土木工程结构损伤诊断研究现状与展望

阐述了结构损伤诊断技术在土木工程结构中的重要性，在综述结构损伤诊断研究现状的基础上，重点介绍了用于土木工程结构的各种损伤诊断方法，并对相关问题进行了讨论和评述，最后对土木工程结构损伤诊断的未来研究方向提出了建议与展望。     

小波分析与神经网络在结构多处损伤监测中的应用

采用小波分析对获得的结构动力响应进行小波分解,根据各种响应信号对损伤的灵敏度选择损伤特征,从而识别结构多次出现损伤的时刻,实现对结构损伤时刻的监控;对结构第1层加速度响应信号做小波包分解,得到各频段能量的特征向量,作为特征参数输入到BP神经网络中实现结构多处损伤位置和程度识别。模拟算例表明,小波分析和BP神经网络联合运用能准确地诊断结构多处损伤的时刻、位置和程度,具有一定的可行性。     

图像融合在探测混凝土内部损伤中的应用研究

图像融合算法与融合规则是图像融合技术的关键课题。本文简单介绍了几类常用的图像融合算法,如加权平均、HIS变换、塔式变换,并对基于多分辨率小波变换的图像融合算法作了较详尽地论述。在系统阐述现有融合规则的基础上,修正了现有的基于边缘梯度与基于局部方差的融合规则,并相应地提出了修正后的融合规则。通过对一混凝土梁内部缺陷进行红外探测与图像融合的试验研究,采用图像融合定性与定量评价,证明了本文修正后的基于边缘梯度与局部方差的融合规则的有效性和普遍适用性。     

Structural damage detection and localisation using multivariate regression models and two-sample control statistics

This paper presents model-free damage identification and localisation methods based on two-sample control statistics as well as damage-sensitive features to be extracted from single- and multivariate regression models. For this purpose, sequential normalised likelihood ratio test and two-sample t-test are adopted to detect the change in two families of damage features based on the coefficients of four different linear regression models. The performance of combinations of these damage features, regression models and control statistics are compared through a scaled two-bay steel frame instrumented with a dense sensor network and excited by impact loading. It is shown that the presented methodologies are successful in detecting the timing and location of the structural damage, while having acceptable false detection quality. In addition, it is observed that incorporating multiple mathematical models, damage-sensitive features and change detection tests improve the overall performance of these model-free vibration-based structural damage detection procedures.     

Wavelet packet based damage detection in beam-like structures without baseline modal parameters

In this paper, a new approach for damage detection in beam-like structures is presented. The method can be used without the need for baseline modal parameters of the undamaged structure. Another advantage of the proposed method is that it can be implemented using a small number of sensors. In the proposed technique, the measured dynamic signals are decomposed into the wavelet packet decomposition (WPD) components, the power spectrum density (PSD) of each component is estimated and then a damage localisation indicator is computed to indicate the structural damage. The proposed method is firstly illustrated with a simulated beam and the identified damage is satisfactory with assumed damage. Then, the method is applied to a steel beam. The effect of damage location and the effects of wavelet type and the decomposition level are examined. The results show that the proposed method has great potential in crack detection of beam-like structures.     

混凝土大坝结构损伤检测振动法的可行性

首先对大坝安全监测与无损检测方面的研究现状、存在的问题进行了综述;在此基础上,阐述了大坝结构损伤检测振动法的基本概念及特点,对振动法在其他领域应用的研究情况作了简要介绍;并根据笔者的数值模拟研究结合其他相关研究,以及对一实际拱坝的初步测试,着重分析了用振动法进行混凝土大坝结构损伤检测的可行性,得出了用振动法检测大坝的结构损伤是完全有可能的;最后指出了用振动法进行混凝土大坝结构损伤检测还需要研究的问题.     

New method for structural damage detection based on generalised displacement

In this paper, a new structural damage detection approach based on changes in the generalised displacement is presented. The generalised displacement is first introduced, its curvature and change are then used to detect structural damage location. Compared with the conventional approach using the original displacement, the generalised displacement method is more effective and is more robust to the noise factors in this new approach. Finally, a numerical example for two-span beam is used to illustrate the effectiveness of this proposed method.     

Measuring and modelling the thermal performance of the Tamar Suspension Bridge using a wireless sensor network

A study on the thermal performance of the Tamar Suspension Bridge deck in Plymouth, UK, is presented in this paper. Ambient air, suspension cable, deck and truss temperatures were acquired using a wired sensor system. Deck extension data were acquired using a two-hop wireless sensor network. Empirical models relating the deck extension to various combinations of temperatures were derived and compared. The most accurate model, which used all the four temperature variables, predicted the deck extension with an accuracy of 99.4%. Time delays ranging from 10 to 66 min were identified between the daily cycles of the air temperature and of the structural temperatures and deck extension. However, accounting for these delays in the temperature–extension models did not improve the models' prediction accuracy. The results of this study suggest that bridge design recommendations are based on overly simplistic assumptions which could result in significant errors in the estimated deck movement, especially for temperature extremes. These findings aim to help engineers better understand the important aspect of thermal performance of steel bridges. This paper also presents a concise study on the effective use of off-the-shelf wireless technology to support structural health monitoring of bridges.     

