A pattern recognition technique for structural identification using observed vibration signals: Linear case studies

This and the companion article summarize linear and nonlinear structural identification (SI) methods using a pattern recognition technique, support vector regression (SVR). Signal processing plays a key role in the SI field, because observed data are often incomplete and contaminated by noise. Support vector regression (SVR) is a novel data processing technique that is superior in terms of its robustness, thus it has the potential to be applied for accurate and efficient structural identification. Three SVR-based methods employing the autoregression moving average (ARMA) time series, the high-order AR model, and the sub-structuring strategy are presented for linear structural parameter identification using observed vibration data. The SVR coefficient selection and incremental training algorithm have also been presented. Numerical evaluations demonstrate that the SVR-based methods identify structural parameters accurately. A five-floor structure shaking table test has also been conducted, and the observed data are used to verify experimentally the novel SVR technique for linear structural identification.     

A pattern recognition technique for structural identification using observed vibration signals: Nonlinear case studies

A novel pattern recognition technique, support vector regression (SVR), has been introduced for linear structural parameter identification in a companion article. It is recognized that structural systems in general are designed to behave nonlinearly when subjected to extreme loading. Therefore SVR-based methods for nonlinear structural identification (SI) have been studied and they are summarized in this paper. The first method uses the SVR technique to identify nonlinear structural parameters, whereby the power parameter controlling the shape of the Bouc–Wen model is known. The second and third methods conduct nonlinear SI in the power parameter unknown condition, with the difference that the third method adopts a model selection strategy to enhance the nonlinear parameter estimation practicability. Five-story nonlinear structural systems whose restoring forces are expressed by the Bouc–Wen model are investigated to demonstrate the effectiveness of the SVR-based SI methods. Verification results show that the third method using the model selection strategy is the most efficient one for nonlinear structural parameter identification.     

Vibration based structural health monitoring: Wavelet packet transform based solution

One prominent problem for vibration-based structural health monitoring is to extract condition indices which are sensitive to damage and yet insensitive to measurement noise. In this paper, a condition index extraction method based on the wavelet packet transform (WPT) is proposed. This transform leads to the formulation of a novel condition index: wavelet packet signature (WPS). The sensitivity of the WPS to the change of structural parameters is derived and validated on a five-degrees-of-freedom spring-mass system. Results show that the WPS is significantly more sensitive to the stiffness change than the natural frequencies and the mode shapes. Its sensitivity is slightly better or comparable to that of the modal flexibility matrices depending on the location of damage. A variability analysis is also performed to study the effect of measurement noise on the proposed WPS. Results show that the WPS does not show any significant variation even under the presence of 10 dB noise. To illustrate the potential of the WPS, a damage indicator is formulated and used to monitor the health condition of the structural system. An experimental study on a three-storey frame shows that when incorporated with a statistical process control approach, the WPS-based damage indicator can distinctly identify the presence of damage in the system.     

Integration of structural health monitoring in a system performance based life-cycle bridge management framework

In this paper, an approach for integrating the information obtained from structural health monitoring in a life-cycle bridge management framework is proposed. The framework is developed on the basis of life-cycle system performance concepts that are also presented in this paper. The performance of the bridge is quantified by incorporating prior knowledge and information obtained from structural health monitoring using Bayesian updating concepts. This performance is predicted in the future using extreme value statistics. Advanced modelling tools and techniques are used for the lifetime reliability computations, including incremental nonlinear finite element analyses, quadratic response surface modelling using design of experiments concepts, and Latin hypercube sampling, among other techniques. The methodology is illustrated on an existing bridge in the state of Wisconsin.     

基于分布式传感网络的结构模态识别方法

结构的模态参数识别是结构健康监测系统的基本任务。随着工程结构的日益大型化和复杂化,振动测试时需要布置大量的传感器。传统的集中采集和处理技术将难以胜任海量数据的处理要求,采用无线智能传感器的结构健康监测系统正是应运而生的新方向,而分布式采集和处理是其特点。在无线智能传感网络拓扑结构中采用分布式算法求解结构整体振型,利用随机子空间法识别各子结构模态,结合粒子群优化算法调整子振型获取结构整体振型。通过混凝土钢管拱桥模型试验验证了分布式算法的可行性,并利用模态置信度(MAC)对比分析了由分布式模态识别方法和集中式模态识别方法得到的结果,结果表明两种算法吻合较好。     

基于SVR的城市供水管网余氯预测分析

支持向量机回归(SupportVector Regression SVR)算法是结构风险最小化原理在函数回归方面的应用。根据北方某城市供水管网余氯的人工采样数据,建立了基于SVR的余氯预测模型,并与人工神经网络、多元线性回归方法进行比较分析,结果表明:在有限样本情况下,SVR模型具有良好的泛化推广能力,各监测点模型预测平均相对误差为1.80%~8.73%,并可获得全局最优解,达到了实用要求,较好地解决了以往管网余氯小样本预测时,常常出现拟合精度高、预测效果较差的问题。     

