Advanced Engineering Informatics最新文献_第10页

Advancing quality control in off-site construction with large language models enhanced by hybrid retrieval-augmented generation 利用混合检索增强生成增强的大型语言模型推进非现场施工的质量控制

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-02-07 DOI: 10.1016/j.aei.2026.104381

Fanfan Meng, Mi Pan

Quality control (QC) is critical for off-site construction (OSC), but it still relies heavily on the knowledge and expertise of inspectors. Projects face challenges involving heterogeneous and fragmented knowledge from multiple stakeholders across different stages, compounded by skilled labor shortage, subjective biases, and human errors. A consistent and reliable approach is needed to guide knowledge-informed QC in OSC, yet currently lacking. This paper aims to develop a novel knowledge-driven framework for advancing off-site construction quality control, empowered by hybrid retrieval-augmented generation (hybrid RAG)-enhanced large language models (LLMs). The hybrid RAG employs a prompt-based approach for entity and relationship extraction to support automated graph construction from unstructured knowledge. Then, a semantic alignment approach is designed to align dense retrieval, sparse retrieval, and subgraph traversal for vector-graph hybrid retrieval, thereby enabling the LLMs to generate more reliable outputs for complex QC decision-making scenarios. Comparative analysis against baseline RAG was conducted on three designed use cases, containing quality information retrieval, quality compliance checking, and quality control task guidance, using three broadly used open-source LLMs, namely DeepSeek-R1-14B, GPT-OSS-20B, and Qwen3-14B. The results demonstrate the superiority of the proposed hybrid RAG in significantly improving model response accuracy, trustworthiness and reliability. This study further demonstrates that medium-sized LLMs can effectively address complex retrieval and generation tasks guided by appropriate approaches. The findings of this study offer valuable insights for advancing construction QC practices, and inform future research in consistent and reliable knowledge retrieval for addressing knowledge-intensive tasks in the architecture, engineering and construction industry.

质量控制（QC）对非现场施工（OSC）至关重要，但它仍然严重依赖于检查员的知识和专业知识。项目面临的挑战包括来自不同阶段的多个涉众的异构和碎片化的知识，以及熟练劳动力短缺、主观偏见和人为错误。在OSC中，需要一个一致和可靠的方法来指导知识灵通的QC，但目前缺乏。本文旨在通过混合检索-增强生成(hybrid RAG)-增强的大型语言模型（llm），开发一种新的知识驱动框架，用于推进非现场施工质量控制。混合RAG采用基于提示的方法进行实体和关系提取，以支持从非结构化知识中自动构建图形。然后，设计了一种语义对齐方法，对向量图混合检索中的密集检索、稀疏检索和子图遍历进行对齐，从而使llm能够为复杂的QC决策场景生成更可靠的输出。采用DeepSeek-R1-14B、GPT-OSS-20B和Qwen3-14B三种广泛使用的开源llm，对包含质量信息检索、质量符合性检查和质量控制任务指导的三个设计用例与基线RAG进行对比分析。结果表明，该算法显著提高了模型响应精度、可信度和可靠性。本研究进一步证明，在适当的方法指导下，中型llm可以有效地解决复杂的检索和生成任务。本研究的发现为推进建筑质量控制实践提供了有价值的见解，并为未来的研究提供了一致和可靠的知识检索，以解决建筑、工程和建筑行业的知识密集型任务。

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引用次数: 0

Advancing sustainable agriculture in Atlantic Canada through HYDRA-SE: A hybrid decision and regression stacked ensemble for yield prediction 通过HYDRA-SE推进加拿大大西洋地区的可持续农业：用于产量预测的混合决策和回归堆叠集合

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1016/j.aei.2026.104433

Avneet Kaur , Raheleh Malekian , Gurjit S. Randhawa , Aitazaz A. Farooque , Ebrahim Hashemi Garmdareh , Bishnu Acharya , Rajandeep Singh , Gurpreet S. Selopal

