Journal of Infrastructure Intelligence and Resilience最新文献

Pareto-optimal seismic co-design of a novel integrated VibroHarvest system for long-period structures 一种用于长周期结构的新型集成振动采集系统的帕累托最优地震协同设计

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-12-27 DOI: 10.1016/j.iintel.2025.100196

Wei Liu , Koichi Kusunoki , Jiang Liu

Long-period structures are susceptible to resonance when subjected to ground motions with dominant frequencies below 1 Hz. This resonance can amplify seismic responses, compromising structural integrity. To enhance seismic resilience and promote energy sustainability, this study introduces an integrated VibroHarvest system (IVHS) that combines structural vibration control with energy harvesting, utilizing a piezoelectric nanogenerator. A multi-objective co-design optimization framework was developed to simultaneously minimize structural displacements and roof-floor accelerations, while maximizing the amount of harvested energy. A matrix-partitioning strategy was integrated into the solution scheme to ensure numerical robustness under strong electromechanical coupling. The dynamic performance of the IVHS was evaluated via time-domain seismic simulations. Additionally, global sensitivity analysis (GSA) was employed to identify the parameters that most significantly influence seismic response and energy-harvesting efficiency. Compared with conventional tuned viscous mass dampers (TVMDs), the IVHS reduces peak isolator displacement and roof-floor acceleration by up to 25.59 % and 15.50 %, respectively. Simultaneously, the system harvests an average power of 0.60 W (0.026–2.66 W under the tested conditions), which is sufficient to enable autonomous post-seismic monitoring and real-time damage evaluation without external power sources. Even the minimum output (∼26 mW) significantly exceeds that of typical piezoelectric harvesters. GSA based on Sobol’s indices reveals that load resistance and electromechanical coupling coefficients influence energy output. Conversely, TVMD damping coefficients and stiffness parameters predominantly influence vibration mitigation. These findings establish a dual-functional design framework for the development of resilient, energy-autonomous civil infrastructure.

当主频率低于1hz的地震动作用下，长周期结构容易产生共振。这种共振会放大地震反应，损害结构的完整性。为了提高地震恢复能力和促进能源可持续性，本研究介绍了一种集成的振动采集系统（IVHS），该系统利用压电纳米发电机将结构振动控制与能量收集相结合。开发了一个多目标协同设计优化框架，以同时最小化结构位移和屋顶-地板加速度，同时最大化收获的能量。在求解方案中引入矩阵划分策略，保证了在强机电耦合条件下的数值鲁棒性。通过时域地震模拟对IVHS的动态性能进行了评价。此外，采用全局敏感性分析（GSA）来识别对地震反应和能量收集效率影响最大的参数。与传统的调谐粘性质量阻尼器（TVMDs）相比，IVHS可将隔震器的峰值位移和顶板加速度分别降低25.59%和15.50%。同时，该系统的平均功率为0.60 W（测试条件下为0.026-2.66 W），足以在没有外部电源的情况下实现震后自主监测和实时损伤评估。即使是最小输出（~ 26 mW）也大大超过了典型的压电采集器。基于Sobol指标的GSA分析揭示了负载阻力和机电耦合系数对能量输出的影响。相反，TVMD阻尼系数和刚度参数主要影响减振。这些发现为弹性、能源自主的民用基础设施的发展建立了双重功能设计框架。

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引用次数: 0

CrackMambaNet: A road crack segmentation network based on dual-branch encoder ensemble with adaptive feature fusion CrackMambaNet：一种基于双分支编码器集成和自适应特征融合的道路裂缝分割网络

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-12-11 DOI: 10.1016/j.iintel.2025.100194

