Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges.This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring.Firstly,the existing monitoring technologies at home and abroad are sorted out,and the advantages and problems of various methods are compared and analyzed,including nondestructive testing,stress measurement,vibration characteristic identification,and other commonly used monitoring technologies.Secondly,the key technologies and equipment in the bridge health monitoring system,such as sensor technology,data acquisition,and processing technology,are introduced in detail.Finally,the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application.In the future,with the development of emerging technologies such as big data,cloud computing,and the Internet of Things,it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

Structural health monitoring of long-span suspension bridges using wavelet packet analysis

During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.

Finite element model updating of existing steel bridge based on structural health monitoring

Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.

Fatigue damage reliability analysis for Nanjing Yangtze river bridge using structural health monitoring data

To evaluate the fatigue damage reliability of critical members of the Nanjing Yangtze river bridge, according to the stress-number curve and Miner’s rule, the corresponding expressions for calculating the structural fatigue damage reliability were derived. Fatigue damage reliability analysis of some critical members of the Nanjing Yangtze river bridge was carried out by using the strain-time histories measured by the structural health monitoring system of the bridge. The corresponding stress spectra were obtained by the real-time rain-flow counting method. Results of fatigue damage were calculated respectively by the reliability method at different reliability and compared with Miner’s rule. The results show that the fatigue damage of critical members of the Nanjing Yangtze river bridge is very small due to its low live-load stress level.

Structural condition assessment of long-span suspension bridges using long-term monitoring data

This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.

Engineering approach to in-situ bridge health monitoring with fiber bragg gratings

In this presentation the feasibility and capability of fiber Bragg gratings (FBG) employed in bridge health monitoring are demonstrated on a real bridge. FBG’s wavelength shift depending on strain variance has been tested. The technique of FBG installation on bridges has been developed. 12 FBG strain sensors and 3 temperature sensors have been successfully embedded in the prestressed concrete box girder during the construction of Heilongjiang Hulan River Bridge. The prestressing tension process and quasi-static loading process of the girder were monitored with those sensors before it was installed onto the bridge. After the bridge was completed, the FBG sensors embedded have been utilized to monitor the strain shift of the beam under quasi-static load, traffic load and temperature. The results show that the traffic fluxes, possible fatigue damage and deflection of the bridge can be revealed conveniently through strain measurements with these FBG sensors, which provide key information for structural health diagnosis. The fact that the FBG strain sensors have withstood the ordeal of harsh construction process and lasted for more than one year proves that their durability and stability can satisfy the requirements for bridge health monitoring. It is also shown that the FBG strain sensor is more adaptive to long-term structural health monitoring than the electric resistance strain gauge.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Development of IoT-Based Condition Monitoring System for Bridges

As of April 2019,India has 1,42,126 kilometres of National Highways and 67,368 kilometres of railway tracks that reach even the most remote parts of the country.Bridges are critical for both passenger and freight movement in the country.Because bridges play such an important part in the transportation system,their safety and upkeep must be prioritized.Manual Condition Monitoring has the disadvantage of being sluggish,unreliable,and inefficient.The Internet of Things has given structural monitoring a boost.Significant decreases in the cost of electronics and connection,together with the expansion of cloud platforms,have made it possible to collect large amounts of data remotely,aggregate it,and perform essential analysis to generate actionable insights.This research focuses on a scalable system for monitoring the state of bridges,such as vibration and loading,employing multimodal inputs,controllers,and Wi-Fi modules.The accelerometer and load cells were installed on the prototype,tested for a sample load(56.21 grams_(avg),590 grams_(max),and 147.66 grams_(rms))with induced vibration(5.87 m/sec^(2)_(avg),18 m/sec^(2)_(max),and 7.04 m/sec^(2)_(rms))that are processed,displayed on-board,and uploaded to ThingSpeak cloud service.This system will aid the maintenance personnel in remotely monitoring it.This system can send out notifications if any of these parameters exceeds their threshold value,allowing you to take preventive measures ahead of time.

