Journal of Infrastructure Intelligence and Resilience最新文献

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A survey of generative models for image-based structural health monitoring in civil infrastructure 基于图像的民用基础设施结构健康监测生成模型综述

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100138

Gi-Hun Gwon, Hyung-Jo Jung

Accurately assessing and monitoring the condition of structures is essential for ensuring the safety and integrity of civil infrastructure. Over the past decade, image-based structural health monitoring technologies have emerged as powerful tools to enhance efficiency and improve the objectivity of structural evaluations. The integration of deep learning technologies with these monitoring systems has significantly improved the efficiency and reliability of structural condition diagnostics. Of particular interest are specifically Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models, which have gained increasing attention due to their versatility in data generation and ability to address fundamental challenges in structural monitoring. While image-based structural health monitoring encompasses both damage detection and structural response measurements, this review primarily focuses on local-level monitoring applications such as damage detection, where generative models have demonstrated particular effectiveness in addressing challenges like limited data availability and environmental variations. This paper provides a comprehensive analysis of these generative models, examining their underlying concepts, mechanisms, and applications in image-based structural health monitoring. Key applications are reviewed, including structural damage detection, data augmentation for training, and emerging areas such as image quality enhancement and domain generalization. Our analysis presents the current state of generative models in structural monitoring, identifying critical challenges and promising future research directions. This systematic review serves as a foundational resource for researchers and practitioners in the field, offering insights into current achievements and potential advancements.

准确评估和监测结构状况对于确保民用基础设施的安全性和完整性至关重要。在过去十年中，基于图像的结构健康监测技术已成为提高效率和改善结构评估客观性的有力工具。深度学习技术与这些监测系统的整合极大地提高了结构状态诊断的效率和可靠性。特别值得关注的是变异自动编码器、生成式对抗网络和扩散模型，由于它们在数据生成方面的多功能性和应对结构监测中基本挑战的能力，这些技术越来越受到关注。虽然基于图像的结构健康监测包括损伤检测和结构响应测量，但本综述主要关注损伤检测等局部级监测应用，在这些应用中，生成模型在应对有限数据可用性和环境变化等挑战方面表现出了特殊的有效性。本文全面分析了这些生成模型，研究了它们的基本概念、机制以及在基于图像的结构健康监测中的应用。本文回顾了关键应用，包括结构损伤检测、用于训练的数据增强，以及图像质量增强和领域泛化等新兴领域。我们的分析介绍了结构监测中生成模型的现状，确定了关键挑战和有前景的未来研究方向。这篇系统综述为该领域的研究人员和从业人员提供了基础资源，让他们深入了解当前的成就和潜在的进步。

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引用次数: 0

Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete 物理训练的人工智能框架，以检测氯化物引起的混凝土降解

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100139

Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu

Numerous critical infrastructures in the United States, including bridges, dams, and nuclear plants, are aging and prone to concrete degradation, compromising their performance and structural integrity. One of the leading causes of degradation is chloride-induced corrosion, where chloride ions diffuse into the concrete, leading to reinforcement corrosion, spalling, and cracking. Detecting chloride degradation at an early stage is crucial for ensuring the safety of these vital structures. However, the visible signs of degradation, such as spalling and cracking, often appear only after significant damage has occurred. Degradation occurs gradually over many years, making it impractical to collect real-time non-destructive testing (NDT) data over extended periods while allowing the structure to continue deteriorating. To overcome this challenge, an integrated structural health monitoring framework is proposed that combines advanced finite element modeling, sensor data, and deep learning techniques. This framework follows a multi-step approach to simulate chloride degradation over the service life of the structure. Subsequently, finite element analyses are performed to numerically simulate non-destructive testing at various stages of degradation to generate corresponding sensor data. By leveraging these simulated data and insights, a physics-driven artificial intelligence framework is developed. The proposed framework offers a state-of-the-art solution to mitigate the challenges associated with long-term degradation monitoring by utilizing high-fidelity simulations and data-driven techniques to achieve detection of chloride-induced concrete damage.

美国的许多关键基础设施，包括桥梁、水坝和核电站，都在老化，容易出现混凝土退化，影响了它们的性能和结构完整性。腐蚀的主要原因之一是氯化物引起的腐蚀，氯离子扩散到混凝土中，导致钢筋腐蚀、剥落和开裂。在早期阶段检测氯化物降解对于确保这些重要结构的安全至关重要。然而，可见的退化迹象，如剥落和开裂，往往只出现在重大损害发生后。在许多年的时间里，老化是逐渐发生的，这使得在长时间内收集实时无损检测（NDT）数据变得不切实际，同时允许结构继续恶化。为了克服这一挑战，提出了一种综合结构健康监测框架，该框架结合了先进的有限元建模、传感器数据和深度学习技术。该框架遵循多步骤方法来模拟结构在使用寿命期间的氯化物降解。随后，进行有限元分析，数值模拟不同退化阶段的无损检测，生成相应的传感器数据。通过利用这些模拟数据和见解，开发了一个物理驱动的人工智能框架。提出的框架提供了一种最先进的解决方案，通过利用高保真模拟和数据驱动技术来实现氯化物引起的混凝土损伤检测，缓解了与长期退化监测相关的挑战。

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引用次数: 0

Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions 基于贝叶斯混合因子的结构损伤检测

Journal of Infrastructure Intelligence and Resilience

Pub Date : 2025-01-06 DOI: 10.1016/j.iintel.2025.100140

Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue

Variations of structural dynamic parameters (e.g., frequencies and damping ratios) can be caused by potential structural damages and environmental effects (e.g., temperature, humidity). It is of critical importance to distinguish them for a reliable vibration-based damage detection. A variational Bayesian mixture of factor analyzers (VB-MFA) is proposed in this paper for the probabilistic modeling of measured natural frequencies. It contains multiple factor analyzers to accommodate the nonlinear effect of environmental factors on the natural frequencies. The variational Bayes with automatic relevance determination prior empowers it to automatically determine the number of analyzers and the dimension of latent factors in each analyzer. In addition, the predictive marginal likelihood of natural frequencies is proposed as a damage index, which naturally considers the uncertainties in latent factors and estimated parameters. The method is verified in two case studies: a laboratory eight-story shear-type building model and the Z24-Bridge, both subjected to temperature variations. It shows that better performance has been achieved comparing to the conventional factor analysis and mixture of factor analyzers. The VB-MFA is capable to model the nonlinear effect of environmental effect on natural frequencies, and improves the accuracy of vibration-based structural damage detection.

结构动态参数（如频率和阻尼比）的变化可能由潜在的结构损坏和环境影响（如温度、湿度）引起。区分它们对于可靠的基于振动的损伤检测至关重要。本文提出了一种变分贝叶斯混合因子分析法（VB-MFA），用于测量固有频率的概率建模。它包含多因素分析仪，以适应环境因素对固有频率的非线性影响。具有自动关联确定先验的变分贝叶斯能够自动确定分析器的数量和每个分析器中潜在因素的维度。此外，提出了固有频率的预测边际似然作为损伤指标，自然地考虑了潜在因素和估计参数的不确定性。该方法在两个案例研究中得到验证：一个实验室八层剪切式建筑模型和z24桥，两者都受到温度变化的影响。结果表明，与传统因子分析和混合因子分析相比，该方法取得了更好的性能。VB-MFA能够模拟环境对固有频率的非线性影响，提高基于振动的结构损伤检测的精度。

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