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Deformation prediction during the construction of segmental bridges based on GA-SMO-SVM algorithm: an example of CFST arch bridge 基于 GA-SMO-SVM 算法的节段桥梁施工过程中的变形预测:以 CFST 拱桥为例
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-07-06 DOI: 10.1007/s13349-024-00825-6
Kaizhong Xie, Jiecai Ning, Quanguo Wang, Hongxin Yao

Limited research has been undertaken on the extant optimization models pertaining to the prediction of arch deformation in the course of erecting long-span concrete-filled steel tube (CFST) arch bridges. Moreover, CFST arch bridges stand as prototypical instances within the realm of bridge engineering’s segmental structures. This study focuses on the CFST arch bridge as a case study for deformation prediction. In pursuit of precise CFST arch bridge deformation prediction during construction, our investigation has formulated an arch-truss deformation prediction model and computational approach. Moreover, high-precision measuring robots (total stations) are usually utilized to monitor the deformation of arch bridges to obtain accurate deformation data for subsequent prediction studies. This model relies on the employment of the genetic algorithm (GA), sequential minimal optimization (SMO) algorithm, and an optimized support vector machine (SVM). Five input parameters, as utilized in the cable-stayed fastening-hanging cantilever assembly method, have been employed in constructing the model. The optimal parameter configuration for the SMO-SVM model was ascertained by employing an adaptive genetic algorithm to ensure an efficient and precise optimization process. Subsequently, the SMO-SVM model underwent training with the identified optimal parameter set. The prediction outcomes were subsequently verified through testing, facilitating the prediction of arch truss deformations during the construction of CFST arch bridges. To demonstrate the applicability of the proposed model, it was applied to the Pingnan Third Bridge, which is recognized as the world's longest-span CFST arch bridge (at the time of completion) and has a main span of 575 m. We also conducted a comparative study of the GA-SMO-SVM model in three distinct dimensions: various kernel functions, differing optimization algorithms, and alternative regression models. Our findings indicate that the GA-SMO-SVM model, which harnesses an adaptive genetic algorithm for efficient model optimization, achieves the most precise deformation predictions with a maximum absolute error of 8.86 mm, outperforming other models and achieving millimeter-level accuracy. Furthermore, the GA-SMO-SVM model operates with high efficiency, requiring approximately 1/64th of the time consumed by the SMO-SVM model optimized via a grid search (GS). This study validates the mechanism of the CFST arch bridge construction deformation prediction model founded on the GA-SMO-SVM algorithm through thorough model interpretation and analysis, presenting a pioneering approach to predicting arch deformations in the construction of extensive-span CFST arch bridges. Additionally, it offers a foundation for predicting deformations in other segmental structures.

关于大跨度混凝土填充钢管(CFST)拱桥建造过程中拱桥变形预测的现有优化模型研究有限。此外,CFST 拱桥是桥梁工程分段结构领域的典型实例。本研究以 CFST 拱桥为案例,进行变形预测。为了精确预测 CFST 拱桥在施工过程中的变形,我们的研究制定了拱桁变形预测模型和计算方法。此外,通常利用高精度测量机器人(全站仪)来监测拱桥的变形,以获得精确的变形数据,用于后续预测研究。该模型采用遗传算法(GA)、顺序最小优化算法(SMO)和优化支持向量机(SVM)。在构建模型时,采用了斜拉索紧固-悬臂组装方法中使用的五个输入参数。通过采用自适应遗传算法,确定了 SMO-SVM 模型的最佳参数配置,以确保高效、精确的优化过程。随后,SMO-SVM 模型使用确定的最佳参数集进行了训练。预测结果随后通过测试得到验证,从而有助于预测 CFST 拱桥施工过程中的拱桁变形。为了证明所提模型的适用性,我们将其应用于平南三桥,该桥被公认为世界上跨度最大的 CFST 拱桥(竣工时),主跨达 575 米。我们还从三个不同的维度对 GA-SMO-SVM 模型进行了比较研究:不同的核函数、不同的优化算法和不同的回归模型。研究结果表明,GA-SMO-SVM 模型利用自适应遗传算法实现了高效的模型优化,实现了最精确的变形预测,最大绝对误差为 8.86 毫米,优于其他模型,达到了毫米级精度。此外,GA-SMO-SVM 模型运行效率高,所需的时间约为通过网格搜索(GS)优化的 SMO-SVM 模型的 1/64。本研究通过全面的模型解释和分析,验证了基于 GA-SMO-SVM 算法的 CFST 拱桥施工变形预测模型的机理,为大跨度 CFST 拱桥施工中的拱桥变形预测提供了一种开创性的方法。此外,它还为预测其他分段结构的变形奠定了基础。
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引用次数: 0
Seismic response and ambient vibrations of a Mediaeval Tower in the Mugello area (Italy) 意大利穆杰罗地区一座中世纪塔楼的地震响应和环境振动
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-07-01 DOI: 10.1007/s13349-024-00824-7
R. M. Azzara, V. Cardinali, M. Girardi, C. Padovani, D. Pellegrini, M. Tanganelli

