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Post-earthquake preliminary estimation of residual seismic capacity of RC buildings based on deep learning 基于深度学习的钢筋混凝土建筑震后剩余抗震能力初步估算
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-05-18 DOI: 10.1007/s13349-024-00805-w
Ting-Yu Hsu, Ching-Feng Wu, Tsung-Chih Chiou

Preliminary assessment of the seismic performance of reinforced concrete (RC) buildings with placards can reduce the number of buildings that require a detailed and costly assessment. Although existing image-processing-based techniques can detect the existence of cracks and spalling in concrete, it remains difficult to define with damage levels of the damaged vertical members based on these techniques. This study aims to fill this gap by exploiting convolutional neural network (CNN) techniques for damage level classification of vertical components in RC buildings. The preliminary seismic assessment approach of existing RC buildings developed by the National Center for Research on Earthquake Engineering, Taiwan is employed in this study, and the residual strength factors for damage levels of vertical members are identified. The proposed CNN technique can estimate the damage levels of the vertical members, and the seismic capacity reduction of these damaged vertical members can be graded accordingly. Hence, the seismic resistance of the RC buildings with damaged members caused by an earthquake can be estimated. The earthquake reconnaissance data collected after recent earthquakes are used to train and validate the CNN network. The performance of the proposed approach is verified using the earthquake data with the necessary information for the preliminary seismic assessment approach. In general, the precision and recall values that we obtain for the identification of the damage in vertical members are acceptable. Based on the results of this study, performing a seismic evaluation of RC buildings by calculating the residual seismic capacity ratio with the help of machine learning appears to be an effective strategy.

使用标牌对钢筋混凝土(RC)建筑的抗震性能进行初步评估,可以减少需要进行详细和昂贵评估的建筑数量。虽然现有的基于图像处理的技术可以检测混凝土中是否存在裂缝和剥落,但仍很难根据这些技术确定受损垂直构件的损坏等级。本研究旨在利用卷积神经网络(CNN)技术对钢筋混凝土建筑中的竖向构件进行损伤等级分类,以填补这一空白。本研究采用了由台湾国家地震工程研究中心开发的既有钢筋混凝土建筑抗震初步评估方法,并确定了竖向构件损伤等级的剩余强度系数。该 CNN 技术可估算出竖向构件的破坏等级,并对这些破坏等级的竖向构件的抗震能力降低程度进行分级。因此,可以估算因地震而受损的钢筋混凝土建筑的抗震能力。最近地震后收集的地震勘测数据被用来训练和验证 CNN 网络。利用地震数据和初步地震评估方法所需的信息,验证了所提出方法的性能。总体而言,我们在垂直构件损坏识别方面获得的精确度和召回值是可以接受的。根据这项研究的结果,在机器学习的帮助下,通过计算剩余抗震能力比来对 RC 建筑进行抗震评估似乎是一种有效的策略。
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引用次数: 0
Damage identification of non-dispersible underwater concrete columns under compression using impedance technique and stress-wave propagation 利用阻抗技术和应力波传播识别受压非分散水下混凝土柱的损伤
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-05-14 DOI: 10.1007/s13349-024-00802-z
Shenglan Ma, Shurong Ren, Chen Wu, Shaofei Jiang, Weijie Huang

Current scouring effects and additives increase the risk of failure in underwater structures, and poor observation complicates the identification and assessment of damage. We present a novel index for assessing non-dispersible underwater concrete columns using stress-wave and impedance. A piezoelectric lead zirconate titanate sensor was used to monitor the compression process of non-dispersible underwater concrete columns and ascertain the extent of damage. The proposed index divides the damage process into initial compaction, elastic deformation, and crack development and failure stages. Additionally, the proposed method quantifies and identifies damage, producing results that agree with those for the axial compression failure characteristics.

