基于动力参数的桥梁结构损伤识别研究进展

简要介绍了基于结构动力参数的损伤识别方法的发展情况和研究现状,并在前人的基础上对其进行了分析和评述,同时根据桥梁损伤识别方法中现存的问题,展望了桥梁损伤识别未来的发展方向和趋势,以期进一步提高结构损伤识别效果并将其成功应用于工程实例。     

大跨桥梁结构损伤检测的研究进展

通过介绍国内外大跨桥梁结构损伤识别方法的研究进展,对人工检测、振动模态检测、无损检测、概率统计方法检测四种大跨桥梁结构损伤识别方法进行了分析和总结,为我国大跨桥梁结构施工企业在大跨桥梁结构施工方面的进一步发展提供借鉴和参考。     

桥梁结构损伤识别方法概述与展望

结构损伤识别是桥梁健康监测系统的核心技术,是对桥梁进行维护和加固的前提。如今人们越来越重视结构安全问题,也随之提出了各种各样的损伤识别方法。文章介绍了结构损伤识别的基本识别方法,分析现有结构损失识别方法的优点和不足,概括了结构损伤识别领域的热点问题及其发展趋势。     

现役公路桥梁结构损伤检测与评估

阐述了公路现役桥梁结构损伤检测及安全评估的重要性,介绍了目前国内外采用的桥梁检测、安全评估及剩余寿命预测的主要方法,特别是基于振动测试的结构损伤识别方法,指出桥梁损伤检测和安全评估对桥梁发展具有重要意义。     

人工智能在拉索损伤识别中的应用研究

对于拉索受力结构来说,拉索的损伤会对整个结构的运营安全性和使用耐久性构成严重的威胁和隐患,因此采用合适的手段对拉索的损伤位置和损伤程度进行快速的识别修复,对拉索类结构来说具有重要的意义。笔者对在大型桥梁结构中具有较大应用潜力的基于神经网络和遗传算法的智能损伤识别方法进行了总结分析,得出适合于大型桥梁结构进行损伤识别的智能识别方法同样适用于拉索损伤识别,但仍存在相应的局限性,需结合其他智能优化算法进一步完善,才能更好的应用于实际工程。随着大数据和人工智能技术的发展,大数据结合人工智能的拉索损伤识别方法将会是未来的发展方向。     

基于动响应数据特征的桥梁结构损伤识别

介绍了本征正交分解(Proper Orthogonal Decomposition, POD)的基本原理,探讨了POD在桥梁结构损伤识别中的应用。提出了基于动响应数据特征的桥梁结构损伤识别方法,该识别方法基于POD技术对桥梁结构在不同位置、不同时刻收集到的位移快照矩阵(Snapshot Matrix)进行本征正交分解,得到结构的本征正交模态(POMs),进而构造出损伤指标来识别结构的损伤位置及程度,实现了对桥梁结构损伤的多工况识别。并以保定黄花沟桥为例,通过数值模拟试验,验证了该方法的有效性,结果表明POD能够从空心板桥结构的振动响应数据中提取出结构的本质特征,并且提取过程简单、快捷,可为桥梁结构提供一种有效的损伤识别方法。     

基于振动的桥梁结构损伤识别方法

桥梁是一个国家最重要的建筑之一,桥梁建筑在交通行业也占据了重要的位置。我国的建桥技术发展比较早,建桥技术在世界上属于中上等水平。但是,现阶段我国有一大批桥梁都已经达到了老龄化的阶段,这就要求我们能够对桥梁的结构损伤进行识别,能够有效地保证桥体的稳定性。本文根据现阶段桥梁结构检测中存在的问题进行研究,并提出相应的桥梁结构损伤的识别方法,以期保障我国桥梁的安全,避免发生事故。     

