エントロピー重み付き適応型SAFE–ファジィモデルによる舗装路盤材への震災がれきRCAの総合材料持続可能性評価
Overall material sustainability assessment of earthquake debris rca in pavement base layers using an entropy-weighted adaptive SAFE–fuzzy model (原題)
Merve Akbaş
🤖 gxceed AI 要約
日本語
2023年カフラマンマラシュ地震で生じた大量がれきの再資源化を目的に、再生骨材（RCA）を0〜100%混入した舗装路盤材の総合材料持続可能性（OMS）を評価する多基準意思決定フレームワークを構築した。30指標をシャノンエントロピーで客観的に重み付けし、5次元に整理した上でマムダニ型ファジィ推論で統合、プレストコット・アレン式持続可能性バロメーターに照合した。結果、自然骨材100%とRCA100%はいずれも持続不可能域（0.380、0.320）に陥り、RCA50%が最高スコア0.740で構造安全性・埋立回避・炭素削減のバランスに優れた。ただしこの最適値は指標・重み・規則ベースに依存するモデル固有の結果である。
English
This study builds a multi-criteria decision framework to assess the Overall Material Sustainability (OMS) of pavement base layers using earthquake-induced recycled concrete aggregates (RCA) at 0–100% replacement. Thirty engineering and ecological indicators were objectively weighted via Shannon Entropy, grouped into five dimensions, and aggregated with a Mamdani fuzzy inference system, then mapped to the Prescott-Allen sustainability barometer. Both 100% natural aggregate (0.380) and 100% RCA (0.320) fell into potentially unsustainable zones, while 50% RCA scored highest (0.740), best balancing structural safety, landfill diversion, and carbon reduction — though this optimum is model-specific, not universal.
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
日本は大規模震災後の災害廃棄物処理と復興インフラの再資源化を繰り返し経験しており、がれき由来再生骨材の活用は建設業の資源循環・炭素削減の実務課題と直結する。SSBJ開示や建設業のScope 3・循環経済対応を検討する企業にとって、材料選定を定量評価する枠組みは参考になる。
In the global GX context
As CSRD and ISSB push companies toward quantified circular-economy and embodied-carbon metrics, this paper offers a replicable MCDM-plus-fuzzy approach for scoring recycled materials against both structural and ecological criteria. It adds disaster-waste recovery as an under-explored lever in sustainable construction and post-crisis infrastructure disclosure.
👥 読者別の含意
🔬研究者:ファジィ推論とエントロピー重み付けを材料持続可能性評価に統合する手法設計の参考になる。
🏢実務担当者:再生骨材の混入率選定を構造・環境・経済の多面で定量比較する際の評価枠組みとして使える。
🏛政策担当者:災害廃棄物の再資源化促進と復興公共調達の基準設計に、多基準の客観評価手法を提供する。
📄 Abstract（原文）
The massive accumulation of debris following the 2023 Kahramanmaraş earthquakes necessitates sustainable resource recovery strategies for resilient infrastructure reconstruction. This study establishes a multi-criteria decision-making framework to evaluate the Overall Material Sustainability (OMS) of pavement base layers incorporating earthquake-induced Recycled Concrete Aggregates (RCA) at varying replacement ratios (0% to 100%). An adapted hierarchical Sustainability Assessment by Fuzzy Evaluation (SAFE) model was developed to overcome the limitations of traditional engineering thresholds. First, 30 primary engineering and ecological indicators, derived from standard testing and techno-economic analysis, were objectively weighted using the Shannon Entropy method. These were structured into five core dimensions: Mechanical Performance, Physical Durability, Environmental Safety, Economic Efficiency, and Social Impact. Subsequently, a Mamdani fuzzy inference system (FIS) aggregated these into Technical Performance and Socio-Eco Efficiency, yielding crisp OMS scores. The defuzzified results, mapped against the Prescott-Allen Sustainability Barometer, revealed a parabolic penalty mechanism for extreme designs. Mix-0 (100% natural aggregate) and Mix-100 (100% RCA) fell into potentially unsustainable zones with scores of 0.380 and 0.320, respectively, failing to balance ecological preservation with structural safety. Conversely, among the five investigated mixtures, Mix-50 (50% RCA) achieved the highest model-derived score (0.740) and the best-performing balance between load-bearing requirements, landfill diversion, and carbon reduction. This model-specific optimum depends on the selected indicators, Shannon Entropy weights, and fuzzy rule base and is not a universal RCA replacement ratio. Ultimately, this framework provides a robust, data-driven tool to optimize disaster waste management and accelerate sustainable urban recovery.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1093/tse/tdag070first seen 2026-11-07 05:43:18
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