水流冲刷效应和添加剂增加了水下结构失效的风险,而观察不力则使损坏的识别和评估变得更加复杂。我们提出了一种利用应力波和阻抗评估非分散水下混凝土柱的新指标。我们使用压电式锆钛酸铅传感器监测非分散性水下混凝土柱的压缩过程,并确定损坏程度。所提出的指标将破坏过程分为初始压实、弹性变形、裂缝发展和破坏阶段。此外,建议的方法还能量化和识别损坏,其结果与轴向压缩破坏特征的结果一致。
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引用次数: 0
Ensemble learning-based structural health monitoring of a bridge using an interferometric radar system 利用干涉雷达系统对桥梁进行基于集合学习的结构健康监测
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-30 DOI: 10.1007/s13349-024-00789-7
Ali Yaghoubzadehfard, Elisa Lumantarna, Nilupa Herath, Massoud Sofi, Mehmet Rad

Due to the increase in population, urbanisation, transportation development, and the aging of existing bridges, there is a growing need for new and rapid structural health monitoring (SHM) of bridges. To address this challenge, a method that stands out is the use of an interferometric radar system-based device, specifically Image by Interferometric Survey-Frequency for structures (IBIS-FS). Known for its portability and non-intrusive operation, IBIS-FS does not require direct contact with the bridge. This study utilised IBIS-FS to capture a pedestrian bridge’s natural frequencies and mode shapes. The data obtained were found to be consistent with results from finite element models, demonstrating the reliability of IBIS-FS in capturing modal parameters. Building upon this foundation, the study then explores the application of advanced ensemble-based machine-learning techniques. By leveraging the data acquired from IBIS-FS, algorithms such as Random Forest, Gradient-boosted Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost) are used for bridge damage detection. These machine-learning (ML) techniques are suited to analyse the incomplete modal parameters of bridges, as captured by IBIS-FS. The study focuses on using these algorithms to interpret the changes in modal parameters, specifically identifying damage as a reduction in the stiffness of elements. This approach allows for a comprehensive analysis, where the modal parameters, including mode shapes and natural frequencies altered by varying noise levels, are fed as input to the models. It was observed that all three ML methods, with Random Forest in particular, can effectively identify the location and severity of damage, demonstrating an efficient training process. The robustness of GBDT and XGBoost in handling complex data sets also shows great promise for their application in bridge damage detection. Collectively, these results underscore the potential of combining advanced ML techniques like Random Forest, GBDT, and XGBoost with the data acquired from IBIS-FS.

由于人口增加、城市化、交通发展以及现有桥梁的老化,对新型快速桥梁结构健康监测(SHM)的需求日益增长。为应对这一挑战,一种突出的方法是使用基于干涉雷达系统的设备,特别是用于结构的干涉测量频率图像(IBIS-FS)。IBIS-FS 以其便携性和非侵入式操作而著称,不需要与桥梁直接接触。本研究利用 IBIS-FS 捕获了一座人行天桥的固有频率和模态振型。获得的数据与有限元模型的结果一致,证明了 IBIS-FS 在捕捉模态参数方面的可靠性。在此基础上,该研究探索了基于集合的先进机器学习技术的应用。通过利用从 IBIS-FS 获取的数据,随机森林、梯度提升决策树 (GBDT) 和极端梯度提升 (XGBoost) 等算法被用于桥梁损伤检测。这些机器学习 (ML) 技术适用于分析 IBIS-FS 获取的桥梁不完整模态参数。研究重点是使用这些算法来解释模态参数的变化,特别是将损伤识别为元素刚度的降低。这种方法可以进行综合分析,将模态参数(包括模态振型和因噪声水平变化而改变的固有频率)作为输入输入到模型中。据观察,所有三种 ML 方法,尤其是随机森林方法,都能有效识别损坏的位置和严重程度,证明了训练过程的高效性。GBDT 和 XGBoost 在处理复杂数据集时的鲁棒性也为其在桥梁损坏检测中的应用提供了巨大的前景。总之,这些结果凸显了将随机森林、GBDT 和 XGBoost 等先进的 ML 技术与从 IBIS-FS 获取的数据相结合的潜力。
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引用次数: 0
Post-event evaluation of residual capacity of building structures based on seismic monitoring 基于地震监测的建筑结构剩余承载力灾后评估
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-28 DOI: 10.1007/s13349-024-00803-y
Luji Wang, Jiazeng Shan

Structural capacity evaluation is essential to support the safety assessment and decision-making process of existing building structures after disastrous earthquakes. Current post-earthquake evaluation practices rely more on manual on-site inspections, which are labor-intensive and subjective. A simulation-based capacity evaluation could be a desired alternative when numerical models for these buildings are prior-identified and updated using structural health monitoring data. This study proposes a procedure for identifying the capacity curve and assessing the residual capacity of existing structures using seismic monitoring data. The mass-normalized spectral acceleration-displacement (AD format) relation is first defined in a single-degree-of-freedom system. Considering the post-event deterioration of structural capacity, a data-driven reduction factor for the capacity curve is introduced to quantify the potential structural degradation. With the aid of the updated capacity curve, the residual capacity of the earthquake-damaged structure is then predicted via incremental dynamic analysis. The feasibility and accuracy of the proposed method are analyzed via numerical simulations and further validated using a large-scale shaking table test and a real-world instrumented building. Results show that the proposed method could identify the capacity curve of the existing structure from seismic monitoring data and estimate the hysteresis responses with a favorable agreement. It could provide the residual capacity of the target structure and quantify its capacity reduction, which can informatively facilitate the post-earthquake structural safety management.