Reliability assessment of the rotation algorithm for earthquake damage estimation

A rotation algorithm was developed that computes the slope of permanently deformed columns subjected to earthquake ground motion using micro-electro-mechanical system accelerometers. Previous studies by the authors have shown that the algorithm can provide estimates of the residual displacement in the column, and thus the story drift can be evaluated. The algorithm was validated through applications to columns that deflect in single and double curvature. Information from tests on such columns conducted at various laboratories was used to conduct the study. The optimal number of sensors and their placement on the test columns were determined through a series of numerical analyses. The accuracy of the rotation and displacement estimates is assessed under the assumptions of zero-noise sensors and perfect measurements. In this paper, the effect of measurement error and sensor noise on the predicted rotations and corresponding deformations is studied in order to provide bounds on the accuracy of the rotation algorithm and on the allowable sensor noise. The same test columns are used for the purposes of these analyses.     

基于压电陶瓷传感器的混凝土结构健康监测技术的数值模拟研究

研究混凝土梁在损伤程度相同的情况下,损伤位置与压电陶瓷(PZT)距离同结构导纳的均方根(root-mean-square deviation,RMSD)之间的关系.以及在损伤位置与PZT距离相等的情况下,不同的损伤程度与RMSD指标之间的关系.运用大型有限元通用软件建立了贴有PZT传感器的混凝土损伤粱模型,并对混凝土梁在各种损伤前后的变化进行数值分析,利用RMSD指标评定其损伤程度.结果表明在损伤程度相同的情况下,损伤判定的RMSD指标随损伤位置与PZT之间的距离增大有减小的趋势;在损伤位置与PZT距离相同的情况下,RMSD指标随损伤程度的增加有增大的趋势.同时也证明了利用有限元软件对基于PZT传感器的混凝土结构损伤检测的数值模拟是可行的.     

An improved damage diagnostic technique based on Singular Spectrum Analysis and time series models

An approach to enhance the sensitivity of the damage index using the prediction errors of autoregressive – autoregressive exogenous models by augmenting Singular Spectrum Analysis (SSA) to detect and locate minor damage is presented. Numerical simulation studies are carried out by considering a simply supported beam with single and multiple cracks. An experimental study on a reinforced cement concrete beam has been carried out to validate the technique using different levels of damage. The studies emphasise that the SSA improves the sensitivity of the damage index for detection and also localisation, handling environmental/operational variability and measurement noise.     

Application of singular spectrum analysis to structural monitoring and damage diagnosis of bridges

Singular spectrum analysis (SSA) is a novel technique and has proven to be a powerful tool for time series data analysis. Through singular value decomposition of Hankel matrix data, the time series of data can be decomposed into several simple, independent and identifiable components from singular values and singular vectors. It has already been widely applied to process climatic, meteorological, geophysical and economic data. In this paper, we demonstrate that the coupling degree of the 1st and 2nd singular values in SSA contains useful indications on the feature and composition of the analysed signal. The proposed method is successfully applied to the monitoring of structure, such as damage detection of the simulated dynamic system, experimental steel frame, bridge foundation scouring and pier settlement in the laboratory and on-site bridge monitoring during typhoon strike. The proposed algorithm is simple and suitable for structural health monitoring in the field.     

改进粒子群算法的两阶段梁式结构损伤识别

针对实际工程监测时损伤识别误差大的问题,提出一种基于改进粒子群算法的两阶段识别方案。第1阶段利用D-S证据理论融合算法进行损伤定位;第2阶段利用改进的粒子群算法,对定位结果进行修正,同时准确定量损伤。仿真算例和实验分析结果表明:由于第1阶段损伤定位减少了可能损伤单元的数量,第2阶段基于改进粒子群算法的搜索范围减小,能更准确地识别多损伤和小损伤的位置和程度,且抗噪性能良好。     

Multi-source information fusion applied to structural damage diagnosis

This study aims to import multi-source information fusion (MSIF) into structural damage diagnosis to improve its validity. Two structural damage identification methods based on MSIF are put forward, one of which is to fuse two or more structural damage detection methods by MSIF and another of which is the improved modal strain energy method by multi-mode information processing based on MSIF. Through a concrete plate experiment it is proved that, if two methods are integrated by character-level information fusion, structural initial damages can be more accurately identified than by a single method. In a simulation of a concrete box beam bridge, it is indicated that the improved modal strain energy method has a nice sensitivity to structural initial damages and a favorable robusticity to noise. These two structural damage diagnosis methods based on MSIF have good effects on structural damage identification and good practicability to actual structures.     