This paper describes the experimental campaigns on the Tower of the Palazzo dei Vicari in Scarperia, a village in the Mugello area (Tuscany) exposed to high seismic hazards. The first campaign was carried out from December 2019 to January 2020, and the Tower underwent the so-called Mugello seismic sequence, which featured an M 4.5 earthquake. Other ambient vibration tests were repeated in June 2021 and September 2023 when another seismic sequence struck the area near Scarperia. These tests aimed to characterise the Tower’s dynamic behaviour under ambient and seismic excitations and check the response of the Tower over time. The experimental results were then used to calibrate a finite-element model of the Tower and estimate its seismic vulnerability. Several numerical simulations were conducted on the calibrated model using the NOSA-ITACA code for nonlinear structural analysis of masonry buildings. The dynamic behaviour of the Tower subjected to a seismic sequence recorded in 2023 by a seismic station at the base was investigated by comparing the velocities recorded along the Tower’s height with their numerical counterparts. Furthermore, several pushover analyses were conducted to investigate the collapse of the Tower as the load’s distribution and direction varied.

本文介绍了在斯卡佩里亚的维卡里宫塔上开展的实验活动,斯卡佩里亚是穆杰罗地区(托斯卡纳)的一个村庄,地震危险性很高。第一次试验于 2019 年 12 月至 2020 年 1 月进行,该塔经历了所谓的穆杰罗地震序列,其中包括一次 M 4.5 级地震。2021 年 6 月和 2023 年 9 月,当斯卡佩里亚附近地区再次发生地震时,又重复进行了其他环境振动测试。这些测试旨在确定塔楼在环境和地震激励下的动态特性,并检查塔楼随时间变化的反应。实验结果随后被用于校准铁塔的有限元模型,并估算其地震脆弱性。使用 NOSA-ITACA 代码对校准后的模型进行了多次数值模拟,该代码用于砌体建筑的非线性结构分析。通过比较铁塔高度沿线记录的速度与数值模拟结果,研究了铁塔在 2023 年由底部地震台站记录的地震序列中的动态行为。此外,还进行了多次推移分析,以研究铁塔在荷载分布和方向变化时的倒塌情况。
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引用次数: 0
Intelligent recognition of ground penetrating radar images in urban road detection: a deep learning approach 城市道路探测中地面穿透雷达图像的智能识别:一种深度学习方法
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-07-01 DOI: 10.1007/s13349-024-00818-5
Fujun Niu, Yunhui Huang, Peifeng He, Wenji Su, Chenglong Jiao, Lu Ren

In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with manual interpretation of GPR images typically used to identify buried objects. Nonetheless, manual interpretation methods are not only inefficient but also subjective, as they rely on the interpreter's experience, thereby affecting the interpreting reliability. This study investigates the distribution and causes of road collapses and develops a deep learning-based intelligent recognition model using GPR detection images of urban roads in cities of the South China as original samples. The finding reveal that road collapses are concentrated in the months of July and August, mainly caused by pipe leakage and rainfall. Common anomalies in urban road GPR detection comprise seven types of target objects, including cavity, pipeline, etc., with standard GPR images acquired through outdoor field experiments. Utilizing GPR forward simulation and image augmentation methods to expand the sample size, as well as generating anchor box dimensions through clustering analysis, have all been proven to effectively improve the model's performance. The urban road GPR image intelligent recognition model, based on the YOLOv4 algorithm, achieves a detection accuracy of up to 85%, proving effective in GPR detection of urban roads in cities of North China. This research offers valuable insights for the future application of deep learning-based image recognition algorithms in urban road GPR detection.