结构承载力评估对于支持灾难性地震后现有建筑结构的安全评估和决策过程至关重要。目前的震后评估方法更多地依赖于人工现场检查,这是一种劳动密集型的主观检查方法。如果事先利用结构健康监测数据对这些建筑物的数值模型进行识别和更新,那么基于模拟的承载能力评估可能是一种理想的替代方法。本研究提出了一种利用地震监测数据识别承载力曲线和评估现有结构剩余承载力的程序。首先在单自由度系统中定义质量归一化频谱加速度-位移(AD 格式)关系。考虑到震后结构承载力的退化,引入了数据驱动的承载力曲线折减系数,以量化潜在的结构退化。然后,借助更新的承载力曲线,通过增量动态分析预测地震破坏结构的剩余承载力。我们通过数值模拟分析了所提方法的可行性和准确性,并利用大型振动台试验和真实世界中的仪器建筑物进一步验证了该方法的可行性和准确性。结果表明,所提出的方法可以从地震监测数据中识别出现有结构的承载力曲线,并估算出滞后响应,两者的一致性较好。该方法可提供目标结构的剩余承载力,并量化其承载力下降情况,从而为震后结构安全管理提供信息。
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引用次数: 0
Study on the shear mechanical response and failure characteristics of prefabricated double-cabin utility tunnel joints 预制双舱公用事业隧道接头的剪切机械响应和失效特征研究
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-26 DOI: 10.1007/s13349-024-00806-9
Chao Zhang, Zhengrong Zhao, Youjun Xu, Xuzhi Nie

Longitudinal joints are the most vulnerable parts of prefabricated utility tunnels, susceptible to damage from external forces and foundation settlement. Currently, the shear mechanical properties of prefabricated double-cabin utility tunnel joints are unclear, preventing the evaluation of the structural or joint safety of utility tunnels. The shear mechanical response and failure characteristics of the joints of prefabricated double-cabin utility tunnels are investigated by combining model testing with numerical simulation. The results indicate that the shear deformation of utility tunnel joints can be categorized into elastic, crack propagation, and damage stages. In the course of joint-shear deformation, the middle utility tunnel sustains centrosymmetric failure. The degree of deformation of the large cabin is greater than that of the small cabin, while the damage to the small cabin is more severe. When the utility tunnel is subjected to the same load, the joint dislocation under the gravelly sand foundation is the smallest, but the damage range is the largest and the cracks are the most. Local strengthening and protection are needed at the chamfer, near the bolt hole, and the top and bottom. The stratum conditions have little effect on the shear stiffness of the joint during the elastic stage, but they have a significant impact during the crack propagation and damage stages. Finally, the joint damage area is approximately 15% of the total utility tunnel, and the deformation region of the longitudinal connectors is approximately 16% of its length.

纵向接缝是预制水电隧道中最脆弱的部分,容易受到外力和地基沉降的破坏。目前,预制双舱水电隧道接头的剪切力学性能尚不明确,无法对水电隧道的结构或接头安全性进行评估。本文通过模型试验和数值模拟相结合的方法,研究了预制双舱水电隧道接头的剪切力学响应和破坏特征。结果表明,水电隧道接头的剪切变形可分为弹性阶段、裂缝扩展阶段和破坏阶段。在接缝剪切变形过程中,中间公用设施隧道发生中心对称破坏。大舱室的变形程度大于小舱室,而小舱室的损坏更为严重。当水电隧道承受相同荷载时,砾砂地基下的接缝错位最小,但破坏范围最大,裂缝最多。在倒角处、螺栓孔附近以及顶部和底部需要进行局部加固和保护。地层条件在弹性阶段对接缝的剪切刚度影响不大,但在裂缝扩展和破坏阶段影响很大。最后,接头损坏区域约占整个实用隧道的 15%,纵向连接件的变形区域约占其长度的 16%。
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引用次数: 0
Complex background segmentation for noncontact cable vibration frequency estimation using semantic segmentation and complexity pursuit algorithm 利用语义分割和复杂性追求算法进行复杂背景分割,以估算非接触式电缆振动频率
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-18 DOI: 10.1007/s13349-024-00798-6
Tianyong Jiang, Chunjun Hu, Lingyun Li

