Journal of Infrastructure Intelligence and Resilience最新文献

英文中文

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

Evaluating data transmission performance of a UHF sensor for wireless temperature monitoring in mass concrete 一种用于大体积混凝土无线温度监测的超高频传感器的数据传输性能评估

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-09-19 DOI: 10.1016/j.iintel.2025.100172

Tai Ikumi , Ignasi Cairó , David Boix-Cots , Carlos Herrera-Mesén , Antonio Aguado , Albert de la Fuente

Embedded wireless sensors for mass concrete temperature monitoring face challenges in operating at representative depths due to signal attenuation, particularly before the concrete sets. This study evaluates a novel ultra-high frequency (UHF) wireless sensor for real-time temperature monitoring in mass concrete. Five large-scale concrete cubes with various mix designs were cast to assess the operational capabilities of the sensors at depths of up to 500 mm from the external surface and at distances of up to 30 m from the receiving antenna. The sensors effectively operated at depths up to 500 mm and distances up to 20 m, outperforming conventional Bluetooth sensors. Data transmission improved as the concrete hardened, with minimal long-term data loss after setting. Concrete consistency within tested ranges did not significantly impact performance. These results confirm that UHF-based sensors are a viable solution for thermal monitoring in mass concrete, eliminating the need for external probes.

由于信号衰减，特别是在混凝土凝固之前，用于大体积混凝土温度监测的嵌入式无线传感器在代表性深度下的操作面临挑战。本研究评估了一种用于大体积混凝土实时温度监测的新型超高频（UHF）无线传感器。铸造了五个具有不同混合设计的大型混凝土立方体，以评估传感器在距离外表面500毫米深度和距离接收天线30米距离的操作能力。该传感器可在500毫米的深度和20米的距离内有效工作，优于传统的蓝牙传感器。随着混凝土硬化，数据传输能力提高，凝结后的长期数据丢失最小。在测试范围内的混凝土一致性对性能没有显著影响。这些结果证实，基于uhf的传感器是大体积混凝土热监测的可行解决方案，无需外部探头。

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引用次数: 0

Partial transfer matrix-based group sparse regularisation for impact force localization and reconstruction 基于部分传递矩阵的群稀疏正则化求解冲击力局部化与重构

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-09-01 DOI: 10.1016/j.iintel.2025.100170

Bing Zhang, Xinqun Zhu, Zihao He, Jianchun Li

Existing methods for impact force identification are based on full transfer matrix. Constructing and using transfer matrices can be computationally intensive, especially for large-scale complex structures in practice. Partial transfer matrix refers to a subset of the full transfer matrix, potentially reducing computational cost and complexity. In this paper, a partial transfer matrix-based group sparse regularisation method is proposed for the impact force localization and reconstruction. Its robustness and adaptivity with respect to different subsets of full transfer matrix, noise level and number of impact forces are numerically studied using impact forces on a simply supported beam. The number of sensors for impact force identification can be significantly reduced by the proposed method and its localization and time history reconstruction can be determined even with one single sensor configuration. A 10 m long steel-concrete composite bridge model is built in the laboratory. The effectiveness of the proposed method for impact force identification is validated and compared with L₁-norm and L₂-norm regularisation methods numerically and experimentally. Results show that the proposed partial transfer matrix-based group sparse regularisation method has good robustness and identification accuracy and has better performance on the impact force localization and time history reconstruction comparing with L₁-norm and L₂-norm regularisation methods.