Shiqiang Qin, Yutao Yang, Mengfan Chen

Automated crack detection is essential for extending the service life of roads and ensuring the operational safety of road structures. Numerous computer vision-based networks have been proposed for crack segmentation in recent years. However, existing networks frequently struggle to balance global context modeling with local detail preservation and lack sufficient robustness in complex scenarios. To address these issues, this study proposes CrackMambaNet for automated pavement crack detection. It is a U-shaped segmentation network with a dual-branch encoder incorporating an adaptive feature fusion module (FFM). The dual-branch encoder consists of global and local branches. The former stacks visual state space (VSS) blocks to capture multi-scale long-range dependencies with linear complexity. The latter is built upon an enhanced ResNet34 backbone integrated with visual attention convolution modules to reinforce edge-aware and fine-grained feature extraction. An adaptive FFM is thus introduced to suppress redundant features through dual SE-attention. Extensive experiments were conducted on various public crack datasets to validate the performance of CrackMambaNet. The results indicate that CrackMambaNet achieved the best performance across different datasets compared with seven existing networks. The mean intersection over union on the DeepCrack, EdmCrack600, and CFRL datasets reached 87.94 %, 76.33 %, and 75.58 %, respectively. These results demonstrate that CrackMambaNet can balance global context modeling and local detail preservation, exhibiting excellent robustness and accuracy in complex scenarios and fine crack segmentation tasks.

自动裂缝检测对于延长道路使用寿命，保证道路结构的运行安全至关重要。近年来，人们提出了许多基于计算机视觉的裂缝分割网络。然而，现有的网络常常难以平衡全局上下文建模和局部细节保存，在复杂场景下缺乏足够的鲁棒性。为了解决这些问题，本研究提出了用于路面裂缝自动检测的CrackMambaNet。它是一个u型分割网络，双支路编码器结合自适应特征融合模块（FFM）。双支路编码器由全局支路和局部支路组成。前者通过堆叠视觉状态空间（VSS）块来捕获具有线性复杂性的多尺度远程依赖关系。后者是建立在增强的ResNet34骨干与视觉注意卷积模块集成，以加强边缘感知和细粒度特征提取。因此，引入了一种自适应FFM，通过双se -注意来抑制冗余特征。在各种公开的裂缝数据集上进行了大量的实验，以验证CrackMambaNet的性能。结果表明，与现有的7个网络相比，CrackMambaNet在不同的数据集上取得了最好的性能。在DeepCrack、EdmCrack600和CFRL数据集上，平均相交率分别达到87.94%、76.33%和75.58%。结果表明，该算法能够平衡全局上下文建模和局部细节保存，在复杂场景和精细裂纹分割任务中表现出良好的鲁棒性和准确性。

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引用次数: 0

Developing a synchronised LoRa wireless sensor network for cost-effective footbridge vibration monitoring 开发具有成本效益的同步LoRa无线传感器网络，用于人行天桥振动监测

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-12-02 DOI: 10.1016/j.iintel.2025.100187

Huiyue Qiao, Hong Guan, Huaizhong Li, Adam Barbosa, Yong Zhu

Excessive vibrations in footbridges can cause pedestrian discomfort and accelerate structural degradation. Despite this, footbridges are monitored far less frequently than vehicle bridges, as conventional monitoring methods for large bridges are often too costly for smaller structures. Recent advances in the Internet of Things (IoT) and wireless sensor networks (WSNs) have enabled more affordable structural health monitoring (SHM) by eliminating the need for wired systems and reducing labour costs. However, existing WSN-based SHM systems typically rely on high data-rate protocols to transmit large volumes of raw data for centralised processing and modal analysis, resulting in high energy consumption at the sensor nodes. To address this limitation, this research proposes a cost-effective WSN tailored for footbridge monitoring and modal identification, integrating edge computing and low-power LoRa communication. The proposed WSN uses four synchronised LoRa slave sensor nodes to sample raw acceleration data and perform onboard processing periodically. Each node extracts peak frequency and phase information locally and transmits only the processed results to a master node connected to a laptop. Node synchronisation via peer-to-peer communication enables the identification of modal shapes based on phase differences between nodes. The proposed LoRa WSN was validated on both a laboratory-scale planar frame and an actual cable-stayed footbridge, successfully distinguishing in-phase and out-of-phase vibrations and enabling real-time monitoring.

