From Prediction to Governed Intervention: AI-Enabled Construction Project Controls for Productivity, Resilience and Net-Zero-Oriented Delivery
予測から統治された介入へ：生産性、レジリエンス、ネットゼロ指向の建設プロジェクト管理におけるAI活用 (AI 翻訳)
Vrcelj Z, Sandanayake MS
🤖 gxceed AI 要約
日本語
建設業界におけるAI活用は予測や監視に留まらず、意思決定への変換が重要である。本論文は統治された意思決定変換の枠組みを提案し、34件の研究をレビューした。実証は限定的だが、AI支援の洞察が権威と説明責任を得る条件を理論化している。
English
This paper addresses the gap between AI technical performance and actual project decision changes in construction. Through a structured review of 34 studies, it proposes a Governed AI Project Controls Framework that separates data foundations from decision interface and governance. Evidence supports theory building rather than causal claims, with productivity closest to intervention and net-zero as a cumulative domain.
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、生産性向上とカーボンニュートラルが課題であり、AI活用のガバナンス枠組みは、SSBJ開示や投資家対応にも示唆を与える。
In the global GX context
Globally, this framework contributes to understanding how AI can be governed in project-based industries, aligning with ISSB and CSRD expectations for transparent decision-making and sustainability outcomes.
👥 読者別の含意
🔬研究者:AIとESGの交差における理論的枠組みと実証研究の方向性を提供する。
🏢実務担当者:AI導入時のガバナンスと意思決定プロセス設計の参考になる。
🏛政策担当者:建設業のAI活用とネットゼロ目標の整合性を評価する際の視点を提供する。
📄 Abstract（原文）
Artificial intelligence (AI) is increasingly used in construction to forecast duration, monitor progress, prioritise risk, support procurement and logistics, improve supply chain visibility, and compare environmental trade-offs. These applications are often judged by their technical performance, yet that does not show whether, or how, an analytical output changes a project decision. This paper addresses that gap through governed decision translation: the process by which an AI-enabled output is inter-preted, validated, challenged, authorized, assigned for implementation, documented, and reviewed. A structured integrative review with framework synthesis was con-ducted using a ScienceDirect seed stream and targeted Web of Science cross-checks. The 34-study corpus was classified by evidence relevance and appraised across study design, deployment maturity, outcome proximity, and methodological credibility. Most studies focus on forecasting, monitoring, optimization, and decision support. Only one provides clear evidence of implementation in a live project, while two others approach an identifiable project-control intervention pathway. The evidence therefore supports theory building rather than causal claims of performance improvement. The resulting Governed AI Project Controls Framework distinguishes the data and analytical foundations of AI-enabled control from the decision interface, governed translation, authorised intervention, value domains, and subsequent learning. It also separates structural, procedural, and interpretive governance. Productivity related performance is treated as relatively close to intervention, resilience as a dynamic capability, and net-zero-oriented delivery as a more cumulative environmental domain. The evidence supports carbon-, energy-, waste-, and material-aware decisions, but not claims of achieved net-zero delivery. The paper offers a conditional explanation of how AI-supported insights may acquire authority, operational consequence, and accountability in temporary, multi-organisational construction projects, together with propositions, boundary conditions, and observable indicators for empirical testing.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.20944/preprints202607.2397.v1first seen 2026-08-07 04:37:21
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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。