近年来,城市道路塌陷事件发生得越来越频繁,尤其是在人口众多的城市。为减少道路塌陷,地球物理勘探在城市道路检测中发挥着至关重要的作用。地面穿透雷达(GPR)是一种非破坏性技术,被广泛用于探测城市道路的损坏情况,通常使用人工判读 GPR 图像来识别被埋物体。然而,人工判读方法不仅效率低下,而且依赖于判读人员的经验,具有一定的主观性,从而影响判读的可靠性。本研究以华南地区城市道路的 GPR 检测图像为原始样本,研究了道路塌陷的分布和成因,并开发了基于深度学习的智能识别模型。研究结果表明,道路塌陷主要集中在七八月份,主要原因是管道渗漏和降雨。城市道路 GPR 检测中常见的异常情况包括空洞、管道等七种类型的目标物体,其标准 GPR 图像是通过室外现场实验获取的。实践证明,利用 GPR 正向模拟和图像增强方法扩大样本量,以及通过聚类分析生成锚箱尺寸,都能有效提高模型的性能。基于 YOLOv4 算法的城市道路 GPR 图像智能识别模型的检测准确率高达 85%,在华北地区城市道路 GPR 检测中证明是有效的。这项研究为未来基于深度学习的图像识别算法在城市道路 GPR 检测中的应用提供了有价值的启示。
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引用次数: 0
Drive-by scour damage detection in railway bridges using deep autoencoder and different sensor placement strategies 利用深度自动编码器和不同的传感器布置策略检测铁路桥梁的驱动冲刷损伤
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-06-25 DOI: 10.1007/s13349-024-00821-w
Thiago Fernandes, Rafael Lopez, Diogo Ribeiro

Foundation scour is a critical phenomenon that may lead to the collapse of railway bridges. This issue is even more concerning in the current scenario where extreme weather events, such as floods, are becoming more severe and recurrent. Among different methodologies for assessing the structural integrity of railway bridges, vehicle-assisted monitoring has emerged as promising due to its low-cost and straightforward sensor installation compared to direct instrumentation of bridges. This paper provides a proof of concept of employing vehicle acceleration measurements from passing trains to detect the occurrence of bridge scour. To assess the effectiveness of accelerometer placement in data acquisition, vertical acceleration responses are collected from various positions throughout the vehicle and for different vehicles in the train, considering operational variabilities and measurement noise. A deep autoencoder model is used to process raw acceleration measurements collected during multiple train passages over a bridge affected by scour, where the scour damage is simulated as a local reduction in stiffness within a specific pier-foundation system. The difference between model-based and vehicle responses obtained from various observed events is the prediction error evaluated by the mean absolute error. The Kullback–Leibler divergence-based damage index is proposed to assess the number of vehicle-crossing events required to infer the damage. Finally, the approach’s accuracy is evaluated using Receiver Operating Characteristic curves. The results demonstrate that the applied methodology is highly effective in detecting both 5% and 10% levels of scour damage for sensors placed on the front and rear bogies of the first and last vehicles, without any prior data preprocessing.

