Advanced Engineering Informatics最新文献_第2页

PE-MPINN: A parameters-enhanced multiphysics-informed neural network for data assimilation of seepage-consolidation coupling problems in spatially variable soils PE-MPINN：用于空间变土渗流-固结耦合问题数据同化的参数增强多物理场信息神经网络

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

Advanced Engineering Informatics

Pub Date : 2026-01-29 DOI: 10.1016/j.aei.2026.104376

Mingyue Sun , Gang Ma , Tongming Qu , Shaoheng Guan , Jiangzhou Mei , Jingzhou Wang , Wei Zhou

Physics-Informed Neural Networks (PINNs) have been widely applied for solving inverse problems due to the powerful capability of integrating physical laws with data-driven learning. However, in the context of multi-field coupling processes in spatially variable soils, conventional PINNs still struggle to explicitly identify uncertain parameters and remain underexplored in engineering-scale applications. To overcome these limitations, this study proposes a Parameters-Enhanced Multiphysics-Informed Neural Network (PE-MPINN) framework for data assimilation of seepage-consolidation problems in spatially variable soils. The framework integrates three-dimensional consolidation theory with random fields, using trainable Karhunen-Loève Expansion vectors to infer heterogeneous soil parameters. Furthermore, a parameters-enhanced subnetwork is introduced to iteratively refine these vectors to continuously improve the representation of soil variability. The proposed approach is validated on a core wall rockfill dam. Results show that PE-MPINN successfully assimilates monitoring and testing data, and accurately predicts pore water pressure, earth pressure, hydraulic conductivity, and compression modulus at engineering scales. Moreover, PE-MPINN demonstrates strong robustness under sparse, noisy data and varying heterogeneity conditions, while achieving superior spatiotemporal extrapolation accuracy. This study highlights the value of integrating physical knowledge with data assimilation by offering a novel, efficient framework for real-time seepage-consolidation analysis and geotechnical digital twin applications.

基于物理信息的神经网络（pinn）由于将物理定律与数据驱动学习相结合的强大能力，在求解逆问题方面得到了广泛的应用。然而，在空间可变土壤中多场耦合过程的背景下，传统的PINNs仍然难以明确识别不确定参数，并且在工程规模应用中仍未得到充分的探索。为了克服这些限制，本研究提出了一个参数增强的多物理场信息神经网络（PE-MPINN）框架，用于空间可变土壤的渗流固压问题的数据同化。该框架将三维固结理论与随机场相结合，利用可训练的karhunen - lo扩展向量来推断非均质土壤参数。在此基础上，引入参数增强子网络对这些向量进行迭代细化，不断提高土壤变异性的表征。该方法在某心墙堆石坝上得到了验证。结果表明，PE-MPINN能有效地同化监测和测试数据，并能准确预测工程尺度下的孔隙水压力、土压力、导水率和压缩模量。此外，PE-MPINN在稀疏、噪声数据和变化异质性条件下表现出较强的鲁棒性，同时具有优异的时空外推精度。本研究通过为实时渗流固结分析和岩土工程数字孪生应用提供一个新颖、高效的框架，强调了将物理知识与数据同化相结合的价值。

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引用次数: 0

Socio-technical assessment of generative AI integration in architecture, engineering, and construction (AEC) workflows: An empirical study using O*NET occupational taxonomy 建筑、工程和施工（AEC）工作流程中生成式AI集成的社会技术评估：使用O*NET职业分类的实证研究