基于静力响应的桥梁结构损伤识别

提出了一种基于静力响应量测的桥梁结构损伤识别方法。在该方法中,通过获得结构损伤前后的结构的静力响应数据,利用结构静力凝聚方法对模型进行缩阶处理,使缩阶后的理论模型的自由度与实测自由度一致。当建立了包含着损伤指数的控制方程以后,利用该方法即可得到结构损伤指数。通过数值算例证实了基于静力响应的桥梁结构损伤识别方法能对结构体系损伤进行有效识别,数值模拟结果显示文中方法具有数值结果稳定和识别精度高的优点。     

浅谈梁的损伤识别方法

以筒支梁桥为例进行了有限元仿真分析，基于柔度法研究了桥梁结构多位置损伤识别方法，结果表明柔度曲率能够进行损伤识别。     

基于曲率模态曲线变化的桥梁损伤识别

针对传统损伤识别方法仅能对损伤位置进行确定,对于损伤程度识别效果较差的问题,根据桥梁出现损伤会使曲率模态曲线产生畸变这一特点,提出一种基于曲率模态曲线变化的损伤识别方法。以曲率模态参数指标为基础,对桥梁损伤前后其曲率模态曲线的变化进行研究。采用多项式拟合和BP神经网络拟合技术,根据桥梁受损后其曲率模态曲线畸变面积的大小来反向拟合出现损伤的位置和损伤程度。以一座简支桥为例,对其设定单损伤和多损伤工况进行研究分析,根据曲率模态曲线畸变产生的部位确定结构损伤的位置,并根据曲率模态曲线的畸变大小来拟合桥梁损伤的程度。结果表明:对于实际工程中经常出现的小损伤工况,该方法识别效果较好,可用于实际工程结构的监测。     

桥梁检测技术研究

针对当前桥梁结构安全问题,在系统阐述了桥梁检测的工作内容、测试仪器及方法的基础上,着重论述了桥梁损伤识别与无损检测的新方法及其发展趋势,以期促进我国桥梁检测技术的发展。     

Structural health monitoring of a cable-stayed bridge with Bayesian neural networks

In recent years, there has been an increasing interest in permanent observation of the dynamic behaviour of bridges for long-term monitoring purpose. This is due not only to the ageing of a lot of structures, but also for dealing with the increasing complexity of new bridges. The long-term monitoring of bridges produces a huge quantity of data that need to be effectively processed. For this purpose, there has been a growing interest on the application of soft computing methods. In particular, this work deals with the applicability of Bayesian neural networks for the identification of damage of a cable-stayed bridge. The selected structure is a real bridge proposed as benchmark problem by the Asian-Pacific Network of Centers for Research in Smart Structure Technology (ANCRiSST). They shared data coming from the long-term monitoring of the bridge with the structural health monitoring community in order to assess the current progress on damage detection and identification methods with a full-scale example. The data set includes vibration data before and after the bridge was damaged, so they are useful for testing new approaches for damage detection. In the first part of the paper, the Bayesian neural network model is discussed; then in the second part, a Bayesian neural network procedure for damage detection has been tested. The proposed method is able to detect anomalies on the behaviour of the structure, which can be related to the presence of damage. In order to obtain a confirmation of the obtained results, in the last part of the paper, they are compared with those obtained by using a traditional approach for vibration-based structural identification.     

Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors

In this paper, a decentralized damage identification method using wavelet signal analysis tools embedded on wireless smart sensors (Imote2) has been proposed and experimentally validated. The damage identification analysis is decentralized by calculating discrete wavelet coefficients for acceleration in Imote2 sensors and transmitting the wavelet coefficients to a base station for damage identification through wavelet entropy indices. The wavelet entropy is modified to serve as a damage-sensitive signature that can be obtained both at different spatial locations and time stations to indicate existence of damage. It is known that wavelet-based approaches have clear advantages over Fourier transform-based ones for damage identification, since the wavelet transform allows for a wider choice of basis functions. This flexibility allows the wavelet transform to isolate changes in a signal that may be difficult to detect using other transform methods. To assess the reliability of the measurement signals, the wireless sensors have been compared with reference wired sensors. The proposed decentralized method for damage identification is verified via experimental tests using two laboratory structures: a three-story shear building structure and a three-dimensional truss bridge structure.     