Crop yield prediction is a fundamental component of global food security and sustainable agricultural planning. Potato, a key staple and cash crop in Atlantic Canada, presents challenges for yield prediction due to the complex nature of the relationships between soil parameters and crop production. In this research, we developed a machine learning (ML) framework that integrates multi-method feature selection and stacked ensemble learning to predict potato yield. The datasets consisted of soil analytical, geospatial, and productivity parameters from potato fields across Prince Edward Island (PEI) and New Brunswick (NB) during the 2017–2018 growing seasons. Stepwise regression-based feature selection identified eight significant features from 33 initial features that were included for training 16 baseline ML algorithms. The five top-performing models were used to further develop the proposed ensemble and stacked ensemble learning methods. Overall, the results indicated that the CatBoost, XGBoost, LightGBM, random forest (RF), and bagged trees outperformed other traditional ML methods. The proposed hybrid yield decision and regression approach- stacked ensemble (HYDRA-SE) framework achieved the highest accuracy (R² = 0.88 and RMSE = 3.75). These results illustrate the significant potential of advanced ensemble learning methods to produce reliable and robust predictive capacity for potato yield prediction at a field level, regional level, and perhaps at the national level and can provide a decision-support system for potato precision agriculture (PA) in Atlantic Canada.

作物产量预测是全球粮食安全和可持续农业规划的基本组成部分。马铃薯是加拿大大西洋地区的主要主食和经济作物，由于土壤参数与作物产量之间关系的复杂性，马铃薯对产量预测提出了挑战。在这项研究中，我们开发了一个机器学习（ML）框架，该框架集成了多方法特征选择和堆叠集成学习来预测马铃薯产量。这些数据集包括2017-2018年生长季节爱德华王子岛（PEI）和新不伦瑞克省（NB）马铃薯田的土壤分析、地理空间和生产力参数。基于逐步回归的特征选择从33个初始特征中识别出8个重要特征，这些特征包括用于训练16个基线ML算法。五个表现最好的模型被用来进一步发展所提出的集成和堆叠集成学习方法。总体而言，结果表明CatBoost、XGBoost、LightGBM、随机森林（RF）和袋装树优于其他传统的机器学习方法。所提出的混合产量决策与回归方法-叠置集成（HYDRA-SE）框架获得了最高的精度（R2 = 0.88, RMSE = 3.75）。这些结果说明了先进的集成学习方法在田间、区域甚至国家层面上产生可靠和稳健的马铃薯产量预测能力的巨大潜力，并可以为加拿大大西洋地区的马铃薯精准农业（PA）提供决策支持系统。

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引用次数: 0

Generative AI-driven data augmentation and object-guided vision-language reasoning for PPE compliance analysis in work-at-height 高空作业PPE符合性分析的生成人工智能驱动数据增强和对象引导视觉语言推理

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-24 DOI: 10.1016/j.aei.2026.104364

Wenyu Xu , Wen Yi , Yi Tan

PPE compliance is a fundamental prerequisite for ensuring safety in work-at-height. Although computer vision has advanced PPE detection, challenges remain in dataset scarcity that limits generalization and in weak semantic reasoning that hinders reliable compliance verification. To address these limitations, this paper presents a generative AI-driven data augmentation and an object-guided vision-language model (VLM) to analyze PPE compliance in work-at-height. Safety standards on work-at-height and PPE (e.g., GB 80-2016, GB 2811-2019) are formalized via ChatGPT 4o into a variable pool and structured prompts, which are used as inputs to text-to-image (T2I) generation model for generating a synthetic dataset. Object detection model is employed to detect PPE elements, and the structured outputs of object detection model are integrated with VLM, enabling vision-language reasoning that combines object detection with natural language understanding. Experimental results demonstrate that DALL·E 3 produces a more realistic synthetic dataset than other image generation models, with the hybrid dataset significantly improving detection performance ([email protected]=88.5%, Small Object [email protected]=75.8%). Using YOLOv11 detections as structured inputs, Qwen2.5-VL-7B achieves reliable compliance reasoning (CRA=87.6%, SC=0.83, EQ=4.2), and these advances are consolidated in an integrated platform supporting automated reporting and interactive analysis. This framework enhances work-at-height safety by alleviating data scarcity through generative augmentation and strengthening PPE compliance reasoning.