基于不完整频响函数的结构损伤识别方法

对土木工程结构的分析模型,在传感器不完整布置的情况下,推导基于不完整频响函数的"定性—定位—定量"的损伤识别方法。首先,利用不完备的频响函数图形的偏移程度进行损伤的定性判定;然后,建立损伤识别指标对损伤位置进行识别;最后,推导损伤定量计算方法,对损伤程度进行评估。仿真结果表明,本文提出损伤识别3阶段方法,对结构损伤探测的评估能得到比较满意的结果。     

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.     

Optimal sensor placement for structural health monitoring based on multiple optimization strategies

Careful selection and placement of sensors are the critical issue in the construction and implementation of an effective structural health monitoring system. A hybrid method termed the optimal sensor placement strategy (OSPS) based on multiple optimization methods is proposed in this paper. The initial sensor placement is firstly obtained by the QR factorization. Then, using the minimization of the off‐diagonal elements in the modal assurance criterion matrix as a measure of the utility of a sensor configuration, the quantity of the sensors is determined by the forward and backward sequential sensor placement algorithm together. Finally, the locations of the sensor are determined by the dual‐structure coding‐based generalized genetic algorithm (GGA). Taking the scientific calculation software matlab (MathWorks, Natick, MA, USA) as a platform, an OSPS toolbox, which is working as a black box, is developed based on the command‐line compiling and graphical user interface‐aided graphical interface design. The characteristic and operation method of the toolbox are introduced in detail, and the scheme selection of the OSP is carried out on the world's tallest TV tower (Guangzhou New TV Tower) based on the developed toolbox. The results indicate that the proposed method is effective and the software package has a friendly interface, plenty of functions, good expansibility and is easy to operate, which can be easily applied in practical engineering. Copyright © 2011 John Wiley & Sons, Ltd.     

基于数据融合的张弦桁架多位置损伤识别与抗噪性分析

为提高张弦桁架多位置损伤识别的准确性,将数据融合方法与损伤识别指标相结合,采用D-S证据矩阵理论融合叠加曲率模态改变率和模态柔度差曲率两项指标的损伤识别结果,提出了张弦桁架多损伤的单次融合和两阶段融合识别方法。以某实际工程缩尺简化得到的张弦桁架作为研究对象,设计了两种不同的多损伤工况,通过有限元分析获取了结构损伤前后的前三阶模态数据,并进行了损伤识别分析。结果表明：基于数据融合的多损伤识别方法能够准确地识别张弦桁架多损伤,其能够综合两种单指标的识别结果,降低甚至消除非损伤位置的干扰,有效解决了单指标识别精度低、不能全面反映损伤位置的问题;两阶段融合方法通过两次融合逐步降低了非损伤位置的干扰,识别效果优于单次融合方法。此外,分析中考虑了不同水平噪声对张弦桁架多损伤识别的影响,结果表明：基于数据融合的多损伤识别方法具有较好的抗噪能力,能够降低噪声和非损伤位置的干扰,且两阶段融合方法的抗噪能力优于单次融合方法。     

Smart structure technologies for civil infrastructures in Korea: recent research and applications

In this paper, recent research and application activities on smart structure technologies for civil infrastructures in Korea are briefly introduced. The development of structural health monitoring systems and effective retrofit/maintenance methodologies for infrastructures has become active in Korea since the mid-1990s, as the number of the deteriorated infrastructures, mostly built on the rapidly industrialised period of the 1970s, has increased very rapidly. Discussions are made on smart sensors and non-destructive technologies, monitoring and damage assessment methods for civil infrastructures, smart monitoring of geo-systems, structural control and centre-based research on smart structure technologies.     

A bridge-monitoring tool based on bridge and vehicle accelerations

Previous research on damage detection based on the response of a structure to a moving load has reported decay in accuracy with increasing load speed. Using a 3D vehicle–bridge interaction model, this paper shows that the area under the filtered acceleration response of the bridge increases with increasing damage, even at highway load speeds. Once a datum reading is established, the area under subsequent readings can be monitored and compared with the baseline reading, if an increase is observed it may indicate the presence of damage. The sensitivity of the proposed approach to road roughness and noise is tested in several damage scenarios. The possibility of identifying damage in the bridge by analysing the acceleration response of the vehicle traversing it is also investigated. While vehicle acceleration is shown to be more sensitive to road roughness and noise and therefore less reliable than direct bridge measurements, damage is successfully identified in favourable scenarios.