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

Advanced Engineering Informatics

Pub Date : 2026-01-28 DOI: 10.1016/j.aei.2026.104392

Ruoxin Xiong , Yael Netser , Pingbo Tang , Beibei Li , Joonsun Hwang

Generative artificial intelligence (GAI) has the potential to reshape workflows across the Architecture, Engineering, and Construction (AEC) sector. While previous research has offered valuable technical demonstrations and conceptual analyses, empirical evidence quantifying GAI-related impacts across AEC occupations and systematic assessment of adoption readiness remain limited. This study develops a domain-specific socio-technical evaluation framework that provides occupational-level analysis of technical capabilities, social risks, and adoption barriers across thirteen O*NET-defined AEC occupations. Data were collected through a six-month survey of 162 AEC professionals, complemented by six expert interviews and a systematic literature review. The findings reveal: (1) Technical Capability, measured using exposure scores ranging from −1 (low applicability) to +1 (high applicability), shows moderate applicability in design-oriented roles (e.g., architectural drafters: 0.16) and minimal alignment for site-based and manual activities (e.g., construction laborers: −0.89). (2) Social Risks, assessed on a 0–1 scale of concern, identify hallucinations (0.71), data privacy (0.70), and intellectual property issues (0.69) as critical concerns. (3) Socio-Technical Adoption highlights limited technical expertise (26.0%) and uncertain return on investment (16.8%) as primary barriers, while respondents emphasized the need for usage guidelines and standards (29.6%) and targeted training (29.2%) to facilitate responsible integration. Based on these findings, the study outlines strategic priorities for responsible GAI deployment, including AEC-specific standards, targeted workforce training, human-in-the-loop validation mechanisms, and domain-tailored digital infrastructure. The framework and empirical evidence provide a foundation for researchers, practitioners, and policymakers seeking to guide the safe and effective integration of GAI into AEC workflows.

生成式人工智能（GAI）有可能重塑建筑、工程和施工（AEC）领域的工作流程。虽然以前的研究提供了有价值的技术演示和概念分析，但量化人工智能对AEC职业的影响和系统评估采用准备程度的经验证据仍然有限。本研究开发了一个特定领域的社会技术评估框架，提供了13个O* net定义的AEC职业的技术能力、社会风险和采用障碍的职业层面分析。通过对162名AEC专业人员进行为期6个月的调查，并辅以6次专家访谈和系统的文献综述，收集了数据。研究结果显示：(1)技术能力，使用从−1（低适用性）到+1（高适用性）的暴露分数进行测量，显示出在面向设计的角色（例如，建筑起草人：0.16）中的中等适用性，以及对基于现场和手工活动（例如，建筑工人：- 0.89）的最小一致性。(2)社会风险，以0-1的关注程度进行评估，认为幻觉（0.71）、数据隐私（0.70）和知识产权问题（0.69）是关键问题。(3)社会技术采用强调有限的技术专业知识（26.0%）和不确定的投资回报（16.8%）是主要障碍，而受访者强调需要使用指南和标准（29.6%）和有针对性的培训（29.2%）来促进负责任的整合。基于这些发现，该研究概述了负责任的GAI部署的战略重点，包括aec特定标准、有针对性的劳动力培训、人在环验证机制和领域定制的数字基础设施。该框架和经验证据为研究人员、从业者和政策制定者寻求指导GAI安全有效地整合到AEC工作流程中提供了基础。

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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 : 2026-01-28 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

AQUADA-UNI: A super-efficient unified cascade multimodal model for thermographic wind turbine blade segmentation and damage detection AQUADA-UNI：一种用于热成像风力涡轮机叶片分割和损伤检测的超高效统一叶栅多模态模型

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

Advanced Engineering Informatics

Pub Date : 2026-01-28 DOI: 10.1016/j.aei.2026.104387

Xiaodong Jia, Xiao Chen

Structural damage inspection of wind turbine rotor blades is crucial for preventing catastrophic failures. AI-driven automated approaches are increasingly being used in blade damage inspection due to their high efficiency and consistency. This paper proposes a novel AI-driven cascade multimodal model to perform thermographic blade segmentation and blade damage detection in one single step with exceptional computational efficiency. For blade segmentation, this model leverages RGB images to enhance thermographic blade segmentation, enabling multimodal complementarity and improving segmentation accuracy. For blade damage detection, the model employs hard example mining and curriculum learning to significantly enhance its ability to distinguish anomalous boundaries between normal and anomaly image patches. Utilizing a computationally efficient cascade network architecture, this model unifies conventional multi-step tasks into one single step for AI-based blade damage detection. Compared to the state-of-the-art method (i.e., the second-best method in our benchmark study), the proposed model halves the computational cost and nearly triples the Frames Per Second (FPS) while maintaining equivalent or slightly higher prediction accuracy, particularly in the test cases where the blade damage is small and its thermal contrast is low, making analysis with conventional AI-based methods extremely challenging. We also shared the dataset with the public to support further study.