遵守个人防护装备是确保高空工作安全的基本先决条件。尽管计算机视觉具有先进的PPE检测，但数据集稀缺性限制了泛化，弱语义推理阻碍了可靠的符合性验证，这些方面仍然存在挑战。为了解决这些限制，本文提出了一个生成式人工智能驱动的数据增强和一个对象引导的视觉语言模型（VLM）来分析高空工作中的PPE合规性。通过ChatGPT 40将高空作业和个人防护安全标准（如GB 80-2016、GB 2811-2019）形式化为变量池和结构化提示，并将其作为文本到图像（t2c）生成模型的输入，生成合成数据集。采用目标检测模型对PPE元素进行检测，并将目标检测模型的结构化输出与VLM相结合，实现了目标检测与自然语言理解相结合的视觉语言推理。实验结果表明，与其他图像生成模型相比，DALL·e3生成的合成数据集更真实，混合数据集显著提高了检测性能（[email protected]=88.5%, Small Object [email protected]=75.8%）。Qwen2.5-VL-7B使用YOLOv11检测作为结构化输入，实现了可靠的符合性推理（CRA=87.6%， SC=0.83， EQ=4.2），并将这些进展整合到一个支持自动报告和交互式分析的集成平台中。该框架通过生成增强和加强PPE合规推理来缓解数据稀缺性，从而提高高空作业安全性。

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引用次数: 0

Signal-driven interpretable modeling framework for the inverse design of Al/steel ultrasonic-assisted resistance spot welding 铝/钢超声辅助电阻点焊反设计的信号驱动可解释建模框架

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-06 DOI: 10.1016/j.aei.2025.104295

Baokai Ren, Hao Tu, Juntao Shen, Kang Zhou

Fabricating vehicles from aluminum and steel is an effective way to balance strength with reduced weight, but the mismatch between the metallurgical and thermophysical properties of the two materials makes it difficult to achieve a reliable weld. Ultrasonic-assisted resistance spot welding (UA-RSW) has been reported to produce high-quality Al/steel joints, but the highly complex electro-thermo-mechano-acoustic system is difficult to model and thus optimize. In situ process signals from monitoring sensors offer a wealth of information on the evolution of the weld but are not directly controllable. In this study, a signal-driven interpretable modeling framework was developed that is centered on a dual-layer surrogate model. The model predicts in situ signal process features from controllable process parameters and then maps the predicted signal process features and initial process parameters to the final weld quality metrics. The model is then integrated with the non-dominated sorting genetic algorithm II to identify the Pareto front of process parameters that results in the optimal weld quality metrics. The Shapley additive explanations method is utilized to enhance the interpretability of the model. When the framework was applied to analyzing the UA-RSW process, the results indicated that ultrasonic vibrations provide a nonthermal and mechanical–metallurgical coupling effect that improves the strength of the Al/steel joint while reducing the amount of welding energy. The proposed framework offers a validated tool for transforming process signal data from monitoring sensors into actionable knowledge for the inverse design of complex manufacturing processes.