Predicting acceleration response of super-tall buildings by support vector regression

Recovering missing data of defective sensors is an important challenge for reliability of structural health monitoring systems and misjudgment of structural conditions. The present study concerns predicting corrupted data of lost sensors by support vector regression (SVR). The method is tuned via optimizing their parameters by observer–teacher–learner-based optimization as a powerful meta-heuristic algorithm. Their performances are compared in predicting the acceleration responses of two real-world super-tall buildings: Milad Tower, located in Tehran, and Canton Tower in Guangzhou. Also the minimum required of sensors to predict the acceleration responses are investigated. The results are evaluated by five statistical indices exhibiting that the optimized SVR has sufficient capacity to predict acceleration responses of both towers with limited number of sensors. The proposed method is of practical interest as it does not require finite element modeling of the structure to derive its dynamic responses.     

Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler

Abstract:   A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so high-dimensional parameter spaces that are fatal to most sampling techniques are handled by the method, making it more practical for health monitoring of real structures. The approach also inherits the advantages of Bayesian techniques: it not only updates the optimal estimate of the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained from small-amplitude vibrations measured before and after a severe loading event, such as an earthquake or explosion.     

大型排海管道施工的健康监测

阐述了实施大型排海管道施工健康监测的必要性和迫切性,介绍了结构健康监测系统的概念、组成及其应用,以及大型排海管道施工控制的研究概况和智能控制的关键技术,最后指出了对大型排海管道施工进行健康监测的重大意义。     

A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovative approaches for structural health monitoring. Current structure inspection standards require an inspector to travel to the structure site and visually assess the structure conditions. A less time consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently. Among several possible techniques is the use of optical instrumentation (e.g. digital cameras) that relies on image processing. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this study. Several image processing techniques, including enhancement, noise removal, registration, edge detection, line detection, morphological functions, colour analysis, texture detection, wavelet transform, segmentation, clustering and pattern recognition, are key pieces that could be merged to solve this problem. Missing or deformed structural members, cracks and corrosion are main deterioration measures that are found in structures, and they are the main examples of structural deterioration considered here. This paper provides a survey and an evaluation of some of the promising vision-based approaches for automatic detection of missing (deformed) structural members, cracks and corrosion in civil infrastructure systems. Several examples (based on laboratory studies by the authors) are presented in the paper to illustrate the utility, as well as the limitations, of the leading approaches.     

Structural Damage Detection Method Using Uncertain Frequency Response Functions

Abstract:   A structural damage detection method using uncertain frequency response functions (FRFs) is presented in this article. Structural damage is detected from the changes in FRFs from the original intact state. The measurements are always contaminated by noise, and sufficient data are often difficult to obtain; making it difficult to detect damage with a finite number of data. To surmount this, we introduce hypothesis testing based on the bootstrap method to statistically prevent detection errors due to measurement noise. The proposed method iteratively zooms in on the damaged elements by excluding the elements which were assessed as undamaged from among the damage candidates, step by step. The proposed approach was applied to numerical simulations using a 2D frame structure and its efficiency was confirmed.     

A scalable IT infrastructure for automated monitoring systems based on the distributed computing technique using simple object access protocol Web-services

Automated monitoring systems are important in civil engineering industry. Existing utilities and infrastructures are monitored when their safety is of concern. These monitoring data also provide key indicators for their health and information for the life cycle management. With the prevalence of wireless sensor network (WSN) technology and the availability of various wireless communication technologies, large-scale automated monitoring systems can be deployed easily, and the cost of such systems are dropping owing to the use of Micro-Electro-Mechanical System (MEMS) technology. In this work, potential technologies for next-generation automated monitoring systems are first visited. A scalable IT (Information Technology) infrastructure for supporting large-scale automated monitoring systems capable of handling multiple projects is then proposed. Scalability, reliability, and extensibility are main considerations when designing the system and when choosing the technology for the system. Some scenarios are presented to demonstrate the characteristics and capabilities of the proposed infrastructure and information systems.     

大型桥梁结构健康监测的研究进展

桥梁管理局已经认识到,对大型桥梁实行长期结构健康监测的意义在于保证结构和运行安全,发出损坏和老化的早期预警可以避免昂贵的维修甚至灾难性的坍塌。对大型桥梁建立长期监测系统,一方面是在桥梁正常使用期内评估结构完整性、耐久性、可靠性的信息,制定最佳的维护计划,保证安全运行。     

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

As intelligent sensing and sensor network systems have made progress and low‐cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large‐scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high‐accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deep‐learning‐enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one‐dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high‐accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long‐span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.     