风力发电机转子叶片结构损伤检测是防止灾难性失效的关键。人工智能驱动的自动化方法由于其高效率和一致性，越来越多地用于叶片损伤检测。本文提出了一种新的人工智能驱动的叶栅多模态模型，该模型可以一步完成热成像叶片分割和叶片损伤检测，并且具有优异的计算效率。对于叶片分割，该模型利用RGB图像增强热成像叶片分割，实现多模态互补，提高分割精度。在叶片损伤检测方面，该模型采用了硬例挖掘和课程学习的方法，显著增强了正常和异常图像斑块之间异常边界的区分能力。该模型利用计算效率高的级联网络架构，将传统的多步骤任务统一为一个步骤，用于基于人工智能的叶片损伤检测。与最先进的方法（即在我们的基准研究中排名第二的方法）相比，所提出的模型将计算成本减半，将每秒帧数（FPS）提高近三倍，同时保持同等或略高的预测精度，特别是在叶片损伤较小且热对比较低的测试用例中，这使得使用传统的基于人工智能的方法进行分析极具挑战性。我们还与公众分享了数据集，以支持进一步的研究。

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引用次数: 0

Integrated optimization of non-permutation flow shop scheduling and maintenance under time-varying operating conditions considering quality control 时变工况下考虑质量控制的非排列流水车间调度与维修集成优化

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

Advanced Engineering Informatics

Pub Date : 2026-01-28 DOI: 10.1016/j.aei.2026.104388

Zhijie Yang , Xinkai Hu , Yibing Li , Kaipu Wang , Shunsheng Guo , Zao Liu

Production, maintenance, and quality are key components influencing the performance of manufacturing systems and should be considered in an integrated manner. However, the production dimensions of previous integrated studies have primarily focused on inventory and lot sizing, overlooking the impact of production scheduling on maintenance and quality. In fact, product quality depends on the degradation state of quality-related components (QRC) in the corresponding machine at different times. Therefore, effective production scheduling needs to further incorporate quality considerations. Meanwhile, maintenance should be coordinated with scheduling to maintain high reliability of both machines and QRC without interrupting product processing. Thus, this study first establishes machine deterioration and product quality loss models considering QRC under time-varying conditions, respectively. Based on this, a mixed integer linear programming (MILP) model for non-permutation flow shops and maintenance is further constructed. An improved multi-objective co-evolutionary artificial bee colony algorithm (IMOCABC) is proposed. It uses six heuristic rules to generate a high-quality initial population. Four crossover operators and six problem-specific neighborhood search operators are applied to improve both global and local search ability and promote cooperative evolution. The effectiveness of the proposed improvement strategy was verified through 20 cases. Meanwhile, it is indicated that IMOCABC outperforms four advanced metaheuristic algorithms. The proposed model and algorithm are applied to an automotive engine manufacturing workshop, reducing the combined cost of preventive maintenance and quality loss from 25,806 (traditional scheme) to 11,877 (integrated scheme), achieving a 46% reduction.

