over 94% of total embodied carbon, demonstrating that a small number of materials can dominate a building's embodied carbon profile. These findings highlight the importance of accounting for non-structural elements, frequently overlooked in conventional BIM-LCA assessments. The proposed method enables early-stage low-carbon design decisions without requiring specialized LCA expertise, offering a transparent, non-proprietary alternative to commercial BIM-LCA tools adaptable to different building typologies through modification of the keyword-based classification system.
🔗 Provenance — このレコードを発見したソース
- semanticscholar http://article.isciencegroup.com/pdf/j.jsts.2026.02.001first seen 2026-09-24 05:13:45 · last seen 2026-09-29 05:30:52
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