This paper proposes a new complex background segmentation method based on the modified fully convolutional network semantic segmentation for noncontact cable vibration frequency estimation. The estimation of frequency from video data is challenged by the presence of background object motion, which directly impacts the accuracy of the video-based method. To address this issue, image tests were carried out among the existing model (U2-Net) to explore the effect of the efficient channel attention (ECA) and convolutional block attention module (CBAM) on cable segmentation performance. As a result, a relative optimal model was identified. This modified model was then used to remove the complex background, while retaining the vibration signals specific to the cable. Subsequently, phase matrices encoding cable vibration were calculated using a phase-based motion estimation algorithm at various cable locations. The modal response of the cable vibration was estimated using the complexity pursuit (CP) algorithm from the segmented video. Finally, the vibration frequency of the cable was estimated. The proposed method was validated on a small-scale cable model. The results are in good agreement with the values sampled by the accelerometer, with an average relative error of 4.50%. This estimation shows the significant potential of the proposed method in structural health monitoring.

本文提出了一种基于修正的全卷积网络语义分割的新型复杂背景分割方法,用于非接触式电缆振动频率估算。从视频数据中估算频率面临着背景物体运动的挑战,这直接影响了基于视频方法的准确性。为解决这一问题,我们对现有模型(U2-Net)进行了图像测试,以探索高效通道注意(ECA)和卷积块注意模块(CBAM)对电缆分割性能的影响。结果,确定了一个相对最优的模型。修改后的模型用于去除复杂背景,同时保留电缆特有的振动信号。随后,使用基于相位的运动估算算法计算了不同电缆位置的电缆振动编码相位矩阵。使用复杂性追寻 (CP) 算法从分割的视频中估算出电缆振动的模态响应。最后,估算出电缆的振动频率。所提出的方法在一个小型电缆模型上进行了验证。结果与加速度计的采样值十分吻合,平均相对误差为 4.50%。这一估算结果表明,所提出的方法在结构健康监测方面具有巨大潜力。
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引用次数: 0
Determination of future creep and seismic behaviors of dams using 3D analyses validated by long-term levelling measurements 利用经长期水准测量验证的三维分析确定大坝未来的蠕变和地震行为
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-16 DOI: 10.1007/s13349-024-00799-5
Murat Cavuslu, Samed Inyurt

This study aims to assess the future structural performance of the Kozlu-Ulutan clay core rockfill (CCR) dam, one of the most significant water structures in the Black Sea region of Turkey, by utilizing 35 years of levelling measurements and 3D finite-difference analyses. Settlement measurements were obtained from five different points on the dam surface every 6 months. Subsequently, a three-dimensional (3D) model of the dam was created using the finite-difference method. Time-dependent creep analyses and seismic analyses were conducted sequentially, employing the Burger-Creep and Mohr–Coulomb material models, respectively. Non-reflecting boundary conditions were defined for the boundaries of the dam model. The 3D numerical analysis results were found to be highly compatible with the 35 years of levelling measurements. Additionally, the future seepage and settlement behaviors of the dam over a 100-year period (2023–2123) were analyzed, considering special time functions. Current and future seismic analyses were performed, taking into account the settlement results of the dam in 2023 and 2123. For seismic analyses, data from ten various earthquakes that occurred in Kahramanmaraş, Hatay, Malatya, and Gaziantep in Turkey in 2023 were utilized. The seismic analysis results provided significant information about the future seismic behavior of the Kozlu-Ulutan Dam, revealing notable differences between the current and future earthquake behaviors of the dam. Moreover, it was concluded that the clay core is the most crucial section concerning the current and future seismic behaviors of CCR dams. The study results emphasized the importance of continuous monitoring and periodic seismic evaluations for the safe operation of CCR dams.