生产、维护和质量是影响制造系统性能的关键组成部分，应该以综合的方式加以考虑。然而，以往综合研究的生产维度主要集中在库存和批量上，忽略了生产调度对维修和质量的影响。实际上，产品质量取决于相应机器中质量相关部件（QRC）在不同时间的退化状态。因此，有效的生产调度需要进一步纳入质量考虑。同时，维护要与调度协调，在不中断产品加工的前提下，保持机器和QRC的高可靠性。因此，本研究首先建立了时变条件下考虑质量rc的机器劣化和产品质量损失模型。在此基础上，进一步构造了非排列流车间和维修的混合整数线性规划（MILP）模型。提出一种改进的多目标协同进化人工蜂群算法（IMOCABC）。它使用六条启发式规则来生成高质量的初始种群。采用4个交叉算子和6个问题邻域搜索算子，提高全局和局部搜索能力，促进协同进化。通过20个案例验证了改进策略的有效性。同时，IMOCABC算法优于四种先进的元启发式算法。将所提出的模型和算法应用于某汽车发动机制造车间，将预防性维修和质量损失的总成本从传统方案的25806降低到集成方案的11877，降低了46%。

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引用次数: 0

First things first: Effects of sequential AR/VR exposure on skill acquisition in industrial training 重要的是：顺序AR/VR暴露对工业培训中技能习得的影响

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

Advanced Engineering Informatics

Pub Date : 2026-01-27 DOI: 10.1016/j.aei.2026.104328

Varun Phadke , Casper Harteveld , Kemi Jona , Mohsen Moghaddam

This paper explores the distinctive and collective affordances of augmented reality (AR) and virtual reality (VR) for industrial training, with a focus on their integrated use and deployment strategies. AR and VR applications were developed to conduct a two-stage, between-subjects user study on a real-life cold spray additive manufacturing task, with varying orders of exposure to AR/VR training. Results reveal nuanced adaptation patterns, indicating that VR-first training reduces the cognitive load during subsequent AR-guided training, thereby enhancing confidence and task efficiency. Conversely, AR-first training supports procedural grounding but presents challenges when transitioning to the immersive spatial demands of VR training. Interestingly, task completion times were found to be independent of the order of exposure, highlighting the flexibility of deployment strategies. Clustering analysis further identifies distinct participant response patterns, offering deeper insights into workload, learning effectiveness, retention, and types of errors. These findings emphasize the importance of leveraging task understanding before the deployment of AR and VR to maximize learning outcomes in complex psychomotor tasks.

本文探讨了增强现实（AR）和虚拟现实（VR）在工业培训中的独特和集体能力，重点是它们的综合使用和部署策略。开发了AR和VR应用程序，对现实生活中的冷喷涂增材制造任务进行了两阶段的受试者之间的用户研究，并进行了不同顺序的AR/VR培训。结果揭示了细微的适应模式，表明vr先行训练减少了后续ar引导训练中的认知负荷，从而增强了信心和任务效率。相反，ar优先培训支持程序基础，但在过渡到VR培训的沉浸式空间需求时提出了挑战。有趣的是，任务完成时间与暴露的顺序无关，突出了部署策略的灵活性。聚类分析进一步确定不同的参与者响应模式，从而更深入地了解工作量、学习效率、保留和错误类型。这些发现强调了在部署AR和VR之前利用任务理解的重要性，以最大限度地提高复杂精神运动任务的学习效果。

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引用次数: 0

Multi-label sewer defect classification based on CLIP with fine-to-coarse contextual representations 基于精细到粗上下文表示的CLIP多标签下水道缺陷分类

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

Advanced Engineering Informatics

Pub Date : 2026-01-27 DOI: 10.1016/j.aei.2026.104362

Yisu Ge , Jialuo Guo , Zhihao Yang , Zhaomin Chen , Liyan Chen , Guodao Zhang

Sewer defect recognition is a critical foundation for urban drainage systems, by analyzing the video in the sewer to find the problems. Contrastive Language-Image Pre-training model(CLIP) performs well on general vision tasks but misses the fine-grained structural variations and localized defect features, resulting in limited performance in practical sewer defect classification. Therefore, a CLIP based multi-label sewer defect classification method is proposed, which leverages the transfer capability of large language model and integrates fine-grained visual-linguistic features. To tackle the problem of insufficient fine-grained defect feature extraction, the Prompt-based Contextual Representation Construction (PCRC) module is designed, leveraging learnable prompts and a two-stage modeling strategy to capture fine-to-coarse contextual representations for each category. Furthermore, the Feature-Level Matching (FLM) module is introduced to align the fine-grained image-text feature for improving defect recognition accuracy. Finally, the ablation studies and extensive comparisons with advanced methods on the public dataset Sewer-ML is presented. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance, of which the mAP and F1-score achieve 75.02% and 80.08%, respectively.