Improved visualization of infrastructure monitoring data using building information modeling

Abstract

With growing complex infrastructure, autonomous condition assessment of large-scale structures has garnered significant attention over the past few decades. Data-driven structural health monitoring (SHM) techniques offer valuable information of existing health of the structures, maintain the safety and their uninterrupted use under varied operational conditions by undertaking timely risk and hazard mitigation. Traditional approaches, however, are not enough to monitor a large amount of SHM data and conduct systematic decision making for future maintenance. In this article, building information modeling (BIM) is utilised as a promising computing environment and integrated digital representation platform of SHM that can organize and visualise a considerable amount of sensor data and subsequent structural health information over a prolonged period. A BIM-enabled platform is utilised to develop the proposed visualisation tool for a long-span bridge and enable automated sensor data inventory into the BIM environment. Such automated tool facilitates systematic maintenance and risk management, while avoiding manual errors resulting from visual inspection of the structures. The proposed method can be considered as a user-friendly and economic framework for condition assessment and disaster mitigation of structures from long-term monitored data.     

Large-scale structural health monitoring using composite recurrent neural networks and grid environments

The demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data-driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field-acquired data. However, the majority of unsupervised learning SHM research focuses on detecting damage in simple structures or components and possibly low-resolution damage localization. In this study, an unsupervised learning, novelty detection framework for detecting and localizing damage in large-scale structures is proposed. The framework relies on a 5D, time-dependent grid environment and a novel spatiotemporal composite autoencoder network. This network is a hybrid of autoencoder convolutional neural networks and long short-term memory networks. A 10-story, 10-bay, numerical structure is used to evaluate the proposed framework damage diagnosis capabilities. The framework was successful in diagnosing the structure health state with average accuracies of 93% and 85% for damage detection and localization, respectively.     

Long-term bridge health monitoring and performance assessment based on a Bayesian approach

This study presents a damage detection approach for the long-term health monitoring of bridge structures. The Bayesian approach comprising both Bayesian regression and Bayesian hypothesis testing is proposed to detect the structural changes in an in-service seven-span steel plate girder bridge with Gerber system. Both temperature and vehicle weight effects are accounted in the analysis. The acceleration responses at four points of the bridge span are utilised in this investigation. The data covering three different time periods are used in the bridge health monitoring (BHM). Regression analyses showed that the autoregressive exogenous model considering both temperature and vehicle weight effects has the best performance. The Bayesian factor is found to be a sensitive damage indicator in the BHM. The Bayesian approach can provide updated information in the real-time monitoring of bridge structures. The information provided from the Bayesian approach is convenient and easy to handle compared to the traditional approaches. The applicability of this approach is also validated in a case study where artificially generated damage data is added to the observation data.     

Structural health monitoring of underground facilities – Technological issues and challenges

Driven by the scarcity of land, many urban planners are seriously considering underground space to meet residential, commercial, transportation, industrial and municipal needs of their cities. Besides saving land resources, the benefits offered by underground structures include safety against earthquakes and hurricanes, and freedom from urban noise. However, owing to their unique design and construction, they call for rigorous structural health monitoring (SHM) programmes during construction and operation, especially when important structures are located nearby on the ground surface. Their continuous monitoring can serve to mitigate potential hazards, ensure better performance and facilitate in-depth understanding of the overall structural behaviour. This paper addresses major technological issues and challenges associated with structural monitoring of underground structures. A detailed review of the available sensor technologies and methods for comprehensive monitoring is presented, with special emphasis on conditions encountered underground. Practical benefits arising out of such monitoring are also highlighted, with the help of several real-life case studies involving underground structures.     

Developing a Smart Structure Using Integrated Subspace‐Based Damage Detection and Semi‐Active Control

Damage detection in large building structures has always faced challenges due to analyzing the large amount of measured data. In this article, a new damage detection approach based on subspace method is proposed to identify damages using limited output data. Also, a new scheme is presented to develop a smart structure by integrating structural health monitoring with semi‐active control strategy. If damage occurs in such a structure under severe excitations, the proposed scheme has the capability to exert necessary control forces in order to compensate for damage and reduce simultaneously the dynamic response of the structure. The reliability and feasibility of the proposed method are demonstrated by implementing the technique to two shear building structures with semi‐active control devices. Results show that the damage could be identified accurately with saving time and cost due to less computation even under noise existence; and dynamic response is significantly reduced in the smart structure.     

Monitoring Structural Health Using a Probabilistic Measure

A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to continually compute the probability of damage. In this approach, a high likelihood of a reduction in model stiffness at a location is taken as a proxy for damage at the corresponding structural location. The concept extends the idea of using as indicators of damage the changes in model parameters identified using a linear finite-element model and modal parameter data sets from the structure in undamaged and possibly damaged states. This extension is needed because of uncertainties in the updated model parameters that in practice obscure health assessment. These uncertainties arise due to effects such as variation in the identified modal parameters in the absence of damage, as well as unavoidable model error. The method is illustrated by simulating on-line monitoring, wherein specified modal parameters are identified on a regular basis and the probability of damage for each substructure is continually updated. Examples are given for abrupt onset of damage and progressive deterioration.