该研究旨在利用 35 年的水准测量和三维有限差分分析,评估土耳其黑海地区最重要的水利工程之一--科兹鲁-乌卢坦粘土岩心填筑(CCR)大坝未来的结构性能。每 6 个月对大坝表面的 5 个不同点进行一次沉降测量。随后,使用有限差分法创建了大坝的三维(3D)模型。分别采用 Burger-Creep 和 Mohr-Coulomb 材料模型,依次进行了随时间变化的蠕变分析和地震分析。为大坝模型的边界定义了非反射边界条件。三维数值分析结果与 35 年的水准测量结果高度吻合。此外,考虑到特殊的时间函数,还对大坝未来 100 年(2023-2123 年)的渗流和沉降行为进行了分析。考虑到大坝在 2023 年和 2123 年的沉降结果,还进行了当前和未来的地震分析。地震分析采用了 2023 年在土耳其卡赫拉曼马拉什、哈塔伊、马拉蒂亚和加济安泰普发生的 10 次不同地震的数据。地震分析结果为 Kozlu-Ulutan 大坝未来的地震行为提供了重要信息,揭示了大坝当前地震行为与未来地震行为之间的显著差异。此外,研究还得出结论,粘土心是影响 CCR 大坝当前和未来地震行为的最关键部分。研究结果强调了持续监测和定期地震评估对 CCR 大坝安全运行的重要性。
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引用次数: 0
Unsupervised transfer learning for structural health monitoring of urban pedestrian bridges 用于城市人行天桥结构健康监测的无监督迁移学习
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-14 DOI: 10.1007/s13349-024-00786-w
Giulia Marasco, Ionut Moldovan, Eloi Figueiredo, Bernardino Chiaia

Bridge authorities have been reticent to integrate structural health monitoring into their bridge management systems, as they do not have the financial and technical resources to collect long-term monitoring data from every bridge. As bridge authorities normally own huge amount of similar bridges, like the pedestrian ones, the ability to transfer knowledge from one or a small group of well-known bridges to help make more effective decisions in new bridges and environments has gained relevance. In that sense, transfer learning, a subfield of machine learning, offers a novel solution to periodically evaluate the structural condition of all pedestrian bridges using long-term monitoring data from one or more pedestrian bridges. In this paper, the applicability of unsupervised transfer learning is firstly shown on data from numerical models and then on data from two similar pedestrian prestressed concrete bridges. Two domain adaptation techniques are used for transfer learning, where a classifier has access to unlabeled training data (source domain) from a reference bridge (or a small set of reference bridges) and unlabeled monitoring test data (target domain) from another bridge, assuming that both domains are from similar but statistically different distributions. This type of mapping is expected to improve the classification accuracy for the target domain compared to a procedure that does not implement domain adaptation, as a result of reducing distributions mismatch between source and target domains.

桥梁管理部门一直不愿将结构健康监测纳入其桥梁管理系统,因为他们没有财力和技术资源来收集每座桥梁的长期监测数据。由于桥梁管理部门通常拥有大量类似的桥梁(如人行天桥),因此从一座或一小部分知名桥梁中迁移知识,以帮助在新桥梁和新环境中做出更有效决策的能力已变得越来越重要。从这个意义上说,机器学习的一个子领域--迁移学习提供了一种新颖的解决方案,即利用一座或多座人行天桥的长期监测数据,定期评估所有人行天桥的结构状况。本文首先在数值模型数据上展示了无监督迁移学习的适用性,然后在两座类似的预应力混凝土人行天桥数据上展示了无监督迁移学习的适用性。迁移学习使用了两种域适应技术,其中分类器可以访问来自参考桥梁(或一小组参考桥梁)的无标记训练数据(源域)和来自另一座桥梁的无标记监测测试数据(目标域),假设这两个域来自相似但统计上不同的分布。与不实施域适应的程序相比,这种映射方式可减少源域和目标域之间的分布不匹配,从而提高目标域的分类准确性。
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引用次数: 0
Field monitoring of the movements and deformations of two subway tunnels during the construction of an overcrossing tunnel: a case study 在修建过街隧道期间对两条地铁隧道的移动和变形进行实地监测:案例研究
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-13 DOI: 10.1007/s13349-024-00801-0
Huangsong Pan, Tong Qiu, Liyuan Tong