下水道缺陷识别是城市排水系统的重要基础，通过分析下水道中的视频来发现问题。对比语言图像预训练模型（CLIP）在一般视觉任务中表现良好，但缺少细粒度结构变化和局部缺陷特征，在实际下水道缺陷分类中性能有限。为此，提出了一种基于CLIP的多标签下水道缺陷分类方法，该方法利用了大语言模型的迁移能力，并融合了细粒度的视觉语言特征。为了解决细粒度缺陷特征提取不足的问题，设计了基于提示的上下文表示构建（PCRC）模块，利用可学习的提示和两阶段建模策略来捕获每个类别的从细到粗的上下文表示。在此基础上，引入特征级匹配（FLM）模块对细粒度的图像-文本特征进行对齐，提高缺陷识别精度。最后，介绍了在公共数据集下水道- ml上的消融研究以及与先进方法的广泛比较。实验结果表明，该方法达到了最先进的性能，mAP和f1得分分别达到75.02%和80.08%。

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引用次数: 0

Enhancing aviation safety with artificial intelligence: A systematic literature review on recent advances, challenges and future perspectives 用人工智能提高航空安全：对近期进展、挑战和未来展望的系统文献综述

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

Advanced Engineering Informatics

Pub Date : 2026-01-27 DOI: 10.1016/j.aei.2026.104378

Cho Yin Yiu , Wen-Chin Li , Kam K.H. Ng , Chia-Fen Chi , Jens Schiefele

The global air traffic is projected to grow significantly in the coming decades, leading to denser airspace and higher operational complexities. Therefore, academic and practitioners are now unleashing the potential of artificial intelligence (AI), particularly the recent advances in large language models (LLM), computer vision, and speech recognition in enhancing aviation safety through advanced cockpit design, AI assistants, human performance monitoring, and supporting air accident investigations. These applications demonstrate a significant promise in enhancing aviation safety. Nevertheless, there are still challenges in applying safe and reliable AI in supporting these safety–critical domains. Indeed, many aviation safety issues, such as accident analysis, human factors, and preventive system designs, are interconnected instead of standalone issues. This systematic literature review explores the recent advances, challenges, and future perspectives on leveraging AI to enhance aviation safety from a macro perspective. Therefore, a framework is established to review relevant studies. First, we identify the relevant literature from initial search, inspection, and screening. After that, we analyse the domains applied and the models leveraged in aviation safety enhancement on the 175 selected studies using content analysis. Then, thematic analysis is applied to reveal the challenges of applying safe and reliable AI in aviation safety. Given the challenges identified, this review recommends future work to incorporate explainable AI, develop AI certification frameworks, design based on hybrid intelligence, and adopt diversified dataset for generalisation.

未来几十年，全球空中交通预计将大幅增长，导致空域更密集，运营复杂性更高。因此，学术界和实践者现在正在释放人工智能（AI）的潜力，特别是最近在大型语言模型（LLM）、计算机视觉和语音识别方面的进展，通过先进的驾驶舱设计、人工智能助手、人类表现监测和支持航空事故调查来提高航空安全。这些应用在提高航空安全方面显示出巨大的希望。然而，在应用安全可靠的人工智能来支持这些安全关键领域方面仍然存在挑战。事实上，许多航空安全问题，如事故分析、人为因素和预防系统设计，都是相互联系的，而不是单独的问题。这篇系统的文献综述从宏观角度探讨了利用人工智能提高航空安全的最新进展、挑战和未来前景。因此，本文建立了一个框架来回顾相关研究。首先，我们从最初的搜索、检查和筛选中确定相关文献。之后，我们对175项选定的研究使用内容分析分析了航空安全增强的应用领域和模型。然后，通过专题分析，揭示了安全可靠的人工智能在航空安全中的应用所面临的挑战。鉴于所确定的挑战，本综述建议未来的工作包括纳入可解释的人工智能，开发人工智能认证框架，基于混合智能的设计，并采用多样化的数据集进行推广。

