Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

A Study on Health Monitoring of Structural Damages for Two Stories Model by Using Vibration Test

Many structures in Japan were built after the war at a revival term or rapid economic growth. These structures have been reached a life in recent years and it is economically not affordable to conduct repair and reconstruct these structures only with a possibility of being damaged. This paper presents an approach to detect the structural damages for two degrees of freedom (2DOF) model. In this study, we conducted Microtremor measurement, free vibration test and vibration test. The 2DOF model was demonstrated the feasibility of using the proposed approach to damage detection of structural member.

Influence of System Uncertainties on Structural Damage Identification through Ambient Vibrations of Steel Structures

The practical difficulties presented by forced vibration testing of large steel structures, such as tall buildings, transmission lines or bridges, led to an increased interest in structural monitoring through ambient vibrations, which usually allows the proper identification of modal properties, natural frequencies, damping and modes of vibration. Changes in these modal properties constitute an indication of structural damage, which may then be assessed on the basis of experimental evidence. The authors proposed an approach to determine the so-called damage damping and stiffness matrices, which are essential to identify the location and intensity of damage. No restrictions were introduced on the damping matrix of the system. The approach requires ambient vibration data of all relevant coordinates used in the structural model, which are processed employing the SSI method. In practice, the identification method is seriously hampered by ambient factors such as temperature or humidity. In general those effects must be filtered out in other to obtain a reliable diagnosis of damage, approach that demands long term monitoring. In this paper, an alternative approach is explored, based on the introduction of error damping and stiffness matrices. Data on both matrices is generated on the basis of observed variations of structural member stiffness and damping caused by ambient factors. The influence of this uncertainty on the identified spectral properties is assessed by simulation.

Application of clan member signal method in structural damage detection

It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.

Analysis of blasting damage in adjacent mining excavations

Following a small-scale wedge failure at Yukon Zinc＇s Wolverine Mine in Yukon, Canada, a vibration monitoring program was added to the existing rockbolt pull testing regime. The failure in the 1150 drift occurred after numerous successive blasts in an adjacent tunnel had loosened friction bolts passing through an unmapped fault. Analysis of blasting vibration revealed that support integrity is not compromised unless there is a geological structure to act as a failure plane. The peak particle velocity（PPV） rarely exceeded 250 mm/s with a frequency larger than 50 Hz. As expected, blasting more competent rock resulted in higher PPVs. In such cases, reducing the round length from 3.5 m to 2.0 m was an effective means of limiting potential rock mass and support damage.

Application of deep autoencoder model for structural condition monitoring

Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.

Cross spectral energy method for damage assessment of the cable-stayed bridge under operational conditions

The new cross spectral energy method(CSEM)is proposed for maintaining cable-stayed bridge safe-ty by the measurable output-only vibration response.Damage assessment of real structures is limited by aseries of problems such as unknown ambient excitation forces,errors introduced by system identification,incomplete dynamic measurements,etc.Thus the methodology based on cross spectral energy of eachsubstructure member is derived to meet these challenges.The novel damage index does not require anymodal or parameter identification technology.It can be calculated directly from vibration test data.In or-der to evaluate the efficiency of the presented methodology,a three dimensional(3D)actual cable-stayedbridge model with one or more damaged positions under operational conditions was studied.In order totestify the reliability of damage detection method,the response data was polluted by the random noise.Itis proved that the proposed method can successfully localize all damage cases even in noisy data.Withthe help of examples,the CSEM can potentially be applied as a nondestructive evaluation technique(NDT)for on-line health monitoring of cable-stayed bridges with minimum disruption of its operations.

FBG传感器在起重机械局部损伤监测中的应用研究

光纤布拉格光栅(Fiber Bragg Grating,FBG)传感器自身应变传递特性和敏感范围波动较大,在应用过程中缺少结合被测结构性能和FBG传感器本身结构特点的应用方法。为此,研究FBG传感器在起重机械局部损伤监测中的应用。根据起重机械的内部振动状况构建损伤模型,在FBG传感器光纤的同一位置放置两个不同的Bragg光栅,并根据波长变化采集局部损伤数据,将采集到的数据输入自回归滑动平均模型(Autoregressive Moving Average Model,ARMA)中,提取损伤状态,完成起重机械局部损伤监测。实验结果表明,所提方法的反馈时间低于0.25 ms,监测到的应变数据与实际应变数据差距相近,监测的损伤部位与设定损伤部位一致,证明该方法监测反馈效率和监测精度高,监测效果好,适用于各种环境下的起重机械局部监测工作。