行人桥的过度振动会引起行人不适，并加速结构的退化。尽管如此，人行桥的监测频率远低于车桥，因为传统的大型桥梁监测方法对于较小的结构来说往往过于昂贵。物联网（IoT）和无线传感器网络（wsn）的最新进展通过消除对有线系统的需求和降低劳动力成本，实现了更实惠的结构健康监测（SHM）。然而，现有的基于wsn的SHM系统通常依赖于高数据速率协议来传输大量原始数据进行集中处理和模态分析，导致传感器节点的高能耗。为了解决这一限制，本研究提出了一种适合人行天桥监测和模态识别的低成本WSN，集成了边缘计算和低功耗LoRa通信。该无线传感器网络采用4个同步的LoRa从传感器节点对原始加速度数据进行采样，并定期进行板载处理。每个节点在本地提取峰值频率和相位信息，并仅将处理后的结果传输到连接到笔记本电脑的主节点。通过点对点通信的节点同步可以根据节点之间的相位差识别模态形状。所提出的LoRa WSN在实验室规模的平面框架和实际斜拉桥上进行了验证，成功区分了同相和非同相振动，并实现了实时监测。

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引用次数: 0

Technologies and techniques in digital twins for real-time data visualisation in building maintenance: A state-of-the-art review 建筑维护中实时数据可视化的数字孪生技术和技术：最新的回顾

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-12-01 DOI: 10.1016/j.iintel.2025.100185

Muhammad Shahzad , Joseph H.M. Tah , Muhammad Younas , Avar Almukhtar

Digital Twins (DT) and Mixed Reality (MR) technologies are emerging as transformative solutions for automating processes and visualising real-time IoT sensor data in building operations and maintenance (O&M). However, current implementations predominantly rely on 2D or static 3D interfaces, limiting visualisation, immersion and data interaction. Though MR can improve data visualisation, its integration with DT technologies is still in its infancy in building maintenance. A systematic review of 55 academic studies provides a critical and in-depth analysis of existing research on DT applications for real-time monitoring and maintenance, visualisation tools, platforms and techniques, MR applications, and related challenges in building O&M. The review identifies a key gap: existing DT-MR frameworks lack sufficient scalability and standardisation required for broader adoption in building O&M. The paper proposes future research directions toward developing immersive DT-MR frameworks that enhance understanding of these technologies in enabling automated, interactive, and visually enriched building maintenance workflows.

数字孪生（DT）和混合现实（MR）技术正在成为建筑运营和维护（O&；M）中自动化流程和可视化实时物联网传感器数据的变革性解决方案。然而，目前的实现主要依赖于2D或静态3D界面，限制了可视化、沉浸和数据交互。虽然MR可以改善数据可视化，但它与DT技术的集成在建筑维护中仍处于起步阶段。本文对55项学术研究进行了系统回顾，对实时监控和维护的DT应用、可视化工具、平台和技术、MR应用以及构建o&m中的相关挑战的现有研究进行了批判性和深入的分析。审查确定了一个关键的差距：现有的DT-MR框架缺乏在构建o&&m中广泛采用所需的足够的可伸缩性和标准化。本文提出了未来的研究方向，即开发沉浸式DT-MR框架，以增强对这些技术的理解，从而实现自动化、交互式和视觉丰富的建筑维护工作流程。

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引用次数: 0

A demonstration of a digital twin framework for structural health monitoring: Application to bridge infrastructures 结构健康监测的数字孪生框架演示：在桥梁基础设施中的应用

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-11-14 DOI: 10.1016/j.iintel.2025.100184