基于EDA统计图量化的桥梁动态监测数据质量评估

探索性数据分析统计图在桥梁健康监测动态数据质量评估中已有广泛应用。为了减少人工观察统计图的主观性,通过近似度量方法实现统计图的量化分析,得到多个指标对监测数据进行快速质量评估。在运营环境激励作用下,桥梁结构动力响应具有短时线性平稳性,近似服从正态分布。以某大跨斜拉桥振动数据为研究对象,首先,绘制样本数据直方图和Q-Q图,通过观察数据分布特征预先判断数据质量,确定优、良和差3个等级。然后,分别通过KL散度和余弦相似度2种近似度量方法对样本数据直方图和Q-Q图进行量化,得到数据服从正态分布程度的指标;通过箱线图检测样本数据全局异常点,得到正常数据占比;统计分析得到量化值和先验质量等级的对应关系,确定以直方图KL散度和余弦相似度为主、以箱线图正常数据占比为辅的数据质量评估标准。最后,取部分数据为验证集,进一步验证所提方法各个指标的合理性,并给出该方法在实际工程上的应用结果。

基于InSAR技术的大跨桥梁温度变形监测研究

以国内某高速铁路钢拱桥为研究对象,选取2017—2018年期间59幅C波段Senti⁃nel-1号雷达卫星影像,利用PS-InSAR技术处理影像获得桥梁的视线向(Line of Sight,LOS)位移,根据SAR成像空间几何关系解算出支座的纵向位移.研究结果表明:支座纵向位移的时空特性与实际桥梁结构相符合,验证了PS-InSAR技术观测桥梁结构位移的可行性.建立支座纵向位移与温度的线性相关模型,并与结构健康监测系统的实测结果进行对比.两者吻合良好,相对误差控制在10%以内,验证了PS-InSAR测量桥梁结构位移的可靠性.利用有限元模拟温度作用下桥梁支座的位移变化,并与PS-InSAR位移时间序列进行对比.两者趋势基本一致,LOS向位移误差在[-10,10]mm,验证了PS-InSAR测量桥梁结构位移的准确性.

Vehicle-Bridge Interaction Simulation and Damage Identification of a Bridge Using Responses Measured in a Passing Vehicle by Empirical Mode Decomposition Method

To prevent early bridge failures, effective Structural Health Monitoring (SHM) is vital. Vibration-based damage assessment is a powerful tool in this regard, as it relies on changes in a structure’s dynamic characteristics as it degrades. By measuring the vibration response of a bridge due to passing vehicles, this approach can identify potential structural damage. This dissertation introduces a novel technique grounded in Vehicle-Bridge Interaction (VBI) to evaluate bridge health. It aims to detect damage by analyzing the response of passing vehicles, taking into account VBI. The theoretical foundation of this method begins with representing the bridge’s superstructure using a Finite Element Model and employing a half-car dynamic model to simulate the vehicle with suspension. Two sets of motion equations, one for the bridge and one for the vehicle are generated using the Finite Element Method, mode superposition, and D’Alembert’s principle. The combined dynamics are solved using the Newmark-beta method, accounting for road surface roughness. A new approach for damage identification based on the response of passing vehicles is proposed. The response is theoretically composed of vehicle frequency, bridge natural frequency, and a pseudo-frequency component related to vehicle speed. The Empirical Mode Decomposition (EMD) method is applied to decompose the signal into its constituent parts, and damage detection relies on the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component. This technique effectively identifies various damage scenarios considered in the study.