地基冲刷是可能导致铁路桥梁坍塌的一个关键现象。在当前情况下,洪水等极端天气事件正变得越来越严重和频繁,这个问题就更加令人担忧。在评估铁路桥梁结构完整性的各种方法中,车辆辅助监测因其低成本和直接安装传感器而比直接安装桥梁仪器更有前途。本文提供了利用过往列车的车辆加速度测量来检测桥梁冲刷发生情况的概念验证。为了评估加速度计位置在数据采集中的有效性,考虑到运行变异和测量噪声,从整个车辆的不同位置和列车中的不同车辆收集垂直加速度响应。使用深度自动编码器模型来处理在列车多次通过受冲刷影响的桥梁时收集到的原始加速度测量值,其中冲刷破坏被模拟为特定桥墩基础系统内刚度的局部降低。通过各种观测事件获得的基于模型的车辆响应与基于模型的车辆响应之间的差异,就是用平均绝对误差评估的预测误差。提出了基于 Kullback-Leibler 发散的损坏指数,以评估推断损坏所需的车辆穿越事件数量。最后,利用接收器工作特性曲线对该方法的准确性进行了评估。结果表明,对于放置在第一辆和最后一辆车前后转向架上的传感器,所应用的方法能非常有效地检测出 5%和 10%级别的冲刷损坏,而无需事先进行任何数据预处理。
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引用次数: 0
Integrated analysis of instrumentation data for structural health assessment and behavior prediction of arch dams 用于拱坝结构健康评估和行为预测的仪器数据综合分析
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-06-23 DOI: 10.1007/s13349-024-00819-4
Milad Moradi Sarkhanlou, Vahab Toufigh, Mohsen Ghaemian

In recent years, machine learning techniques have been available to predict and interpret the structural behavior of dams. Continuous monitoring of dam structure safety is vital in preventing possible damage. This study aims to predict the structural behavior by considering data collected for 13 years from instruments in the dam structure. Various machine learning methods are performed to account for the multi-non-linear relationships between dam displacement and the influential factors, thereby exploring the displacement laws of the dam. Three error metric indicators are employed for prediction, validation, and verification techniques to ensure the performance of models. Validation techniques include historical data validation, prediction validation, and the residual behavior over time. Predicting the structural behavior of the dam using the selected model requires data related to the input variables of the model. For this reason, the long short-term memory (LSTM) model, a robust algorithm for predicting time series variables, was used to predict the input variables. LSTM model provided acceptable predictions of changes in the input variables for these years. Additionally, the Boosted Regression Trees model, selected as the most accurate in the evaluation process, was employed to predict the structural behavior of the dam for periods not yet experienced by the dam, using these input variables. The predicted behavior of the dam demonstrated a strong ability to interpret the health of the dam structure and prevent possible damages. The effectiveness of the LSTM model was confirmed as a promising method in predicting time series input variables for ML models to predict dam displacements in the next years.

近年来,机器学习技术已可用于预测和解释大坝的结构行为。对大坝结构安全的持续监测对于预防可能发生的破坏至关重要。本研究旨在通过考虑从大坝结构中的仪器收集到的 13 年数据来预测结构行为。采用多种机器学习方法来考虑大坝位移与影响因素之间的多非线性关系,从而探索大坝的位移规律。在预测、验证和检验技术中采用了三种误差度量指标,以确保模型的性能。验证技术包括历史数据验证、预测验证和随时间变化的残余行为。使用选定的模型预测大坝的结构行为需要与模型输入变量相关的数据。因此,使用了长短期记忆(LSTM)模型来预测输入变量,这是一种预测时间序列变量的稳健算法。LSTM 模型对这些年输入变量的变化做出了可接受的预测。此外,在评估过程中被选为最准确的增强回归树模型也被用来预测大坝在尚未经历的时期内的结构行为。大坝的预测行为表现出很强的解释大坝结构健康状况和预防可能发生的损坏的能力。LSTM 模型的有效性得到了证实,它是预测时间序列输入变量的一种有前途的方法,可用于 ML 模型预测未来几年的大坝位移。
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引用次数: 0
Understanding of leaning utility poles for visual monitoring of power distribution infrastructure 了解倾斜的电线杆,对配电基础设施进行可视化监测
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-06-21 DOI: 10.1007/s13349-024-00820-x
Luping Wang, Gang Liu, Shanshan Wang, Hui Wei

