Investigation concerning structural health monitoring of an instrumented cable-stayed bridge

In Hong Kong, a sophisticated long-term structural health monitoring system has been devised by the Highways Department of HKSAR Government to monitor the structural performance and health conditions of three cable-supported bridges. On-structure instrumentation systems for two new long-span bridges are also being implemented. The implementation of these monitoring systems highlights the necessity for developing a monitoring-based structural health evaluation paradigm for long-span bridges. This paper describes the research directed towards this that has been conducted in the Hong Kong Polytechnic University. Taking the instrumented cable-stayed Ting Kau Bridge as a paradigm, the research covers the development of an index system and a database system for monitoring data management, the modelling of the environmental variability of measured modal properties with the intention of eliminating environmental effects in vibration-based damage detection, and the feasibility of using measured modal properties from the deployed vibration sensors for structural damage identification.     

Performance of Vibration-Based Damage Detection Methods in Bridges

Abstract:   The important advances achieved in the modal identification, sensors, and structural monitoring of bridges have motivated the bridge engineering community to develop damage detection methods based on vibration monitoring. Some of these methods have already been demonstrated under certain conditions in bridges with deliberate damage. However, the performance of these methods for damage detection in bridges has not been fully proven so far and more research needs to be done in this direction. In this article, six damage detection methods based on vibration monitoring are evaluated with two case studies. First, the dynamic simulation and modal parameters of a cracked composite bridge are obtained. Here, the damage detection methods are evaluated under different crack depth, extension of the damage, and noise level. Second, damage is identified in a reinforced concrete bridge. This bridge was deliberately damaged in two phases. In this example, damage detection methods, which do not require comparison between different structural conditions, were applied. In the first case study, evaluated damage detection methods could detect damage for all the damage scenarios; however, their performance was notably affected when noise was introduced to the vibration parameters. In the second case study, the evaluated methods could successfully localize the damage induced to the bridge.     

Soft Computing Based Multilevel Strategy for Bridge Integrity Monitoring

Abstract: In recent years, structural integrity monitoring has become increasingly important in structural engineering and construction management. It represents an important tool for the assessment of the dependability of existing complex structural systems as it integrates, in a unified perspective, advanced engineering analyses and experimental data processing. In the first part of this work the concepts of dependability and structural integrity are discussed and it is shown that an effective integrity assessment needs advanced computational methods. For this purpose, soft computing methods have shown to be very useful. In particular, in this work the neural networks model is chosen and successfully improved by applying the Bayesian inference at four hierarchical levels: for training, optimization of the regularization terms, data‐based model selection, and evaluation of the relative importance of different inputs. In the second part of the article, Bayesian neural networks are used to formulate a multilevel strategy for the monitoring of the integrity of long span bridges subjected to environmental actions: in a first level the occurrence of damage is detected; in a following level the specific damaged element is recognized and the intensity of damage is quantified.     

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.     

Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics

The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and communicates the data to the Internet where it is stored in a curated data repository. Bridge response data in the repository are autonomously processed to extract truck load events using machine learning, compensate for environmental variations using nonlinear regression and to quantitatively assess anomalous bridge performance using statistical process control.     

Critical issues,condition assessment and monitoring of heavy movable structures: emphasis on movable bridges

In this paper, a relatively less studied class of structures is presented based on the research conducted on Florida's movable bridges over the last several years. Movable bridges consist of complex structural, mechanical and electrical systems that provide versatility to these bridges, but at the same time, create intermittent operational and maintenance challenges. Movable bridges have been designed and constructed for some time; however, there are fewer studies in the literature on movable bridges as compared to other bridge types. In addition, none of these studies provide a comprehensive documentation of issues related to the condition of movable bridge populations in conjunction with possible monitoring applications specific to these bridges. This paper characterises and documents these issues related to movable bridges considering both the mechanical and structural components. Considerations for designing a monitoring system for movable bridges are also presented based on inspection reports and expert opinions. The design and implementation of a monitoring system for a representative bascule bridge are presented along with long-term monitoring data. Various movable bridge characteristics such as opening/closing torque, bridge balance and friction are shown since these are critical for maintenance applications on mechanical components. Finally, the impact of environmental effects (such as wind and temperature) on bridge mechanical characteristics is demonstrated by analysing monitoring data for more than 1000 opening/closing events.     

Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

Many bridge structures, one of the most critical components in transportation infrastructure systems, exhibit signs of deteriorations and are approaching or beyond the initial design service life. Therefore, structural health inspections of these bridges are becoming critically important, especially after extreme events. To enhance the efficiency of such an inspection, in recent years, autonomous damage detection based on computer vision has become a research hotspot. This article proposes a three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies. The convolutional neural network for image classification, object detection, and semantic segmentation are, respectively, proposed to conduct system‐level failure classification, component‐level bridge column detection, and local damage‐level damage localization. To enable efficient training and prediction using a small data set, the model robustness is a crucial aspect to be taken into account, generally through its hyperparameters’ selection. This article, based on Bayesian optimization, proposes a principled manner of such selection, with which very promising results (well over 90% accuracies) and robustness are observed on all three‐level deep learning models.     

An efficient algorithm for architecture design of Bayesian neural network in structural model updating

There has been growing interest in applying the artificial neural network (ANN) approach in structural system identification and health monitoring. The learning process of neural network can be more robust when presented in the Bayesian framework, and rational architecture of the Bayesian neural network is critical to its performance. Apart from number of hidden neurons, the specific forms of the transfer functions in both hidden and output layers are also crucially important. To the best of our knowledge, however, the simultaneous design of proper number of hidden neurons, and specific forms of hidden‐ and output‐layer transfer functions has not yet been reported in terms of the Bayesian neural network. It is even more challenging when the transfer functions of both layers are parameterized instead of using fixed shape forms. This paper proposes a tailor‐made algorithm for efficiently designing the appropriate architecture of Bayesian neural network with simultaneously optimized hidden neuron number and custom transfer functions in both hidden and output layers. To cooperate with the proposed algorithm, both the Jacobian of the network function and Hessian of the negative logarithm of weight posterior are derived analytically by matrix calculus. This is much more accurate and efficient than the finite difference approximation, and also vital for properly designing the Bayesian neural network architecture as well as further quantifying the confidence interval of network prediction. The validity and efficiency of the proposed methodology is verified through probabilistic finite element (FE) model updating of a pedestrian bridge by using the field measurement data.     

Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging-based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle-bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.     

Numerical Evaluation of Vibration‐Based Methods for Damage Assessment of Cable‐Stayed Bridges

Abstract: This study investigated a number of different damage detection algorithms for structural health monitoring of a typical cable‐stayed bridge. The Bayview Bridge, a cable‐stayed bridge in Quincy, Illinois, was selected for the study. The focus was in comparing the viability of simplified techniques for practical applications. Accordingly, the numerical analysis involved development of a precise linear elastic finite element model (FEM) to simulate various structural health monitoring test scenarios with accelerometers. The Effective Independence Method was employed to locate the best distribution of the accelerometers along the length of the bridge. The simulated accelerometer data based on the FEM analysis was employed for the evaluation of the four damage identification methods investigated here. These methods included the Enhanced Coordinate Modal Assurance Criterion, Damage Index Method, Mode Shape Curvature Method, and Modal Flexibility Index Method. Some of these methods had been previously applied only to a number of specific bridges. However, the investigation here provides the relative merits and shortcomings of the damage detection methods in long‐span cable‐stayed bridges.     

Investigation on dynamic behaviour of a full-scale reinforced concrete bridge subjected to strong earthquakes using an automated analysis platform

In this paper, the condition of a full-scale concrete bridge subjected to strong earthquakes is evaluated using vibration-based analyses. A new computational toolkit is developed in MATLAB environment for damage identification and long-term monitoring. Two types of parametric and non-parametric analysis methods are carried out on monitoring data. Moreover, a dynamic performance index is proposed based on an AutoRegressive Moving Average with eXogenous excitation (ARMAX) model. This index utilises the response predicted from an ARMAX model to evaluate bridge behaviour during strong earthquakes. Based on the results, a minor but permanent drop of 0.05?Hz in natural frequency of the first transverse and vertical modes is observed after the first strong earthquake. Also, a significant drop in frequency of the first transverse mode is observed during the two strong earthquakes. The results of the index show that the bridge did not follow linear behaviour during the two strong earthquakes as expected from a linear system. A close to flag-shaped force-displacement relationship is also observed during the first strong earthquake that can be an indication of nonlinearity in bridge behaviour. The analysis results illustrate the efficiency of the new monitoring platform for long-term monitoring and management of large datasets.     