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引用次数: 0

A unified LLM-KG framework for low‑annotation urban rail transit signal system operation: knowledge acquisition and dynamic update 低标注城市轨道交通信号系统运行的统一LLM-KG框架：知识获取和动态更新

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

Advanced Engineering Informatics

Pub Date : 2026-01-24 DOI: 10.1016/j.aei.2026.104327

Wei Cai , Xiaomin Zhu , Zeyu Sun , Aihui Ye , Guanhua Fu , Runtong Zhang

Intelligent operation and maintenance (O&M) of urban rail transit signal systems (URTSS) is essential for ensuring train safety and operational efficiency. However, most O&M data exist as unstructured and sparsely labeled texts, posing major challenges for reliable knowledge extraction, semantic reasoning, and dynamic knowledge management. To address these issues, this paper proposes a unified large language model-knowledge graph framework (ULLM-KG) tailored for low-annotation, knowledge-intensive O&M environments. Firstly, a bidirectional knowledge graph construction mechanism (BKGC) is introduced to rapidly build a domain-specific initial knowledge graph. Secondly, a KG-enhanced distantly supervised entity and event extraction method (KG-DS3E) is designed to enhance critical knowledge extraction accuracy from unstructured texts. Thirdly, a prompt-driven knowledge-enhanced reasoning method (PD-KER) is proposed to improve semantic quality in fault diagnosis and maintenance recommendations. Lastly, a dynamic knowledge graph updating mechanism with temporal awareness and conflict resolution (DKG-UCF) is used to ensure efficient and accurate knowledge evolution. Based on real-world URTSS O&M data, experimental evaluations are conducted on state-of-the-art LLMs (GPT-4o, DeepSeek-V3, and Qwen3-32B). On datasets with varying annotation ratios and rare faults, ULLM-KG demonstrates significantly superior performance in knowledge extraction and reasoning tasks compared to other state-of-the-art methods. Its ability to dynamically update knowledge is also verified to be excellent. ULLM-KG provides a general solution for the intelligent O&M of URTSS under low-annotation conditions.

城市轨道交通信号系统（URTSS）的智能运维是保障列车安全和运行效率的关键。然而，大多数O&；M数据以非结构化和稀疏标记的文本形式存在，这对可靠的知识提取、语义推理和动态知识管理提出了重大挑战。为了解决这些问题，本文提出了一个统一的大型语言模型-知识图框架（ULLM-KG），该框架专为低注释、知识密集型的操作和管理环境量身定制。首先，引入双向知识图谱构建机制（BKGC），快速构建特定领域的初始知识图谱；其次，设计了一种kg增强的远程监督实体和事件提取方法（KG-DS3E），以提高从非结构化文本中提取关键知识的准确性。再次，提出了一种提示驱动的知识增强推理方法（PD-KER），以提高故障诊断和维修建议的语义质量。最后，采用一种具有时间感知和冲突解决的动态知识图更新机制（DKG-UCF）来保证知识进化的高效和准确。基于真实的URTSS o&m数据，在最先进的llm （gpt - 40、DeepSeek-V3和Qwen3-32B）上进行了实验评估。在具有不同标注比率和罕见错误的数据集上，ULLM-KG在知识提取和推理任务中表现出明显优于其他最先进方法的性能。其动态更新知识的能力也被证明是优秀的。ULLM-KG为低标注条件下URTSS的智能运维提供了一种通用的解决方案。

