[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5188":3,"related-5188":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":49,"slug":52,"view_count":15,"doi":53,"paper":54,"created_at":74},5188,"AI-driven whole-life costing for decision-ready UK public building retrofits","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40747-026-02516-9","Abstract Public-sector building retrofits in the United Kingdom require decisions that balance whole-life cost, carbon reduction, service continuity and auditability under limited data and constrained capital budgets. This paper develops a decision-ready whole-life costing (WLC) workflow that integrates environmental, social and governance (ESG) priorities with uncertainty-aware artificial intelligence (AI) for public-building retrofit appraisal. The workflow combines national non-domestic Energy Performance Certificate (EPC) and Display Energy Certificate (DEC) data, Public Sector Decarbonisation Scheme (PSDS) delivery evidence and stakeholder inputs to construct a scenario library of public-building archetypes, retrofit actions and appraisal constraints. Stakeholder priorities from interviews, a questionnaire and a co-design workshop are translated into transparent weights using fuzzy analytic hierarchy process (F-AHP). These weights parameterise a reinforcement learning (RL) planner, while a Bayesian convolutional neural network–long short-term memory (BCNN–LSTM) surrogate provides uncertainty-bounded energy, carbon and cost trajectories for repeated policy evaluation. Results show that the workflow supports auditable retrofit sequencing across archetype and budget conditions and improves composite ESG performance relative to conventional WLC-only, £\u002FtCO 2 e and static multi-criteria decision analysis (MCDA) baselines. For 1000 matched archetype–budget cases, the trained deep Q-network (DQN) decision-search stage achieves a 30.03-fold speedup over traditional scalar feasible-sequence enumeration. The contribution is an integrated and explainable WLC–ESG–AI workflow for early-stage public retrofit decision support. The current validation remains based on public datasets and scenario-level evidence so future work should extend the framework using project-specific metered data, digital twins and live portfolio deployment.","摘要：英国公共部门建筑改造需要在有限数据和受限资本预算条件下，做出平衡全生命周期成本、碳减排、服务连续性和可审计性的决策。本文开发了一种面向决策的全生命周期成本核算（WLC）工作流程，将环境、社会与治理（ESG）优先事项与不确定性感知人工智能（AI）相结合，用于公共建筑改造评估。该工作流程整合了全国非住宅能源性能证书（EPC）和展示能源证书（DEC）数据、公共部门脱碳计划（PSDS）实施证据以及利益相关方输入，构建了公共建筑原型、改造措施和评估约束的场景库。来自访谈、问卷和协同设计研讨会的利益相关方优先事项，通过模糊层次分析法（F-AHP）转化为透明权重。这些权重为强化学习（RL）规划器提供参数化依据，而贝叶斯卷积神经网络—长短期记忆网络（BCNN–LSTM）代理模型则为重复策略评估提供具有不确定性边界的能源、碳和成本轨迹。结果表明，该工作流程支持跨原型和预算条件下可审计的改造排序，并且相对于仅采用传统WLC、£\u002FtCO₂e和静态多准则决策分析（MCDA）的基线方法，改善了综合ESG绩效。对于1000个匹配的原型—预算案例，训练后的深度Q网络（DQN）决策搜索阶段相较于传统标量可行序列枚举实现了30.03倍的加速。本文的贡献在于为早期公共改造决策支持提供了一种集成且可解释的WLC–ESG–AI工作流程。当前验证仍基于公共数据集和场景级证据，因此未来工作应使用项目特定的计量数据、数字孪生和实时组合部署来扩展该框架。",null,"Complex & Intelligent Systems","2026-10-16T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦英国公共建筑改造的AI全生命周期成本评估，与三农、农业信息化、智慧农业等主题无关，因此不纳入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"公共建筑改造","全生命周期成本","建筑碳排放","人工智能决策",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":48},[32,41],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I2",6,true,[],[38,39],"artificial intelligence","decision support",[],{"code":42,"score":43,"supported":35,"title":44,"abstract":45,"tags":47},"I1",3,[],[46],"数字孪生",[],"农业相关性需要复核",[50,51],"英国 公共建筑 改造 全生命周期成本","全生命周期成本 人工智能决策 公共建筑改造 建筑碳排放","英国公共建筑改造全生命周期成本-5188","10.1007\u002Fs40747-026-02516-9",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":15,"oa_url":6,"card":66,"direction":72,"ingested_from":73},"W7210603173",[57,60,62,64],{"name":58,"orcid":59},"Ziru Tao","https:\u002F\u002Forcid.org\u002F0009-0000-5666-5712",{"name":61,"orcid":9},"Paul Baguley",{"name":63,"orcid":9},"Rashid; id_orcid 0000-0001-9983-5929 Maqbool",{"name":65,"orcid":9},"Obuks Ejohwomu",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"构建融合ESG与不确定性AI的英国公共建筑改造全生命周期成本决策流程。","整合EPC\u002FDEC与PSDS数据，用F-AHP赋权、强化学习规划与BCNN-LS","该流程支持可审计的改造排序，ESG综合表现优于传统方法，决策搜索提速30倍。","农业绿色发展与碳","可将该WLC-ESG-AI框架迁移至乡村公共设施或农业建筑节能改造，结合实测数据与数字孪生验证。","数字乡村与农业信息化","openalex","2026-10-17T23:30:39.441589Z",{"total":15,"page":76,"page_size":76,"items":77},1,[]]