Maryam Nasim , Abbas Rajabifard , Yiqun Chen , Bijan Samali

This study introduces a novel, multi-layered Digital Twin (DT) framework designed to enhance the resilience of ageing bridge infrastructure through real-time structural health monitoring (SHM) and data-driven decision support. The proposed framework integrates physics-based Finite Element Modelling (FEM), drone-based photogrammetry, and wireless sensor networks to construct a dynamic digital representation of the physical asset. By continuously synchronising sensor data with virtual models, the system establishes a foundation for predictive maintenance and lifecycle optimisation. Key innovations include a modular architecture that supports the seamless integration of diverse data sources, a closed-loop feedback mechanism for iterative model updating, and functionality for real-time anomaly detection. The proposed system supports proactive monitoring by enabling dynamic condition tracking, structural behaviour analysis, and long-term trend forecasting. The framework has been demonstrated on an operational railway truss bridge, where live vibration and environmental data were used to calibrate and validate the DT in a real-world setting. The results underscore the system's potential as a robust and scalable monitoring solution for historically significant and ageing transport assets. This work addresses critical limitations of conventional SHM approaches by offering a unified, data-centric strategy for infrastructure management. Beyond operational awareness, the proposed DT platform provides a strategic pathway toward more intelligent, more sustainable infrastructure systems prioritising resilience, informed maintenance planning, and future adaptability.

本研究引入了一种新颖的多层数字孪生（DT）框架，旨在通过实时结构健康监测（SHM）和数据驱动的决策支持来增强老化桥梁基础设施的复原力。提出的框架集成了基于物理的有限元建模（FEM），基于无人机的摄影测量和无线传感器网络，以构建物理资产的动态数字表示。通过不断同步传感器数据与虚拟模型，该系统为预测性维护和生命周期优化奠定了基础。关键的创新包括支持多种数据源无缝集成的模块化架构，迭代模型更新的闭环反馈机制，以及实时异常检测的功能。该系统通过动态状态跟踪、结构行为分析和长期趋势预测来支持主动监测。该框架已在实际运行的铁路桁架桥上进行了演示，现场振动和环境数据用于在实际环境中校准和验证DT。结果强调了该系统作为一个强大的、可扩展的监测解决方案的潜力，用于历史上重要的和老化的运输资产。这项工作通过为基础设施管理提供统一的、以数据为中心的策略，解决了传统SHM方法的关键局限性。除了运营意识之外，拟议的DT平台还为更智能、更可持续的基础设施系统提供了一条战略途径，优先考虑弹性、知情维护计划和未来适应性。

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引用次数: 0

Augmented reality-based smart structural health monitoring system with accurate 3D model alignment 基于增强现实的智能结构健康监测系统，具有精确的3D模型对齐

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-11-12 DOI: 10.1016/j.iintel.2025.100186

Omar Awadallah , Katarina Grolinger , Ayan Sadhu

Structural Health Monitoring (SHM) has become increasingly critical due to the rapid deterioration of civil infrastructure. Traditional methods involving heavy equipment are costly and time-consuming. Recent SHM approaches use advanced non-contact sensors, internet-of-things (IoT), and Augmented Reality (AR) glasses for faster inspections and immersive experiences during inspections. However, current methods lack quantitative damage data, remote collaboration support, and accurate 3D model alignment with the real structure. Recognizing these current challenges, this paper proposes an AR-based system that integrates Building Information Modelling (BIM) visualization and follows a flexible manipulation approach of 3D holograms to improve structural condition assessments. The proposed framework utilizes the Vuforia software development toolkit to enable the automatic alignment of 3D models to the real structure, ensuring successful model alignment to assist users in accurately visualizing damage locations. The framework also enables flexible manipulation of damage locations, making it easier for users to identify multiple damage points in the 3D models. The proposed system is validated through lab-scale and full-scale bridge use cases, with data transfer performance analyzed under 4G and 5G networks for remote collaboration. This study demonstrates that the proposed AR-based SHM framework successfully aligns 3D models with real structures, allowing users to manually adjust models and damage locations. The experimental results confirm its feasibility for remote collaborative inspections, highlighting significant improvements with 5G networks. Nevertheless, performance under 4G remains acceptable, ensuring reliability even without 5G coverage.