大跨度斜拉桥模态参数长期追踪及其变异性分析

为了丰富大跨度斜拉桥模态参数的实测数据库,依托苏通大桥结构健康监测系统采集的数据,采用所建立的模态自动识别和追踪方法,获取了2010年期间该桥的模态参数值,并据此分析了在温度和风速作用下桥梁模态参数的变异性。研究结果表明,大桥主梁模态频率的变化受温度和风速共同影响,随着温度的升高而降低,随着风速的升高而增加。大桥主梁模态阻尼比的变异性显著大于模态频率,低风速下主梁一阶侧弯阻尼比在0.5%~15%之间波动,风速大于9 m/s时逐渐降低并稳定在2%左右。大桥主梁前四阶竖弯阻尼比受气动阻尼的影响较大,在风速较小时随着风速的增加略有增加。研究结果可为大桥的服役性能评估与运营管理提供参考。

自激非平稳振动数据驱动的拉索动力性能表征

跨海斜拉桥的拉索在非极端风环境下会产生自激振动,基于现代监测与数据分析技术可即时把握拉索自激振动的数字特征,并以此反映拉索的实时动力性能。根据某跨海大桥主航道斜拉桥拉索振动加速度长期监测数据中的共性特征,提出基于振动加速度时间序列信号上包络线高斯混合模型和频域功率谱的拉索风致自激振动非平稳段自动提取方法。提出基于各自激振动非平稳时间序列进行主导频率识别,并采用带通滤波后的最后1个下降段数据进行阻尼比识别的策略,排除了振动幅值上升段环境风自然激励带入能量对阻尼比识别的干扰。基于各自激振动非平稳段主导频率-阻尼比的识别结果,通过对频率值进行聚类,得到各阶模态对应的模态频率-阻尼比数据簇。根据阻尼比数据的离散、偏态特征提出采用对数正态分布模型的e^(μ)和其累积分布函数的分位值来描述拉索阻尼比统计规律。采用各数据簇的频率质心以及阻尼比概率特征参数作为表征拉索动力性能当前状态的指标。针对背景工程的主要结论包括:桥梁拉索振动加速度信号噪声强、干扰大,在动力特征分析时须排除类似干扰;桥梁拉索的自激振动加速度幅值最高已超过3000 mm/s^(2),振动幅度较大;桥梁拉索的阻尼比平均水平约为0.03%,与设计规范推荐值相比偏低。

桥梁传感器优化布置的研究现状与发展趋势

桥梁在交通网络中充当着极其重要的角色,其正常运行与否关系到人民生活幸福指数的高低。所以,为其建立结构健康监测系统以实时监测其安全状态、预警其异常行为及预测其工作性能走向的方法极为必要。桥梁结构传感器的优化布置,简单来说就是通过正确合理地布置所需传感器来获取桥梁的各项信息。传感器的优化布置研究的关键则在于其布置方法与评价准则,国内外的学者经过多年的研究,在该领域取得一定的突破与创新,但是仍然存在很多不完善的地方,如何通过改进各种算法来确定传感器优化布置最优解成为研究的主要方向。该文重点阐述传感器优化布置准则和传感器优化布置的方法在目前研究方向中发挥的重要作用,并对桥梁健康监测的后续发展表达看法和展望。

桥梁结构状态评估方法研究现状与展望

桥梁老龄化日益严重,桥梁安全状态成为广泛关注的问题,对桥梁进行合理的状态评估有助于桥梁管理养护和危险预警,保证桥梁正常运营。首先介绍了桥梁健康监测系统,分析监测系统存在的问题。其次分别从试验、数据分析和数字孪生方面介绍了桥梁评估方法的研究现状,然后阐述了桥梁状态评估的发展与挑战,综合分析表明目前结构健康监测系统仍存在较多问题,试验方面基于动力测试的损伤识别和基于视觉监测的桥梁状态评估都有自身的局限性,海量的监测数据需要进行有效的处理,BIM技术下的桥梁健康监测系统能实现监测与养护一体化运维管理,数字孪生技术将为桥梁状态评估带来新的可能。