Protecting power infrastructure through visual surveillance can assure the safe operation of a power system, especially in unstructured environments where leaning utility poles are particularly inclined to cause large-area blackouts or even personal injury. Current methods place too much emphasis on detection and not enough on understanding leaning postures. However, due to the diversity and uncertainty of leaning utility poles, understanding them remains an urgent problem. Traditional posture estimation via three-dimensional (3D) point clouds is energy-intensive and costly, which limits its adoption in resource-constrained visual surveillance systems. In this study, we present a methodology to understand utility poles, and to estimate their leaning postures using a low-cost monocular camera. Edges and lines are extracted. Through their corresponding proximity and orientation, potential lines of utility poles are estimated. By analyzing relative geometric constraints between potential lines, utility poles are segmented and corresponding leaning angles are estimated, which is helpful to make risk-informed decisions to make leaning utility poles resilient. The approach requires neither prior training, nor calibration or adjustment of the camera’s internal parameters. It is robust against color and illumination associated with severe weather conditions. The percentage of correctly segmented pixels was compared to the ground truth, demonstrating that the method can successfully understand utility poles, meeting safety monitoring requirements for power infrastructure.

通过视觉监控保护电力基础设施可以确保电力系统的安全运行,特别是在非结构化环境中,倾斜的电线杆尤其容易造成大面积停电甚至人身伤害。目前的方法过于强调检测,而对倾斜姿态的理解不够。然而,由于倾斜电线杆的多样性和不确定性,理解它们仍然是一个亟待解决的问题。传统的三维(3D)点云姿态估算耗能且成本高昂,这限制了其在资源有限的视觉监控系统中的应用。在本研究中,我们提出了一种了解电线杆的方法,并使用低成本单目摄像头估算其倾斜姿态。我们提取了电线杆的边缘和线条。通过其相应的距离和方向,估算出电线杆的潜在线路。通过分析潜在线路之间的相对几何约束,对电线杆进行分割,并估算出相应的倾斜角度,这有助于做出风险知情决策,使倾斜的电线杆具有弹性。这种方法既不需要事先训练,也不需要校准或调整相机的内部参数。它对与恶劣天气条件相关的颜色和光照具有鲁棒性。正确分割像素的百分比与地面实况进行了比较,表明该方法可以成功地了解电线杆,满足电力基础设施的安全监控要求。
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引用次数: 0
Application and comparison of GRNN, BPNN and RBFNN in the prediction of suspender frequency and tension on arch bridge GRNN、BPNN 和 RBFNN 在拱桥悬索频率和张力预测中的应用和比较
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-06-17 DOI: 10.1007/s13349-024-00816-7
Zhu Zhang, Eryu Zhu, Bin Wang, Ye Chen

The prediction of suspender frequency and tension is difficult to solve due to the non-linear nature of suspender parameters. A method of predicting suspender frequency and tension using the generalized regression neural network (GRNN) model was proposed in this paper. It is necessary to select some suspender parameters as inputs into the model to solve the non-linear nature problem of the suspender parameters, such as length, mass unit per length, bending stiffness, fundamental frequency as well as tension, and to select the suspender frequency or tension as output. To consider the effect of different boundary constraints, analytical expressions of suspender parameters based on the singular perturbation method are derived and applied to train the models. Two different types of neural network models: back propagation neural network (BPNN) and radial basis function neural network (RBFNN), are also used to predict suspender frequency and tension to compare with the GRNN model. Datasets consist of measurements and literature samples are used to verify the models. Furthermore, R2, MAE, and RMSE are used to compare the performance of the models. The results showed that the application of GRNN achieves higher accuracy in predicting suspender frequency and tension compared to BPNN and RBFNN.

由于悬带参数的非线性特性,悬带频率和张力的预测很难解决。本文提出了一种利用广义回归神经网络(GRNN)模型预测悬带频率和张力的方法。为了解决悬带参数的非线性问题,有必要选择一些悬带参数作为模型的输入,如长度、单位长度质量、弯曲刚度、基频以及张力,并选择悬带频率或张力作为输出。为了考虑不同边界约束的影响,基于奇异扰动法推导出了悬带参数的解析表达式,并应用于模型训练。此外,还使用了两种不同类型的神经网络模型:反向传播神经网络(BPNN)和径向基函数神经网络(RBFNN)来预测悬带频率和张力,以便与 GRNN 模型进行比较。数据集包括测量数据和文献样本,用于验证模型。此外,还使用 R2、MAE 和 RMSE 来比较模型的性能。结果表明,与 BPNN 和 RBFN 相比,应用 GRNN 预测吊带频率和张力的准确度更高。
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引用次数: 0
Experimental and numerical investigations on defect location detection of multi-damage steel beams using advanced damage location vector approach 利用先进的损伤定位矢量法对多重损伤钢梁的缺陷定位检测进行实验和数值研究
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-06-06 DOI: 10.1007/s13349-024-00814-9
Nahid Khodabakhshi, Alireza Khaloo, Amin Khajehdezfuly