由于民用基础设施的迅速恶化，结构健康监测（SHM）变得越来越重要。使用重型设备的传统方法既昂贵又耗时。最近的SHM方法使用先进的非接触式传感器、物联网（IoT）和增强现实（AR）眼镜，以实现更快的检查和沉浸式检查体验。然而，目前的方法缺乏定量的损伤数据，远程协作支持，以及与真实结构精确的3D模型对齐。认识到这些当前的挑战，本文提出了一个基于ar的系统，该系统集成了建筑信息模型（BIM）可视化，并遵循3D全息图的灵活操作方法，以改善结构状况评估。该框架利用Vuforia软件开发工具包实现3D模型与真实结构的自动对齐，确保成功的模型对齐，以帮助用户准确地可视化损伤位置。该框架还可以灵活地操纵损伤位置，使用户更容易识别3D模型中的多个损伤点。该系统通过实验室规模和全尺寸桥用例进行了验证，并在4G和5G网络下分析了远程协作的数据传输性能。该研究表明，提出的基于ar的SHM框架成功地将3D模型与真实结构对齐，允许用户手动调整模型和损伤位置。实验结果证实了其远程协同检测的可行性，突出了5G网络的重大改进。尽管如此，4G下的性能仍然可以接受，即使没有5G覆盖也能确保可靠性。

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引用次数: 0

A resilience analysis framework for airport runways considering offensive-defensive strategies 考虑攻防策略的机场跑道弹性分析框架

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-11-01 DOI: 10.1016/j.iintel.2025.100183

Tie Yan , Yingjun Wang

The functionality evaluation of military infrastructure under attack determines the validity of operational command decisions in a critical way. In battlefield environments, conventional methodologies have been proven to be insufficient for accurate functionality evaluation due to challenges in considering multi-source uncertainties from adversarial strategies, weapon system variability, and environmental dynamics. To address this gap, an analysis framework for resilience of military infrastructure (AFRMI) was proposed, systematically integrating uncertainties in five phases, i.e. (1) offensive-defensive strategy, (2) weapon engagement, (3) physical damage, (4) functionality loss, and (5) functionality recovery. Focusing on airport runways in Air Defense and Missile Countermeasures scenarios, this study established a probabilistic methodology for quantifying multi-dimensional uncertainties as well as their cross-phase propagation mechanisms. The adversarial interactions between combatants were formulated as a Two-player Zero-sum Markov Game, where Nash Equilibrium strategies under resource constraints were derived through Q-learning reinforcement algorithms. A case study was conducted to demonstrate the application of AFRMI and revealed that it could quantifies the effect of equilibrium strategies to infrastructure functionality, as well as that of phase-coupled uncertainty propagation, overcoming the shortcoming of traditional static damage assessment by incorporating dynamic recovery pathways. The framework will provide a theoretical basis for real-time functionality state prediction under evolving adversarial conditions, and a decision-making paradigm for resilience management of military infrastructures.

受到攻击的军事基础设施的功能评估对作战指挥决策的有效性起着至关重要的作用。在战场环境中，由于考虑来自对抗策略、武器系统可变性和环境动力学的多源不确定性的挑战，传统方法已被证明不足以进行准确的功能评估。为了解决这一差距，提出了一个军事基础设施弹性分析框架（AFRMI），系统地整合了五个阶段的不确定性，即(1)攻防战略，(2)武器交战，(3)物理损坏，(4)功能损失和(5)功能恢复。以防空和导弹对抗场景下的机场跑道为研究对象，建立了一种量化多维不确定性及其跨相位传播机制的概率方法。将战斗人员之间的对抗性互动描述为二人零和马尔可夫博弈，其中通过q -学习强化算法推导出资源约束下的纳什均衡策略。结果表明，该方法可以量化平衡策略对基础设施功能的影响，以及相耦合不确定性传播的影响，克服了传统静态损伤评估方法的不足，引入了动态恢复路径。该框架将为不断变化的对抗条件下的实时功能状态预测提供理论基础，并为军事基础设施的弹性管理提供决策范式。