During the construction of a new tunnel overcrossing existing tunnels at close proximity, the existing tunnels should be protected by protective structures and/or ground improvement measures. However, the construction of these structures and ground improvement may cause movement or deformation to the existing tunnels, potentially jeopardizing their operational safety, particularly under soft soil and sensitive ground conditions. This study presents the results of a year-long field monitoring program focusing on the movement of two underlying subway tunnels during different construction phases of an overcrossing cut-and-cover tunnel. Protective structures/measures for the existing subway tunnels included the construction of H-pile walls, deep soil mixing, cast-in-situ bored piles, and staged excavation for the new tunnel. In terms of construction-induced movement to the existing subway tunnels, it was found that the construction of H-pile walls induced the largest vertical settlement, the deep soil mixing operations induced the largest horizontal displacements, and the staged excavation induced the largest uplift. Although the maximum horizontal displacement at the springline of a subway tunnel near the center of the construction area slightly exceeded the alarm value, the implemented protective structures/measures were effective in reducing the total horizontal and vertical displacements of the existing tunnels.

在興建新隧道橫跨現有隧道時,現有隧道應受到保護構築物及/或地面改善措施的保護。然而,这些结构和地面改善措施的建设可能会导致现有隧道的移动或变形,从而可能危及其运营安全,尤其是在软土和敏感的地面条件下。本研究介绍了一项为期一年的实地监测项目的结果,重点关注两条地下隧道在明挖回填隧道不同施工阶段的移动情况。现有地铁隧道的保护结构/措施包括建造 H 型桩墙、深层土壤搅拌、现浇钻孔桩,以及分阶段挖掘新隧道。在施工对现有地铁隧道造成的移动方面,发现建造工字桩墙引起的垂直沉降最大,深层土壤搅拌作业引起的水平位移最大,而分阶段开挖引起的隆起最大。虽然靠近施工区中心的地铁隧道弹线处的最大水平位移略微超过了警戒值,但已实施的保护结构/措施有效地减少了现有隧道的总水平和垂直位移。
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引用次数: 0
Cross-correlation difference matrix based structural damage detection approach for building structures 基于交叉相关差矩阵的建筑结构损伤检测方法
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-12 DOI: 10.1007/s13349-024-00781-1
Soraj Kumar Panigrahi, Chandrabhan Patel, Ajay Chourasia, Ravindra Singh Bisht

Damages to various building structures often occur over their service life and can occasionally lead to severe structural failures, threatening the lives of its residents. In recent years, special attention has been paid to investigating various damages in buildings at the early stage to avoid failures and thereby minimize maintenance. Structural health monitoring can be used as a tool for damage quantification using vibration measurements. The application of various sensors for measuring accelerations, velocity and displacement in civil infrastructure monitoring has a long history in vibration-based approaches. These types of sensors reveal dynamic characteristics which are global in nature and ineffective in case of minor damage identification. In a practical application, the available damage detection approaches are not fully capable of quickly sensing and accurately identifying the realistic damage in structures. Research on damage identification from strain data is an interesting topic in recent days. Some work on the cross-correlation approach is now a centre of attraction and strictly confined to bridge or symmetric structures. The present paper uses strain data to validate the cross-correlation approach for detecting damage to building structures. The effectiveness of the methodology has been illustrated firstly on a simply supported beam, then on a 5-storey steel frame and a 6-storey scaled-down reinforced concrete shear building and lastly on a frame structure with moving load as a special case. The results show that this approach has the potential to identify damages in different kinds of civil infrastructure.

各种建筑结构在使用过程中经常会发生损坏,有时会导致严重的结构故障,威胁居民的生命安全。近年来,人们特别重视在早期阶段调查建筑物的各种损坏情况,以避免出现故障,从而最大限度地减少维护工作。结构健康监测可作为一种利用振动测量进行损坏量化的工具。在土木基础设施监测中应用各种传感器测量加速度、速度和位移,这种基于振动的方法由来已久。这些类型的传感器揭示的动态特性具有全局性,对于轻微损坏的识别无效。在实际应用中,现有的损伤检测方法并不完全能够快速感应和准确识别结构中的实际损伤。从应变数据中进行损伤识别的研究是近年来一个有趣的话题。目前,交叉相关方法的一些研究工作已成为关注的焦点,但仅限于桥梁或对称结构。本文利用应变数据来验证检测建筑结构损坏的交叉相关方法。首先在简单支撑梁上说明了该方法的有效性,然后在 5 层钢结构框架和 6 层按比例缩小的钢筋混凝土剪力墙建筑上进行了说明,最后作为特例在带移动荷载的框架结构上进行了说明。结果表明,这种方法具有识别不同类型民用基础设施损坏的潜力。
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引用次数: 0
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