Damage location vector (DLV) method is a model-based structural health monitoring approach that needs the frequency response–function response of the structure. A review of the literature indicates that although the DLV method accurately identifies the damage location in the single-damage structures, it does not work properly for the multi-damage. Accordingly, the aim of this research is to advance the DLV approach to increase its accuracy to detect the damage locations and severities in the multi-damage structures. In this regard, experimental and numerical studies were performed on the two-fixed ends steel beam having multiple damages with different intensities. During laboratory tests, the vibration response of steel beam specimens with multiple defects stimulated by hammer impact was measured. Different sensor locations were considered in the tests. A finite-element model of the steel beam was developed to calculate the dynamic response of undamaged beam under impact loading. Based on the fundamentals of hypothesis testing and data fusion, a threshold was derived to advance the DLV approach to detect the multiple damages. Moreover, the effect of sensor position on the performance of the DLV approach was investigated. The proposed method was also applied to a long-span box-shaped bridge to investigate its accuracy and efficiency for detecting damages in realistic complex structures. Moreover, the results obtained from the advanced DLV method were compared with other conventional methods, considering the effect of noise and different damage scenarios. The findings reveal that the advanced DLV approach proposed in this study accurately detects the defect locations and severities in the structures having multiple damages.

损伤位置矢量(DLV)方法是一种基于模型的结构健康监测方法,它需要结构的频率响应函数响应。文献综述表明,虽然 DLV 方法能准确识别单损伤结构的损伤位置,但在多损伤结构中却无法正常工作。因此,本研究的目的是推进 DLV 方法,提高其检测多损伤结构中损伤位置和严重程度的准确性。为此,我们对具有不同强度多重损伤的两端固定钢梁进行了实验和数值研究。在实验室试验中,测量了具有多重损伤的钢梁试样在锤击刺激下的振动响应。试验中考虑了不同的传感器位置。建立了钢梁的有限元模型,以计算未损坏钢梁在冲击荷载下的动态响应。基于假设检验和数据融合的基本原理,得出了一个阈值,以推进 DLV 方法检测多重损坏。此外,还研究了传感器位置对 DLV 方法性能的影响。还将所提出的方法应用于一座大跨度箱形桥梁,以研究其在实际复杂结构中检测损坏的准确性和效率。此外,考虑到噪声和不同损坏情况的影响,将高级 DLV 方法与其他传统方法的结果进行了比较。研究结果表明,本研究提出的先进 DLV 方法能准确检测出具有多重损坏的结构中的缺陷位置和严重程度。
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引用次数: 0
Site-specific traffic modelling and simulation for a major Italian highway based on weigh-in-motion systems accounting for gross vehicle weight limitations 基于车辆总重限制的动态称重系统,为意大利一条主要高速公路进行现场交通建模和模拟
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-05-31 DOI: 10.1007/s13349-024-00809-6
Nagavinothini Ravichandran, Daniele Losanno, Maria Rosaria Pecce, Fulvio Parisi

The present-day road traffic with the persistent change in the type and volume of vehicles needs to be specifically investigated for effective safety management of aging highway infrastructures. Actual traffic data can be implemented in refined procedures for stochastic simulation of road infrastructure performance, structural health monitoring (SHM), definition of weight limits on highways, and traffic-informed structural safety checks. While weigh-in-motion (WIM) systems had been widely used in many countries, their installation on Italian highways was mostly discussed and carried out only after the catastrophic collapse of the Polcevera bridge in 2018. This study presents a statistical data analysis, probabilistic models, and a simulation procedure for highway traffic, based on measurements of two WIM systems located along European route E45 close to Naples, Italy. Different limitations to maximum gross vehicle weight (GVW) were enforced at the locations of the two WIM systems, according to the Italian road code and the Italian guidelines for risk classification, safety assessment and monitoring of existing bridges, respectively. WIM data sets were filtered to exclude erroneous traffic data and vehicle classes defined according to the number of axles and axle distance were statistically characterised, allowing the derivation of probabilistic models for all traffic parameters of interest. A simulation methodology to generate random traffic load from the WIM data is also presented for its possible use in probabilistic performance assessment and traffic informed SHM of road infrastructures such as bridges.