基于BP神经网络的高桩码头基桩损伤识别研究

针对高桩码头基桩的损伤识别问题,基于BP神经网络开展了损伤定位研究。传统损伤定位方法在识别过程中受到人为主观因素的干扰,且对于只有一阶模态数据的情况定位效果有限。为克服这些问题,构建了不受人为因素影响的损伤定位神经网络,以第三类损伤指标ULSC和δFC作为训练样本,实现了对基桩局部损伤的准确定位。在建立合理的高桩码头有限元模型的基础上,构建了基于BP神经网络的损伤定位模型,并使用ABAQUS模拟数据和实测振动信号数据进行训练和测试。实验结果表明,该神经网络模型具有较高的定位准确性和鲁棒性,在不同损伤工况和10%噪声水平下仍表现优异。

基于数字孪生和深度学习的结构损伤识别

土木工程实际结构损伤状态的时间跨度通常只占总生命周期的一小部分。为解决传统基于数据驱动的结构损伤识别方法缺乏足够多的损伤训练数据的问题,提出结合数字孪生和深度学习的结构损伤识别方法,并应用于实际工程。该方法利用数值仿真模型和在线监测数据构建结构的数字孪生,以获得不同损伤工况下结构动力响应的“大数据”;为了摆脱对外激励信息的依赖,应用经验模态分解法和传递率函数对得到的数据进行预处理;将预处理后的固有模态传递率函数数据作为深度学习的输入进行训练,实现结构的损伤识别。为验证方法的有效性,对实际结构未经训练的监测数据进行分析,结果表明,该方法泛化能力良好,能够有效识别结构损伤状况。通过数字孪生技术解决了传统方法数据匮乏的问题,不需要任何地震信息,利用固有模态传递率函数数据训练的深度神经网络仍能保持较高的损伤识别准确率,二者结合可以使工程结构健康监测更为主动、可靠、高效。

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.

预留核心土法隧道爆破围岩损伤与振动特性研究

目的 研究预留核心土法爆破开挖影响下的山岭隧道围岩损伤与振动特性,为爆破参数优化以及隧道加固设计提供理论参考。方法 依托新疆阿克特蒲2号左洞隧道工程,借助ANSYS/LSDYNA软件建立隧道爆破开挖数值模型,基于现场实际开挖轮廓和振速监测数据验证数值模型的合理性,进而分析围岩累积损伤特性、振速分布和振速衰减规律。结果 爆破结束后,隧道轮廓平整,预留核心土上部损伤严重;隧道拱顶y方向、拱肩z方向及拱脚x方向的振速最大;距离掌子面越近,采用萨道夫斯基经验公式预测的振速结果误差越大。结论 为保证结构稳定性,在下次爆破施工前可以通过减少掏槽孔、辅助孔的装药量或者调整其孔网参数来减小爆炸荷载,沿最大振速方向进行重点加固;采用萨道夫斯基经验公式预测距离掌子面10 m内的振速时,避免仅用z方向距离近似为爆心距大小,应同时考虑三个方向的距离。

铁路隧道初期支护混凝土爆破累积损伤特征

依托京张高铁草帽山隧道工程,开展爆破振动监测及声波测试。爆破振动监测结果表明:隧道初期支护对应的质点峰值振动速度满足萨道夫斯基公式。声波测试结果表明:随着爆破次数的增多,爆破累积损伤逐渐增大。但随着爆破距离的增大,累积损伤的增量却呈逐渐减小的趋势。通过数据分析及曲线拟合的方式分别得到爆破施工对应的累积损伤范围(R_(cr))及临界爆破振动速度(PPV_(cr))。数值分析结果显示,累积损伤范围(R_(cr))与对应的临界爆破振动速度(PPV_(cr))之间存在良好的指数函数关系,通过设定相应的爆破振动速度阈值可以达到定量控制爆破损伤范围的目的。

强噪声小样本条件下基于图卷积神经网络的结构损伤识别

基于深度学习的结构损伤识别主要通过捕捉数据特征及内部规律来实现。训练样本不足、噪声干扰均可能导致有效特征及内部规律的挖掘失败。从数据中尽可能挖掘更多的信息用于识别损伤尤为重要。文中提出了基于图卷积神经网络的结构损伤识别方法。首先,为了能够提取更多特征,即同时考虑不同位置传感器之间的相关性和各个传感器数据的自身特性,通过图构造方法将一维振动数据转换为图数据。然后采用图卷积神经网络提取图样本的数据特征并实现快速分类,完成损伤识别的目的。采用卡塔尔大学看台结构模型来验证所提方法的可行性及可靠性,并探讨噪声程度、样本个数、构图方式及相关图卷积网络参数对识别结果的影响。结果表明:与一维卷积神经网络相比,图卷积神经网络模型在强噪声、小样本的情况下具有较高的损伤识别精度。构图方式及图池化方法对识别结果有一定的影响,Path构图方式与Topk池化的识别结果较为稳定且高于其他组合形式。