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引用次数: 0

FACTS: Training-free zero-shot diffusion framework for facade texture restoration in 3D urban models 事实：用于3D城市模型立面纹理恢复的无训练零射击扩散框架

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

Advanced Engineering Informatics

Pub Date : 2026-01-24 DOI: 10.1016/j.aei.2026.104385

Juexiao Cheng , Xiangru Huang , Guanzhou Chen , Tong Wang , Jiaqi Wang , Xiaoliang Tan , Aiyi Jiang , Xiaodong Zhang

High-fidelity facade texture restoration is crucial for the realism and utility of 3D urban models in digital twin applications. Low-quality textures can compromise visualization, simulation accuracy, and decision-making. This challenge is particularly evident in Level of Detail 1 and 2 (LoD-1 and LoD-2) models, which represent buildings as basic massing models. In these models, textures baked from complex 3D mesh sources often suffer from geometric distortions, occlusions, and inconsistent illumination. To address these issues, we introduce FACTS (Facade Automated Correction and Texture Synthesis), a novel zero-shot, training-free framework for facade texture restoration. FACTS operates as an automated pipeline, taking 3D Mesh as input and producing geometrically and photometrically corrected models. Its key innovations are as follows: (1) a prompt-guided, occlusion-aware inpainting module that uses semantic guidance to repair missing texture regions; (2) a multi-scale edge-feature-guided diffusion process that enforces geometric consistency by leveraging structural priors extracted from the image; and (3) an efficient illumination harmonization method in the CIELAB color space to resolve lighting inconsistencies across texture patches. Recognizing that conventional metrics fail to assess architectural integrity, we propose three novel metrics: the Edge Straightness Score (ESS), Hough Transform Line Consistency (HTLC), and Linearity Index (LI). Our experiments on the SFDB and RUF-3D datasets show significant improvements over baselines. Specifically, FACTS improved ESS, HTLC, and LI scores on degraded textures by 40.69%, 11.16%, and 54.76%, respectively. The framework processes 2.5-megapixel texture in approximately 58.8 s on a single consumer-grade GPU. This work provides a scalable and interpretable solution for the automated restoration of defective facade textures, thereby enhancing the visual realism and structural accuracy of existing 3D urban models. Code and data available at https://github.com/CVEO/FACTS.

在数字孪生应用中，高保真立面纹理修复对于三维城市模型的真实感和实用性至关重要。低质量的纹理会影响可视化、模拟精度和决策。这一挑战在细节级别1和2 （LoD-1和LoD-2）模型中尤为明显，它们将建筑物表示为基本的体块模型。在这些模型中，从复杂的3D网格源烘烤的纹理经常遭受几何扭曲，遮挡和不一致的照明。为了解决这些问题，我们引入了FACTS（立面自动校正和纹理合成），这是一种新的零拍摄，无需训练的立面纹理恢复框架。FACTS作为自动化管道运行，以3D网格为输入，并产生几何和光度校正模型。其主要创新点如下：(1)基于语义引导修复缺失纹理区域的快速引导、闭塞感知的补图模块；(2)利用从图像中提取的结构先验来增强几何一致性的多尺度边缘特征引导扩散过程；(3)在CIELAB色彩空间中采用一种高效的光照协调方法来解决纹理斑块间的光照不一致问题。认识到传统的度量标准无法评估建筑的完整性，我们提出了三个新的度量标准：边缘直线度评分（ESS）、霍夫变换线一致性（HTLC）和线性度指数（LI）。我们在SFDB和RUF-3D数据集上的实验表明，与基线相比，我们有了显著的改进。具体来说，FACTS在退化纹理上分别提高了40.69%、11.16%和54.76%的ESS、HTLC和LI分数。该框架在单个消费级GPU上处理250万像素的纹理大约58.8秒。这项工作为有缺陷的立面纹理的自动修复提供了一个可扩展和可解释的解决方案，从而提高了现有3D城市模型的视觉真实感和结构准确性。代码和数据可在https://github.com/CVEO/FACTS上获得。

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