为了对老化的公路基础设施进行有效的安全管理,需要对车辆类型和数量不断变化的当今公路交通进行专门研究。实际交通数据可用于道路基础设施性能随机模拟、结构健康监测(SHM)、公路重量限制定义和交通信息结构安全检查的完善程序中。虽然运动中称重(WIM)系统已在许多国家得到广泛应用,但其在意大利高速公路上的安装大多是在 2018 年波尔切维拉大桥灾难性坍塌之后才开始讨论和实施的。本研究根据对位于意大利那不勒斯附近的欧洲 E45 公路沿线的两个 WIM 系统的测量结果,提出了公路交通的统计数据分析、概率模型和模拟程序。根据意大利道路法规和意大利现有桥梁风险分类、安全评估和监控指南,在两个 WIM 系统的位置分别实施了不同的最大车辆总重(GVW)限制。对 WIM 数据集进行过滤,以排除错误的交通数据,并对根据车轴数和轴距定义的车辆类别进行统计,从而推导出所有相关交通参数的概率模型。此外,还介绍了从 WIM 数据中生成随机交通负荷的模拟方法,该方法可用于道路基础设施(如桥梁)的概率性能评估和交通信息 SHM。
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引用次数: 0
A novel Bayesian optimal detector-based approach for determining the first arrival time of wire breakage-induced near-wall acoustic wave in PCCPs 基于贝叶斯最优检测器的新方法,用于确定 PCCP 中断线诱发的近壁声波的首次到达时间
IF 4.4 2区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-05-25 DOI: 10.1007/s13349-024-00810-z
Xudu Liu, Yang Han, Minghao Li, Xin Feng

Wire breakage in prestressed cylinder concrete pipes (PCCPs) due to various factors, such as corrosion, hydrogen embrittlement, material defects and overload, may lead to structural failure. Real-time detection of acoustic waves generated by wire breakage is possible using fiber optic sensors. Accurate determination of the first arrival time (FAT) of acoustic wave is vital for localizing wire breakages. A novel method based on the Bayesian optimal detector is proposed to automatically identify the FAT of near-wall acoustic wave. The FATs are subsequently fed into a localization model of wire breakage. The localization results are compared for the FAT of the proposed method and human subjective picking via model tests. The results show that compared with human subjective picking, the wire breakage localization of the proposed method can ensure the accuracy of the results. The maximum errors of the longitudinal and circumferential positions of the proposed method are 0.15 m and 0.02 m, respectively. The experimental results demonstrate that the FATs determined by the Bayesian optimal detector enable the accurate localization of wire breakage with noisy measurements. The proposed method overcomes the limitation of traditional picking methods in determining the FAT, which provides a promising tool for real-time monitoring of wire breakage in PCCPs.

由于腐蚀、氢脆、材料缺陷和过载等各种因素,预应力圆筒混凝土管(PCCP)中的钢丝断裂可能会导致结构失效。使用光纤传感器可以实时检测断丝产生的声波。准确测定声波的首次到达时间(FAT)对断线定位至关重要。本文提出了一种基于贝叶斯最优检测器的新方法,用于自动识别近壁声波的 FAT。随后将 FAT 输入断线定位模型。通过模型试验,比较了拟议方法和人类主观拾取的 FAT 定位结果。结果表明,与人的主观拾取相比,拟议方法的断线定位能确保结果的准确性。建议方法的纵向和圆周位置的最大误差分别为 0.15 m 和 0.02 m。实验结果表明,贝叶斯最优检测器确定的 FAT 能够在有噪声测量的情况下准确定位断线。所提出的方法克服了传统拾取方法在确定 FAT 方面的局限性,为实时监测 PCCP 中的断线情况提供了一种很有前途的工具。
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Journal of Civil Structural Health Monitoring
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