大跨悬索桥损伤定位的自适应概率神经网络研究

由于概率神经网络(PNN)以贝叶斯概率方法描述测量数据,因而PNN在有噪声条件下的结构损伤检测方面,具有巨大的潜力。而PNN中高斯核函数的宽度,严重影响网络的泛化能力,本文提出了一种运用自适应PNN进行复杂结构的损伤定位研究方法,并与传统PNN对大跨悬索桥的损伤定位进行了仿真性能比较;同时讨论了噪声程度、特征向量简化对损伤识别精度的影响。研究发现,运用自适应PNN进行损伤定位,不仅性能优于传统PNN,而且进行特征向量简化时,可以提高损伤定位的识别精度。     

基于神经网络的结构损伤识别研究进展

为提高结构损伤识别方法的精确性和适用性,将神经网络引入到结构损伤识别中。介绍了神经网络的由来、原理和研究意义,概述了国内外基于神经网络的结构损伤识别研究进展。通过分析可以看,出用于结构损伤识别的神经网络方法有着广阔的应用前景。论文针对进一步研究的方向提出了建议。     

桁架结构损伤识别的数据融合方法研究

以数据融合技术进行桁架结构的单损伤和多损伤识别。通过研究基于频率的结构损伤理论,分析归一化的频率和损伤位置的关系;利用小波概率神经网络的算法对决策融合进行修正,建立基于小波概率神经网络的数据融合结构损伤识别模型。运用结构计算软件计算了一典型桁架结构的频率,并融合为小波概率神经网络算法的输入特征向量,并对桁架算例模型结构进行损伤识别。通过桁架不同位置的损伤情况,验证该方法的有效性,并提出工程应用中应注意的问题。研究结果表明,基于小波概率神经网络算法的数据融合技术是一种比较可靠的损伤识别方法,具有良好的工程应用前景。     

神经网络在结构工程健康监测中的应用探讨

建立结构损伤诊断子系统是建立大型工程结构智能健康监测专家系统的核心问题。人工神经网络技术可以实现结构损伤的自动识别与定位,具有广阔的应用前景。本文介绍基于人工神经网络的两级损伤识别策略,并对采用人工神经网络进行结构损伤诊断的网络输入参数与网络结构选择等关键问题进行了探讨。     

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。     

结构健康监测技术在超大跨城市钢箱梁高架桥变形监测中的应用

大跨径桥梁的轻柔化及形式与功能的复杂化,使桥梁结构健康监测技术成为国内外工程界的研究热点.通过在L32联钢箱梁内部布设分布式监测光纤,构建神经网络式桥梁智能监测系统,实现城市大跨度钢箱梁桥梁受力变形状态的在线安全监测;并通过及时处理分析监测数据,随时掌握桥梁的安全状况,能够实现长期运营阶段的在线监测.     

Frontier of continuous structural health monitoring system for short & medium span bridges and condition assessment

It is becoming an important social problem to make maintenance and rehabilitation of existing short and medium span(10-20 m) bridges because there are a huge amount of short and medium span bridges in service in the world. The kernel of such bridge management is to develop a method of safety(condition) assessment on items which include remaining life and load carrying capacity. Bridge health monitoring using information technology and sensors is capable of providing more accurate knowledge of bridge performance than traditional strategies. The aim of this paper is to introduce a state-of-the-art on not only a rational bridge health monitoring system incorporating with the information and communication technologies for lifetime management of existing short and medium span bridges but also a continuous data collecting system designed for bridge health monitoring of mainly short and medium span bridges. In this paper, although there are some useful monitoring methods for short and medium span bridges based on the qualitative or quantitative information, mainly two advanced structural health monitoring systems are described to review and analyse the potential of utilizing the long term health monitoring in safety assessment and management issues for short and medium span bridge. The first is a special designed mobile in-situ loading device(vehicle) for short and medium span road bridges to assess the structural safety(performance) and derive optimal strategies for maintenance using reliability based method. The second is a long term health monitoring method by using the public buses as part of a public transit system (called bus monitoring system) to be applied mainly to short and medium span bridges, along with safety indices, namely, “characteristic deflection” which is relatively free from the influence of dynamic disturbances due to such factors as the roughness of the road surface, and a structural anomaly parameter.     

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。     

Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method

A two-stage eigensensitivity-based finite element (FE) model updating procedure is developed for structural parameter identification and damage detection for the IASC-ASCE structural health monitoring benchmark steel structure on the basis of ambient vibration measurements. In the first stage, both the weighted least squares and Bayesian estimation methods are adopted for the identification of the connection stiffness of beam-column joints and Young’s modulus of the structure; then the damage detection is conducted via the FE model updating procedure for detecting damaged braces with different damage patterns of the structure. Comparisons between the FE model updated results and the experimental data show that the eigensensitivity-based FE model updating procedure is an effective tool for structural parameter identification and damage detection for steel frame structures.