用铝和钢制造汽车是平衡强度和减轻重量的有效方法，但两种材料的冶金和热物理性能之间的不匹配使得难以实现可靠的焊接。超声辅助电阻点焊（UA-RSW）已被报道用于制造高质量的铝/钢接头，但其高度复杂的电热-热-机械-声系统难以建模和优化。来自监测传感器的现场过程信号提供了关于焊缝演变的丰富信息，但不是直接可控的。在本研究中，开发了一个以双层代理模型为中心的信号驱动的可解释建模框架。该模型根据可控制的工艺参数预测现场信号过程特征，然后将预测的信号过程特征和初始工艺参数映射到最终的焊接质量指标。然后将该模型与非支配排序遗传算法II相结合，确定工艺参数的Pareto前，从而得到最优焊接质量指标。采用Shapley加性解释方法增强模型的可解释性。将该框架应用于UA-RSW工艺分析，结果表明，超声振动提供了一种非热和机械-冶金耦合效应，提高了Al/钢接头的强度，同时降低了焊接能量。提出的框架提供了一种有效的工具，将过程信号数据从监测传感器转换为复杂制造过程逆向设计的可操作知识。

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引用次数: 0

Explainable topological-disordered 3D lattice model for hydration-temperature coupled fields of early-age concrete 早期混凝土水化-温度耦合场的可解释拓扑无序三维点阵模型

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.aei.2026.104358

Shujie Han, Xiao Zhang, Xuejiang Lan, Xiaohui Chang

The inherent structural disorder in early-age concrete significantly impacts computational efficiency and simulation accuracy in three-dimensional (3D) lattice modeling, while modeling parameters may demonstrate non-negligible effects on numerical outcomes. To achieve high-fidelity simulation of coupled hydration-temperature fields, this study proposes an automated modeling framework for topological-disordered 3D lattice structures. Parametric studies revealed that randomness and grid resolution critically influence bulk thermal conductivity. Optimal parameters were systematically determined through systematic simulation results and mechanistic interpretation of elemental heat flux distributions using automated algorithms. A macroscopic statistical relationship between modeling parameters and effective thermal conductivity of the lattice system was established by integrating stochastic analysis with the thermal resistance grid method. Validation demonstrated that this approach effectively captures modeling parametric effects on thermal conduction behavior. Finally, the proposed 3D lattice model’s capability to simulate coupled hydration-thermal fields of early-age concrete was rigorously verified against published experimental datasets under two environmental conditions. Explainable machine learning(ML) analysis was conducted on early-age temperature field simulation results of multiple concrete mixtures to investigate the influential significance of various raw material parameters on early-age temperature development. Furthermore, the parametric analysis framework and statistical methodology offer optimized solutions for disorder lattice modeling in cementitious materials, improving both computational efficiency and physical accuracy.

早期混凝土固有的结构无序性显著影响三维（3D）点阵建模的计算效率和模拟精度，而建模参数可能对数值结果产生不可忽视的影响。为了实现水化-温度耦合场的高保真仿真，本研究提出了一种拓扑无序三维晶格结构的自动建模框架。参数研究表明，随机性和网格分辨率对体导热系数有重要影响。通过系统模拟结果和自动化算法对元素热流密度分布的机理解释，系统地确定了最优参数。将随机分析与热阻网格法相结合，建立了模型参数与晶格系统有效导热系数之间的宏观统计关系。验证表明，该方法有效地捕获了建模参数对热传导行为的影响。最后，根据已发表的实验数据集，在两种环境条件下严格验证了所提出的三维晶格模型模拟早期混凝土水化-热耦合场的能力。对多种混凝土混合料早期温度场模拟结果进行可解释性机器学习（ML）分析，探讨各种原材料参数对早期温度发展的影响意义。此外，参数分析框架和统计方法为胶凝材料的无序晶格建模提供了优化的解决方案，提高了计算效率和物理精度。

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引用次数: 0

AI-supported seismic performance evaluation of structures: challenges, gaps, and future directions at early design stages 人工智能支持的结构抗震性能评估：早期设计阶段的挑战、差距和未来方向

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.aei.2025.104301

Fatma Ak, Berk Ekici, Ugur Demir

This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.