现有的冲击力识别方法是基于全传递矩阵的。构造和使用传递矩阵可能需要大量的计算量，特别是在实际中处理大型复杂结构时。部分转移矩阵是指完全转移矩阵的子集，可以潜在地降低计算成本和复杂性。本文提出了一种基于部分传递矩阵的群稀疏正则化方法，用于冲击力的局部化和重构。以简支梁的冲击力为例，研究了该方法对全传递矩阵的不同子集、噪声水平和冲击力数量的鲁棒性和自适应性。该方法可以显著减少用于冲击力识别的传感器数量，并且即使采用单一传感器配置也可以确定其定位和时程重建。在实验室中建立了一座10 m长的钢-混凝土组合桥模型。通过数值和实验验证了该方法的有效性，并与l1范数和l2范数正则化方法进行了比较。结果表明，与l1范数和l2范数正则化方法相比，本文提出的基于部分传递矩阵的群稀疏正则化方法具有较好的鲁棒性和识别精度，在冲击力局部化和时程重建方面具有更好的性能。

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引用次数: 0

Data normalization for the continuous monitoring of a steel truss bridge: A case study from the Italian railway line 钢桁架桥梁连续监测数据归一化：以意大利铁路线为例

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-09-01 DOI: 10.1016/j.iintel.2025.100171

Antonio Argentino, Luca Radicioni, Francesco Morgan Bono, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli

Structural health monitoring is recognized as a powerful tool to assist bridge management. Continuous long-term monitoring of bridge structures presents several challenges, including the need for effective system design, robust sensors deployment, efficient data management, and comprehensive data analysis and interpretation. In the field of operational modal analysis, automatic tracking of bridge frequencies over time has been shown to be significantly influenced by temperature fluctuations. This effect is also observed in low-frequency sampled signals. To address these issues, the authors present a double-step procedure to effectively mitigate the influence of temperature on the estimated modal parameters and raw signals from displacement, strain and rotation transducers. The procedure is based on multiple linear regression, taking the measured temperatures as inputs, followed by low-pass filtering operations applied to the residuals through moving averages, leading to the creation of minimum detectable anomaly curves. The latter allow to establish quantitative relationships between filtering window lengths and detectable damage thresholds at specified confidence levels. The case study involves a railway steel truss bridge, where more than a year of data was collected through a permanent monitoring system. The monitoring layout includes a variety of sensors deployed to measure the structural response, as well as environmental and operational variables. A 15-month dataset demonstrates how temperature compensation effectively reduces signal variability, which is crucial for enhancing early-stage anomalies detection.

结构健康监测被认为是辅助桥梁管理的有力工具。持续的长期监测桥梁结构提出了几个挑战，包括需要有效的系统设计，强大的传感器部署，高效的数据管理以及全面的数据分析和解释。在运行模态分析领域，电桥频率随时间的自动跟踪已被证明受温度波动的显著影响。在低频采样信号中也观察到这种效应。为了解决这些问题，作者提出了一种双步骤方法来有效地减轻温度对估计模态参数和来自位移、应变和旋转传感器的原始信号的影响。该过程基于多元线性回归，将测量温度作为输入，然后通过移动平均对残差进行低通滤波操作，从而创建最小可检测异常曲线。后者允许在特定置信水平上建立过滤窗口长度和可检测损害阈值之间的定量关系。该案例研究涉及一座铁路钢桁架桥，通过一个永久性监测系统收集了一年多的数据。监测布局包括各种传感器，用于测量结构响应，以及环境和操作变量。一个15个月的数据集展示了温度补偿如何有效地降低信号变异性，这对于增强早期异常检测至关重要。

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引用次数: 0

A combined approach for detection and localization of subsurface pipe leaks using ground microphone and GPR 一种利用地传声器和探地雷达联合探测和定位地下管道泄漏的方法

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-08-12 DOI: 10.1016/j.iintel.2025.100169

Zhihao Lin , Hai Liu , Xin Deng , Xu Meng , Jie Cui , Erol Tutumluer

Leaks in underground water pipelines not only lead to water wastage but can also cause serious public safety issues such as road collapse in urban areas. To address the low efficiency and accuracy of traditional pipeline leak monitoring and detection methods, this paper proposes a combined approach for detection and localization of subsurface pipeline leaks using ground microphones and ground penetrating radar (GPR). In the ground microphone method, loudness units referenced to digital full scale is used to identify leaky range along a pipeline. A GPR investigation is performed in the suspected area to detect the water-rich area and localize the leakage point. According to the depth and leakage volume of the pipeline, two strategies using the two methods are proposed for efficient and accurate detection and localization of buried pipeline leaks. Two case studies are conducted to validate the proposed approaches. The results indicate that the combined approach effectively leverages the advantages of ground microphones and GPR, enabling efficient and accurate detection and localization of buried pipeline leaks.