某建筑工程预应力混凝土管桩施工振动监测与承载力分析

为探究预应力混凝土管桩施工对周围环境的影响,以福州市长乐区某建筑工程为背景,在考虑场地土类型影响的基础上进行预应力混凝土管桩施工振动监测,并通过竖向抗压静载试验分析预应力混凝土管桩承载力是否满足设计要求。结果表明,预应力混凝土管桩施工对周围环境的影响与场地土类型有关,中硬土上管桩施工对周围环境的影响最大,中软土次之,软弱土最小;所选试验桩的Q-s曲线均为缓变型曲线,且试验结果均满足设计要求。

基于波传播特性的钢板桩振动环境效应评价和预测

通过三轴测振仪,对3个典型地区不同厚度软土的项目钢板桩振动沉桩进行监测,提取了距离沉桩点不同距离处地表的径向、切向和竖向的振动加速度,并利用Mathematica和SeismoSignal软件,分析了振动峰值加速度和振动峰值速度随距离沉桩点不同距离的变化趋势。结果表明,软土地区的振动峰值加速度和振动峰值速度均在距离沉桩点一定距离处发生突变,造成该突变的原因和压缩波传到地面有关,突变点与沉桩点之间的最短距离即为临界距离。采用GDS⁃RCA共振柱三轴测振仪,对3个场地振动沉桩前后距离沉桩点不同距离处的土体剪切模量进行测试,得到影响临界距离的因素,并通过测试数据得到临界距离处振动峰值速度差值与软土层厚度、土阻抗之比和临界距离之间的关系和改进的振动峰值速度预测方程;改进后的方程不仅真实地反映了不同软土层厚度、土层参数以及临界距离对振动峰值速度的影响,而且更真实地反映了软土地区振动衰减规律。

建筑基桩动力特性分析及健康监测技术研究

通过深入研究了建筑基桩的动力特性,并探讨了相关的健康监测技术。介绍建筑基桩动力学的理论基础。阐述动力特性分析方法。以及探讨基桩健康监测技术的原理,聚焦于动力作用下的健康诊断技术,涉及动态应变数据分析、疲劳损伤评估方法,以及预警机制与决策支持系统的建立。旨在为建筑基桩的安全运营提供科学有效的监测与诊断手段。

基于振动的结构损伤识别方法的近期研究进展

基于振动的损伤识别方法是少有的几种全局损伤检测方法之一 .文中介绍了该方法的现状及发展 ,对有关方法进行了总结和评述 。

海洋平台振动监测技术试验研究

为保证海洋平台服役期间安全运行,以实验室海洋平台模型为研究对象,开展有关振动监测技术的试验研究。通过预制损伤等物理手段,模拟海洋平台模型完好与损伤状态。使用加载设备对平台模型加载,采集其在不同加载角度、频率与增益下的动力响应。对振动传感器校准后,利用传感器、信号调理仪以及信号采集软硬件设备,组成监测系统。分析试验数据,调整相关试验参数,选取最佳信号采集状态。选取双谱分析法对试验数据进行信号处理,得出平台模型完好与损伤状态的数据变化。结果表明:根据振动监测数据的双谱模量与频率分布,能够识别损伤。

深基坑附近房屋出现裂缝的施工监测分析

针对明挖隧道深基坑旁6#房屋出现明显的墙体裂缝,结合施工设计开展相应的安全监测工作,监测内容主要有房屋裂缝、房屋沉降、土体深部位移、支护桩水平位移及爆破振动等。根据对典型裂缝测点和房屋周边环境的跟踪监测成果,对裂缝成因进行分析,推断出施工动荷载是房屋裂缝出现较快扩展的主要因素,冲孔桩施工使房屋墙体力学性能弱化,爆破开挖施工造成墙体出现裂缝,连续几天爆破振速超过1.0 cm/s时产生的已有裂缝将持续扩展;而环境温差对裂缝宽度变化有一定的影响。将分析成果应用于后续施工中,对7#房屋附近区域的爆破作业提出严格的振速控制要求,使7#房屋裂缝扩展速率得到很好的控制。