本研究回顾了91篇与抗震建筑设计和基于人工智能（AI）的建模相关的期刊文章，这些文章利用了机器学习、深度学习和元启发式优化算法。以前对人工智能应用的评论检查了工程问题，而没有考虑建筑设计参数和结构不规则对抗震性能的影响。本文讨论了人工智能在整合建筑设计变量和抗震性能目标方面的作用，强调了早期设计阶段的挑战、差距和未来方向。回顾的文章表明，人工智能在解决地震性能目标方面是成功的；然而，评估建筑和结构变量的整体框架尚未提出。该综述强调了利用人工智能进行抗震建筑设计的主要发现、差距和未来方向。

引用次数: 0

BearGen: LLM-guided signal generation framework for bearing fault diagnosis BearGen：基于llm的轴承故障诊断信号生成框架

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-02-04 DOI: 10.1016/j.aei.2026.104400

Jaeyoung Lee , Hyuna Jeon , Uiin Kim , Misuk Kim

Signal data are essential for condition monitoring, fault diagnosis, and decision-making across industrial domains, and research leveraging signal data has been actively pursued in areas such as healthcare and manufacturing. However, acquiring such data is costly and difficult due to factors such as the risk of equipment damage, the need for expert labeling, and the scarcity of fault data. Moreover, collected data often contain sensitive operational information, making sharing difficult, and enterprises are restricted from using high-performance models hosted on external servers due to security concerns. To address these challenges, we propose BearGen, a novel framework that combines the strong generative capabilities of Large Language Models (LLMs) with the precise data distribution learning of diffusion models to synthesize high-quality signal data in on-premise environments. BearGen first employs an LLM to generate descriptions of existing signals and then conditions a description-guided diffusion model on these descriptions to generate high-quality synthetic signals. We evaluated BearGen on eight publicly available bearing fault diagnosis datasets, and the results showed superior performance compared to existing approaches. In addition, we experimentally validated the reliability and usefulness of the generated signal descriptions. Further experiments under conditions simulating real industrial environments — such as limited data availability and severe data imbalance — verified the practical applicability of the framework. By operating in on-premise environments, BearGen resolves data security concerns while alleviating data scarcity and imbalance. Furthermore, by providing natural language descriptions, it enhances interpretability and offers significant potential for decision support in real-world industrial applications.

信号数据对于工业领域的状态监测、故障诊断和决策至关重要，利用信号数据的研究已在医疗保健和制造业等领域得到积极开展。然而，由于设备损坏的风险、需要专家标记以及故障数据的稀缺性等因素，获取此类数据既昂贵又困难。此外，收集的数据通常包含敏感的操作信息，这使得共享变得困难，而且出于安全考虑，企业被限制使用托管在外部服务器上的高性能模型。为了应对这些挑战，我们提出了BearGen，这是一个将大型语言模型（llm）的强大生成能力与扩散模型的精确数据分布学习相结合的新框架，可以在内部部署环境中合成高质量的信号数据。BearGen首先使用LLM生成现有信号的描述，然后在这些描述上条件描述引导扩散模型以生成高质量的合成信号。我们在8个公开可用的轴承故障诊断数据集上对BearGen进行了评估，结果显示，与现有方法相比，BearGen的性能更好。此外，我们通过实验验证了生成的信号描述的可靠性和实用性。在模拟真实工业环境的条件下进行的进一步实验-例如有限的数据可用性和严重的数据不平衡-验证了该框架的实际适用性。通过在内部部署环境中运行，BearGen解决了数据安全问题，同时缓解了数据稀缺和不平衡。此外，通过提供自然语言描述，它增强了可解释性，并为实际工业应用中的决策支持提供了重要的潜力。

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引用次数: 0

DSC-DETR: Real-time detection of transmission line defects using dynamic shuffle context DSC-DETR：利用动态洗牌上下文实时检测传输线缺陷

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-31 DOI: 10.1016/j.aei.2026.104403