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引用次数: 0

Long-term monitoring of externally prestressed concrete bridges using semi-closed elasto-magnetic sensors 采用半封闭式弹磁传感器对外部预应力混凝土桥梁进行长期监测

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-10-25 DOI: 10.1016/j.iintel.2025.100182

Sai Wu

The effectiveness of external reinforcement significantly influences the durability and safety of bridges. During construction, prestress is typically applied and controlled using hydraulic jacks. However, long-term service conditions may lead to sectional deterioration or wire fractures in external tendons, which often remain undetected due to the absence of effective monitoring techniques. Since non-destructive methods should be applied to obtain the condition of external tendons for long-term monitoring, and considering the limitations of anchor tendon gauges and vibration-based methods, this research implemented a novel study for long-term monitoring and analysis of tendons in externally prestressed concrete bridges during operation. First, semi-closed elasto-magnetic sensors were deployed on two existing highway viaducts without removing the tendons; Second, prestress loss in bridges with different external prestressing configurations were analyzed using finite element method. Third, data collected over a 12-month period following bridge opening were analyzed under actual traffic loads and with consideration of temperature effects. The results indicate that the prestress values measured by the elasto-magnetic sensors correspond closely with theoretical predictions, demonstrating the suitability of this monitoring approach for practical bridge applications. Furthermore, no significant prestress loss was observed during the monitoring period.

外部加固的有效性对桥梁的耐久性和安全性有着重要的影响。在施工过程中，预应力通常使用液压千斤顶施加和控制。然而，由于缺乏有效的监测技术，长期的使用条件可能导致外部肌腱的截面恶化或钢丝断裂，而这些情况往往无法被发现。由于长期监测需要采用非破坏性的方法获取外筋状态，并且考虑到锚筋测量仪和基于振动的方法的局限性，本研究为外预应力混凝土桥梁运行过程中肌腱的长期监测和分析提供了一种新颖的研究方法。首先，将半封闭式弹磁传感器部署在两条现有的高速公路高架桥上，而不拆除其肌腱；其次，采用有限元法对不同外预应力结构下桥梁的预应力损失进行了分析。第三，在实际交通荷载和考虑温度影响的情况下，对桥梁开通后12个月内收集的数据进行了分析。结果表明，弹磁传感器测得的预应力值与理论预测值吻合较好，证明了该监测方法在实际桥梁应用中的适用性。此外，在监测期间没有观察到明显的预应力损失。

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引用次数: 0

Methodologies for functional recovery assessment of hilly roads situated in the Indian Himalayan region 印度喜马拉雅地区丘陵公路功能恢复评价方法

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-10-20 DOI: 10.1016/j.iintel.2025.100181

Rahul Kumar , Mahipal Kulariya , Sandip Kumar Saha

Road networks are a critical component of the built environment and play a vital role in enhancing overall community resilience. Ensuring the safety and functionality of these networks is essential, especially in hilly regions such as the Indian Himalayan Region (IHR), which is highly vulnerable to multiple hazards such as earthquakes and earthquake-induced landslides. Past events in this region have demonstrated that the disruptions in road connectivity during such events lead to significant socio-economic losses by restricting access to essential services, impeding trade, and affecting tourism-based livelihoods. Therefore, this study proposes methodologies to identify critical road segments and systematically estimate the expected functional recovery of hilly roads in the IHR. The approach integrates a rapid visual screening-based technique to preliminarily assess potentially vulnerable buildings, hill cuts, and slopes that may obstruct roads. In addition, it provides a framework for incorporating detailed probabilistic methods to enable a more refined estimation of road functionality recovery. The applicability of the proposed methodologies is illustrated through a detailed case study. The insights gained from this work can support the development of effective mitigation strategies and guide resource allocation to restore the functionality of roads affected by earthquakes and earthquake-induced landslides.