Development of an integrated structural health monitoring system for bridge structures in operational conditions

This paper presents an overview of development of an integrated structural health monitoring system. The integrated system includes vibration and guided-wave based structural health monitoring. It integrates the real-time heterogeneous sensor data acquiring system, data analysis and interpretation, physical-based numerical simulation of complex structural system under operational conditions and structural evaluation. The study is mainly focused on developing: integrated sensor technology, integrated structural damage identification with operational loads monitoring, and integrated structural evaluation with results from system identification. Numerical simulation and its implementation in laboratory show that the system is effective and reliable to detect local damage and global conditions of bridge structures.     

基于定期检测与遗传算法的大跨度斜拉桥损伤识别

为了对运营中的斜拉桥进行安全性评估,首先需要根据实桥测试结果对结构损伤进行初步识别。在斜拉桥定期索力检测与主梁关键截面的应力监测结果的基础上,提出斜拉桥损伤识别的遗传算法,并编制了复杂结构损伤识别的遗传算法程序,通过各期结构检测结果之间的变化来反演检测期间结构参数的变化,据此识别结构损伤位置与程度,保证了在结构参数化有限元模型存在识别误差的情况下也能得到正确的损伤识别结果。宁波招宝山大跨度预应力混凝土独塔斜拉桥的损伤识别仿真结果表明,本文方法和程序具有较高的识别精度,并可推广应用于其它复杂结构的损伤识别。     

Dynamic test and finite element model updating of bridge structures based on ambient vibration

The dynamic characteristics of bridge structures are the basis of structural dynamic response and seismic analysis, and are also an important target of health condition monitoring. In this paper, a three-dimensional finite-element model is first established for a highway bridge over a railroad on No.312 National Highway. Based on design drawings, the dynamic characteristics of the bridge are studied using finite element analysis and ambient vibration measurements. Thus, a set of data is selected based on sensitivity analysis and optimization theory; the finite element model of the bridge is updated. The numerical and experimental results show that the updated method is more simple and effective, the updated finite element model can reflect the dynamic characteristics of the bridge better, and it can be used to predict the dynamic response under complex external forces. It is also helpful for further damage identification and health condition monitoring.     

基于有限元模型和不完备模态测试信息的结构损伤识别方法研究

本文在有限元模型的基础上,建立了一种基于不完备模态测试信息的损伤识别方法。采用Levenberg-Marquardt非线性最小二乘算法对建立的杆系结构损伤识别方程进行求解,并通过一拱桥的数值计算结果表明,该方法可以同时进行结构的损伤定位和定量研究,在测试数据不完备及一定数据噪声水平条件下,本文的方法仍有较好的损伤识别能力。     

悬索桥损伤识别概述

根据悬索桥的特点,提出了大跨度悬索桥结构损伤识别的实用方法,即整体识别和局部识别,对具体的损伤判断方法进行了论述,从而大大推动了悬索桥损伤识别的研究、应用,并指出从整体到局部对悬索桥进行损伤识别具有一定的实用性。     

基于振型参数的装配式桥梁损伤识别研究

利用结构动力特性的振型参数对桥梁进行快速损伤诊断和定位,可以提高结构性能评价与损伤诊断的效率。本文以装配式预应力混凝土T梁为算例,通过定义位移振型和曲率振型的桥梁损伤识别指标进行损伤识别,计算结果表明采用位移振型和曲率振型的方法进行损伤识别和定位效果较好。     

基于神经网络的连续梁桥结构损伤识别

探讨了神经网络技术对连续桥梁的损伤位置和损伤程度识别的方法,用有限元软件ANSYS的模态分析技术对连续梁结构的损伤情况进行模拟,得到损伤标示量并将其作为神经网络训练样本,数值仿真结果表明,该方法对于实际工程结构的损伤识别具有一定的参考意义。