Yuanxue Xin , Jiabin Huang , Mengyao Xu , Jiawei Chen , Pengfei Shi

Power transmission line inspection is critical for ensuring grid reliability, yet accurately detecting diverse defects in real-time remains a persistent challenge due to the complexity of defect patterns and environmental variations. To address this problem, we propose the Dynamic Shuffle Context Detection Transformer (DSC-DETR), an end-to-end framework designed for efficient and high-precision defect detection in aerial inspection scenarios. Dynamic Shuffle Context Network (DSCNet) enhances the model’s detection capability for small-scale targets by dynamically integrating local details and global contextual information through cross-scale feature fusion. We designed an Efficient Coupled Encoder, which incorporates a Multilayer Coupled Focusing (MCF) Module. Within the Efficient Coupled Encoder, we deployed large-scale convolution operations using a parallel strategy. By progressively constructing a multi-scale perceptual field, we enhanced semantic information fusion across different feature levels. We incorporated Haar Wavelet Pooling Sampling (HWPS), implicitly encoding spatial relationships into channel dimensions to provide location cues for defect detection and improve accuracy. Our DSC-DETR achieves 52.8% AP (Average Precision) and 138.8 FPS (Frames Per Second) on the Transmission Line Multi-Category Defect (TLMD) dataset and 73% AP and 158.7 FPS on the Inspection of Power Line Assets Dataset (InsPLAD), respectively. This performance surpasses other State-of-the-Art (SOTA) methods in accuracy and real-time, and is beneficial to practical application in real-time transmission line fault scenarios.

输电线路检测是确保电网可靠性的关键，但由于缺陷模式和环境变化的复杂性，实时准确检测各种缺陷仍然是一个持续的挑战。为了解决这个问题，我们提出了动态Shuffle上下文检测转换器（DSC-DETR），这是一个端到端的框架，旨在在航空检测场景中高效、高精度地检测缺陷。动态随机上下文网络（Dynamic Shuffle Context Network, DSCNet）通过跨尺度特征融合动态整合局部细节和全局上下文信息，增强了模型对小尺度目标的检测能力。我们设计了一个高效的耦合编码器，它包含了一个多层耦合聚焦（MCF）模块。在高效耦合编码器中，我们使用并行策略部署了大规模卷积操作。通过逐步构建多尺度感知场，增强了不同特征层次的语义信息融合。采用Haar小波池采样（HWPS）方法，将空间关系隐式编码到通道维度中，为缺陷检测提供位置线索，提高检测精度。我们的DSC-DETR在传输线多类别缺陷（TLMD）数据集上实现了52.8%的AP（平均精度）和138.8 FPS（帧每秒），在电力线资产检查数据集（InsPLAD）上分别实现了73%的AP和158.7 FPS。这一性能在准确性和实时性上均优于其他SOTA方法，有利于实时输电线路故障场景的实际应用。

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引用次数: 0

Transforming healthcare facility management with digital technologies: A systematic review and future roadmap 用数字技术改造医疗设施管理：系统回顾和未来路线图

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-02-09 DOI: 10.1016/j.aei.2026.104404

Xiaoyu Zhang , Yujie Zhang , Yan Peng , Cheong Peng Au-Yong , Nuratiqah Aisyah Awang

Healthcare facilities are mission-critical systems that directly affect the quality and efficiency of healthcare services. Digital technologies (DTs) are emerging as pivotal enablers for transforming healthcare facility management (HFM) towards proactive, data-driven paradigms; however, the rapid growth of related publications has resulted in a fragmented evidence base, lacking a comprehensive and current review of applications, challenges, and future pathways. This paper presents a systematic literature review (SLR) and bibliometric analysis of 74 peer-reviewed articles to synthesize how DTs are applied in HFM. Since 2018, the number of publications has increased markedly. The review examines four major DTs—1) Building Information Modeling (BIM); 2) Internet of Things (IoT); 3) Artificial Intelligence (AI); and 4) Digital Twin—and highlights their roles in strengthening healthcare facility operations in complex and demanding contexts. Despite these advances, the review reveals persistent challenges: interoperability between systems, scalability beyond pilots, cybersecurity and data privacy, and organizational adoption. To address these issues, the paper proposes a structured roadmap with short- and long-term priorities spanning technical and organizational domains, offering concrete actions for engineers, facility managers, and researchers. The findings provide actionable guidance and a focused research agenda to advance HFM digitalization, with implications for operational resilience, cost efficiency, and patient safety.