道路网络是建筑环境的重要组成部分，在增强整体社区复原力方面发挥着至关重要的作用。确保这些网络的安全和功能至关重要，特别是在印度喜马拉雅地区（IHR）等丘陵地区，这些地区极易受到地震和地震引发的山体滑坡等多重灾害的影响。过去在该地区发生的事件表明，此类事件期间道路连接中断会限制人们获得基本服务，阻碍贸易，影响以旅游业为基础的生计，从而导致重大的社会经济损失。因此，本研究提出了在《国际卫生条例》中确定关键路段并系统估计丘陵道路预期功能恢复的方法。该方法集成了一种基于快速视觉筛选的技术，可以初步评估潜在的易受伤害的建筑物、山丘切口和可能阻碍道路的斜坡。此外，它还提供了一个框架，用于合并详细的概率方法，以便对道路功能恢复进行更精确的估计。通过详细的案例研究说明了所提出方法的适用性。从这项工作中获得的见解可以支持制定有效的减灾战略，并指导资源分配，以恢复受地震和地震引起的滑坡影响的道路的功能。

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引用次数: 0

Iterative building condition assessment and prediction using unified 3D point clouds and a BIM model: A case study 使用统一的3D点云和BIM模型的迭代建筑状态评估和预测：案例研究

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-10-15 DOI: 10.1016/j.iintel.2025.100180

Sajith Wettewa, Ping Chai, Nilmini Weerasinghe, Lei Hou, Ruwini Edirisinghe, Guomin (Kevin) Zhang, Sujeeva Setunge

Visual building condition assessment is resource-intensive, particularly when inspections are repeated across large areas. This study seeks to reduce this burden by predicting the visual condition of surface-level architectural and structural elements and the latent risk in underlying elements using condition graphs. The proposed Scan-to-Condition prediction workflow builds on Scan-to-BIM and extends it to support ongoing inspection and prediction. This is demonstrated in a case study of an 818 m² mixed-use building in Melbourne that lacked prior BIM documentation. The workflow consisted of 4 components: (1) an optimised terrestrial laser scanning protocol for repeatable documentation and manageable data volumes, (2) a Scan-to-BIM modelling schema enriched with element-level confidence metrics and hidden-profile inference (3) a unified, multimodal condition documenting platform, and (4) an automated BIM-to-Graph Markup Language (GraphML) conversion method that generated graph representations for condition prediction using a custom Graph Attention Network (GAT) model. The workflow was designed as an iterative cycle in which baseline and follow-up point clouds, a semantically enriched BIM, and inspector reports were progressively integrated to form a longitudinal dataset. Future predictions are proposed to drive inspections and maintenance, progressively reducing manual human inspections over time.

视觉建筑状况评估是资源密集型的，特别是在大面积重复检查时。本研究试图通过使用状态图预测地表建筑和结构元素的视觉状况以及底层元素的潜在风险来减轻这种负担。提出的扫描到状态预测工作流程建立在扫描到bim的基础上，并将其扩展到支持正在进行的检查和预测。这在墨尔本一座818平方米的混合用途建筑的案例研究中得到了证明，该建筑缺乏先前的BIM文档。工作流由4个部分组成：(1)优化的地面激光扫描协议，用于可重复记录和可管理的数据量；(2)扫描到bim建模模式，丰富了元素级置信度指标和隐藏轮廓推理；(3)统一的多模态状态记录平台；(4)bim到图形标记语言（GraphML）自动转换方法，该方法使用自定义图形注意网络（GAT）模型生成用于状态预测的图形表示。工作流程被设计为一个迭代周期，其中基线和后续点云，语义丰富的BIM和检查员报告逐步集成，形成纵向数据集。未来的预测将推动检查和维护，随着时间的推移逐步减少人工检查。

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引用次数: 0