地下水管道的泄漏不仅会导致水资源的浪费，还会造成严重的公共安全问题，如城市地区的道路塌陷。针对传统管道泄漏监测检测方法效率低、精度低的问题，提出了一种利用地面传声器与探地雷达（GPR）相结合的地下管道泄漏检测与定位方法。在地面传声器方法中，参考数字满量程的响度单位用于识别管道沿线的泄漏范围。在可疑区域进行探地雷达调查，探测富水区域，定位泄漏点。根据管道的深度和泄漏量，提出了利用这两种方法对埋地管道泄漏进行高效、准确的检测和定位的策略。进行了两个案例研究来验证所提出的方法。结果表明，该方法有效地利用了地面传声器和探地雷达的优势，能够高效、准确地检测和定位埋地管道泄漏。

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引用次数: 0

Laser rangefinder and vision-based 3D structural displacement monitoring 基于激光测距和视觉的三维结构位移监测

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-07-21 DOI: 10.1016/j.iintel.2025.100158

Yihong Ou , Liang Su , Lujia Zhu , Qianli Ma , Haijian He , Fengwei Wu

Although the structural displacement monitoring methods based on computer vision and laser projection have been raised, they still suffer from drawbacks including that the relative orientation between the laser and the receiving board must be known and the out-of-plane displacement is lost. This paper proposes a laser rangefinder and vision-based method for monitoring 3D structural displacement. The method uses computer vision techniques to extract pixel coordinates of the laser spot and utilizes the circular grid pattern on the receiving board to recover its coordinates in physical domain. Several known displacements are applied to the laser rangefinder to solve for the unknown system parameters using numerical optimization. Two laboratory experiments were conducted to prove the accuracy of the method, in which the maximum error of estimated displacements was less than 1 mm. A low-cost prototype was also developed and validated, showing the feasibility and effectiveness of the proposed method in practical applications.

虽然基于计算机视觉和激光投影的结构位移监测方法已经提出，但它们仍然存在必须知道激光与接收板之间的相对方向和丢失面外位移的缺点。提出了一种基于激光测距仪和视觉的三维结构位移监测方法。该方法利用计算机视觉技术提取激光光斑的像素坐标，并利用接收板上的圆形网格图案恢复其物理域坐标。将几个已知的位移应用到激光测距仪中，对未知的系统参数进行数值优化求解。通过两次室内实验证明了该方法的准确性，其中估计位移的最大误差小于1 mm。开发并验证了一个低成本的原型，验证了该方法在实际应用中的可行性和有效性。

引用次数: 0

AI-powered automatic design of fire sprinkler layout for random building floorplans 人工智能自动设计随机建筑平面图的消防喷头布局

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-07-21 DOI: 10.1016/j.iintel.2025.100167

Yanfu Zeng , Xinyi Liu , Yifei Ding , Zhe Zheng , Tianhang Zhang , Xinyan Huang , Xinzheng Lu

Fire sprinkler system is a commonly designed safety provision in modern buildings, yet the current manual drawing preparation process is burdened by time-consuming tasks, heavy workloads, and human errors. This study introduces an intelligent framework aimed at automating the drawing preparation process for fire sprinkler layout. A database of 120 sprinkler design drawings was compiled to train a pix2pixHD generative adversarial network (GAN). After training, the GAN model can generate sprinkler placement with a protection coverage of 99.5% for new and random architectural floorplans. Apart from ensuring code-compliant design, the total number of sprinklers designed by GAN is 13% lower than those arranged by professional engineers. By adopting this intelligent method, the time needed for design drawing preparation can be saved by 76%, and the cost-benefit of the sprinkler design can be improved by using reasonable fewer sprinklers.