医疗保健设施是直接影响医疗保健服务质量和效率的关键任务系统。数字技术（DTs）正在成为将医疗设施管理（HFM）转变为主动、数据驱动范式的关键推动因素；然而，相关出版物的快速增长导致了证据基础的碎片化，缺乏对应用、挑战和未来途径的全面和当前的审查。本文通过对74篇同行评议文章的系统文献综述和文献计量学分析，综合分析了在HFM中的应用。2018年以来，出版物数量显著增加。本文探讨了四个主要的DTs-1)建筑信息模型（BIM）；2)物联网（IoT）；3)人工智能（AI）；4)数字孪生，并强调它们在复杂和苛刻环境中加强医疗机构运营方面的作用。尽管取得了这些进步，但该报告也揭示了一些持续存在的挑战：系统之间的互操作性、试点之外的可扩展性、网络安全和数据隐私以及组织采用。为了解决这些问题，本文提出了一个结构化的路线图，其中包括跨越技术和组织领域的短期和长期优先事项，为工程师、设施管理人员和研究人员提供了具体的行动。研究结果为推进HFM数字化提供了可操作的指导和重点研究议程，对运营弹性、成本效率和患者安全具有重要意义。

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引用次数: 0

Motion-prior and Confidence-aware Gaussian Splatting (MCGS) SLAM for 3D scene reconstruction of indoor built environments 运动先验和自信感知高斯飞溅（MCGS） SLAM用于室内建筑环境的三维场景重建

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1016/j.aei.2026.104317

Yuanyuan Deng, Vincent J.L. Gan

3D Gaussian Splatting SLAM is an emerging area for 3D scene reconstruction, yet it faces challenges in maintaining motion and temporal consistency under rapid motion, illumination changes, and dynamic object occlusion in the built environment. This paper proposes a motion-prior and confidence-aware Gaussian Splatting (MCGS) SLAM, which hardnesses a probabilistic motion-prior framework that enforces kinematic constraints and adapts to varying motion states. Secondly, the confidence estimation mechanism evaluates the reliability of Gaussian primitives based on temporal, geometric, photometric, and structural indicators to guide a balanced spatial representation. An adaptive keyframe selection method further optimizes keyframe density and improves temporal coherence by dynamically adjusting keyframe frequency. Lastly, multi-task optimization is undertaken, which combines photometric and geometric loss, probabilistic motion constraints, and confidence-based weighting, enabling joint optimization of pose tracking accuracy and mapping quality. Experiments show that MCGS-SLAM achieves 83% trajectory error reduction and 3D mapping quality gains while maintaining a competitive frame rate.

三维高斯飞溅SLAM是三维场景重建的一个新兴领域，但在快速运动、光照变化和建筑环境中动态物体遮挡的情况下，如何保持运动和时间的一致性面临挑战。本文提出了一种运动先验和置信度感知的高斯飞溅SLAM (MCGS)，该SLAM采用了一种概率运动先验框架，该框架可以强制执行运动约束并适应不同的运动状态。其次，置信估计机制基于时间、几何、光度和结构指标评估高斯原语的可靠性，以指导平衡的空间表示。自适应关键帧选择方法通过动态调整关键帧频率进一步优化关键帧密度，提高时间相干性。最后，进行了多任务优化，结合了光度和几何损失、概率运动约束和基于置信度的加权，实现了姿态跟踪精度和映射质量的联合优化。实验表明，MCGS-SLAM在保持具有竞争力的帧率的同时，实现了83%的轨迹误差减少和3D映射质量的提高。

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