温度荷载作用下混凝土曲线箱梁桥支座位移研究

本研究基于武汉城市桥梁健康监测项目,对某大型互通立交曲线匝道梁桥长期监测数据进行分析。利用支座位移及环境温度监测数据相关性分析了桥梁病害情况,并给出相应的维修加固建议,对于保证桥梁结构的安全运营具有重要意义。首先,分析了曲线梁桥的平面受力与变形的特点,并阐述了温度对曲线梁桥变形的影响特性。其次,分析了环境温度与支座位移的相关性,并结合预警机制,制定了针对支座位移的桥梁结构健康监测预警流程。最后,基于一个实际案例,结合支座位移与梁缝工作状态空间联动性,成功预测到了桥梁结构的病害。本研究针对曲线梁桥的特点,提供了一种新的基于支座位移监测数据的健康诊断思路,以保证桥梁结构的安全运营。

一种基于四层架构的桥梁健康监测系统Web端应用平台

作为一种新型桥梁管养信息化系统,桥梁健康监测系统可实现桥梁设施的自动化、智慧化运维管理。当前,桥梁健康监测系统存在监测数据分析处理不足、平台可视化水平较低、系统业务功能单一等问题,限制了非专业人士对系统平台的使用,也制约了系统的进一步推广应用。针对桥梁健康监测系统的现有问题,文字提出了一种基于Web端的桥梁健康监测应用平台。该平台基于四层架构打造,在数据处理、界面展示、功能扩展等方面具备显著优势,能够为桥梁管养运维工作提供直观可靠的信息化管理工具。

长大连续钢构桥梁健康监测系统平台的研究及应用

目前,我国尚没有完全面向桥梁施工期和运维期的仿真分析系统,同时存在传统桥梁结构监测系统大数据处理自适应能力较差、状态评价分析片面及预警模式不完备等问题,通过对普通国省道长大连续钢构桥梁智慧健康安全监测集群式平台研究、长大连续钢构桥梁结构仿真分析、CAE+AI结构反演监测与可视化预测预警,采用力学仿真分析、机器学习、双向长短期记忆神经网络等方法,实现了可视化结构模型,为该类桥梁在运营期养护决策提供数据依据,提高养护精准性。

Analysis and assessment of bridge health monitoring mass data——progress in research/development of “Structural Health Monitoring”

The "Structural Health Monitoring" is a project supported by National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.50725828).To meet the urgent requirements of analysis and assessment of mass monitoring data of bridge environmental actions and structural responses,the monitoring of environmental actions and action effect modeling methods,dynamic performance monitoring and early warning methods,condition assessment and operation maintenance methods of key members are systematically studied in close combination with structural characteristics of long-span cable-stayed bridges and suspension bridges.The paper reports the progress of the project as follows.(1) The environmental action modeling methods of long-span bridges are established based on monitoring data of temperature,sustained wind and typhoon.The action effect modeling methods are further developed in combination with the multi-scale baseline finite element modeling method for long-span bridges.(2) The identification methods of global dynamic characteristics and internal forces of cables and hangers for long-span cable-stayed bridges and suspension bridges are proposed using the vibration monitoring data,on the basis of which the condition monitoring and early warning methods of bridges are developed using the environmental-condition-normalization technique.(3) The analysis methods for fatigue loading effect of welded details of steel box girder,temperature and traffic loading effect of expansion joint are presented based on long-term monitoring data of strain and beam-end displacement,on the basis of which the service performance assessment and remaining life prediction methods are developed.

Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography

Manual inspections of infrastructures such as highway bridge, pavement, dam, and multistoried garage ceiling are time consuming, sometimes can be life threatening, and costly. An automated computerized system can reduce time, faulty inspection, and cost of inspection. In this study, we developed a computer model using deep learning Convolution Neural Network (CNN), which can be used to automatically detect the crack and non-crack type structure. The goal of this research is to allow application of state-of-the-art deep neural network and Unmanned Aerial Vehicle (UAV) technologies for highway bridge girder inspection. As a pilot study of implementing deep learning in Bridge Girder, we study the recognition, length, and location of crack in the structure of the UTC campus old garage concrete ceiling slab. A total of 2086 images of crack and non-crack were taken from UTC Old Library parking garage ceiling using handheld mobile phone and drone. After training the model shows 98% accuracy with crack and non-crack types of structures.