消防喷淋系统是现代建筑中常见的安全设施，但目前手工绘制图纸的过程耗时长，工作量大，容易出现人为错误。本文介绍了一种智能框架，旨在实现消防喷头布置图编制过程的自动化。建立了一个包含120张喷淋设计图纸的数据库，用于训练一个pix2pixHD生成对抗网络（GAN）。经过训练，GAN模型可以为新的和随机的建筑平面图生成保护覆盖率为99.5%的喷头布置。除了确保符合规范的设计外，GAN设计的喷头总数比专业工程师布置的少13%。采用这种智能方法，设计图纸的编制时间可节省76%，合理减少喷头的使用，提高喷头设计的成本效益。

引用次数: 0

Cascading failures in urban infrastructure systems: A comprehensive review of disaster chain mechanisms 城市基础设施系统的级联故障：灾害链机制的综合综述

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-06-03 DOI: 10.1016/j.iintel.2025.100157

Zheng Lu , Deyu Yan , Huanjun Jiang , Hongjing Xue , Zhao-Dong Xu

Urban engineering systems (UESs) are highly interconnected, forming complex dependencies that render them vulnerable to cascading failures during disasters. While existing studies have explored specific aspects of disaster chains in UESs, a synthesized framework for understanding their interdependencies, data acquisition challenges, and methodological limitations remains underdeveloped. This paper addresses this gap by conducting a systematic review of UES disaster chains, beginning with the definitions of disaster chains from different academic perspectives, common types of urban disaster chains, namely earthquake, flood, fire, freezing and ground subsidence disaster chains, as well as the interdependency of UES. Furthermore, three identification methods of disaster chains are summarized, namely based on historical disaster data, expert experience, and natural language processing (NLP). Moreover, five analysis methods of disaster chains are summarized, including those based on Bayesian networks, complex networks, numerical simulation, scenario simulation and remote sensing, with comparison of their applicability, advantages, limitations and complexity. The benefits and drawbacks of each approach are clearly illustrated. The paper concludes by discussing the limitations in the current literature and suggests that future research may utilize new technologies to facilitate data analyzing process, conduct cross-regional studies, and focus on integrating socio-economic factors for disaster-related decision-making support.

城市工程系统（UESs）是高度互联的，形成了复杂的依赖关系，使它们在灾难期间容易发生级联故障。虽然现有的研究已经探索了UESs灾害链的具体方面，但了解其相互依赖性、数据获取挑战和方法局限性的综合框架仍然不发达。本文从不同学术视角对灾害链的定义、城市灾害链的常见类型，即地震、洪水、火灾、冰冻和地面沉降灾害链，以及城市灾害链之间的相互依存关系等方面对UES灾害链进行了系统回顾，以弥补这一空白。总结了基于历史灾害数据、专家经验和自然语言处理（NLP）的三种灾害链识别方法。总结了基于贝叶斯网络、复杂网络、数值模拟、场景模拟和遥感的五种灾害链分析方法，比较了它们的适用性、优势、局限性和复杂性。每种方法的优点和缺点都清楚地说明了。本文最后讨论了现有文献的局限性，并建议未来的研究可以利用新技术来促进数据分析过程，进行跨区域研究，并将重点放在整合社会经济因素以支持灾害相关决策。

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引用次数: 0

Corrigendum to “Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function” [J. Infrastruct. Intell. Resilience, 2 (2025) 100135] “利用weierstrass mandelbrot函数的多变量台风风速分形数值模拟”的勘误[J]。Infrastruct。智能。弹性，2 (2025)100135]

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-06-01 DOI: 10.1016/j.iintel.2025.100156

Kang Cai , Mingfeng Huang , Qiang Li , Qing Wang , Yi-Qing Ni

引用次数: 0

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