Neural network method based on a new damage signature for structural health monitoring

Adopting wide-band Lamb wave based active monitoring technology, this study focuses on a neural network method based on a new damage signature for on-line damage detection applied to thin-walled composite structures. Honeycomb sandwich and carbon fiber composite structures are studied. Two kinds of damage are considered: delamination and impact damage. A new damage signature is introduced to determine the presence and extent of damage in composites, while eliminating the influence of different distances between the actuator and sensor. Neural network method is researched to take advantage of this new damage signature combined with several other signatures to decide the damage mode. Kohonen neural network is developed. The proposed method is shown to be effective, reliable, and straightforward for the specimens considered in the present study, which are composed of different materials and suffer various levels of damage.     

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.     

Traffic load modelling based on structural health monitoring data

Live and fatigue load models are foundations for the life-cycle design of highway bridges. Many highway bridges are now equipped with structural health monitoring (SHM) systems, which provide valuable data to establish load models. In this paper, traffic load models of the Binzhou Yellow River Highway Bridge are developed based on the field measurement of vehicles by an existing SHM system. The probabilistic distribution model and extreme value distribution of gross vehicle weight are statistically analysed using the monitoring data. The results indicate that they follow the bimodal lognormal and Gumbel distributions, respectively. The fatigue load spectrum is also studied. The logistic model is employed to predict the long-term traffic volume, and its parameters of the logistic model are updated using the monitored traffic volume. The combination of the fatigue load spectrum and the traffic volume forecast using the updated logistic model provides a load model for estimation of fatigue damage evolution of bridges.     

Application of neural networks for the structural health monitoring in curtain-wall systems

In a curtain-wall system, the main and the most possible cause of failures, is the total or partial destruction of its connections with the bearing structure. The present paper deals with the respective health monitoring problem and proposes an Artificial Neural Network (ANN) in order to identify possible imperfections in a typical curtain-wall system. Several Finite Element (FE) models of the curtain-wall system were developed and a parametric analysis was carried out dealing with the loss of rigidity in the aforementioned connections. During the numerical investigations, datasets containing the deflections of the columns of the curtain-wall structure were computed. The obtained results were used to create the Patterns Database, which, in turn, was used as the input for the training of the ANNs. Due to the relatively small number of training patterns, the regularization technique was also employed in order to improve the network generalization. The number of sensors and their optimal placement for appropriate network training were investigated. A wide variety of network architectures was studied and their influence on the network training was analyzed. The obtained results showed that ANNs can be an efficient method for the identification and localization of imperfections in curtain-wall systems.     

Optimization of bridge maintenance strategies based on structural health monitoring information

Highway bridges are subjected to strength degradation processes. Under budget constraints, it is important to determine the best maintenance strategies. Optimized strategies, based on prediction models, are already considered for the maintenance and operation of highway bridges. Prediction models are updated both in space and time by using non-destructive testing methods. Nevertheless, there is an urgent need for the efficient inclusion of structural health monitoring (SHM) data in structural assessment and prediction models. Indeed, SHM allows keeping strength degradation processes under control and should be included in life-cycle cost models. The lifetime reliability of structures is characterized by survivor functions. The SHM data enable to update the probability density function of time to failure through a Bayesian process. The aim of this paper is threefold: (a) to include SHM data in a bridge life-cycle cost analysis, (b) to determine optimal maintenance strategies based on monitoring information, and (c) to show the benefits of SHM. Optimal strategies are determined considering the cases without and with including monitoring results; the benefit of monitoring is then highlighted. The proposed concepts are applied to the I-39 Northbound Bridge over the Wisconsin River in Wisconsin, USA. A monitoring program of that bridge was performed by the ATLSS Engineering Research Center at Lehigh University.     

Reliability assessment of cable-stayed bridges based on structural health monitoring techniques

This paper presents the reliability analysis approach of long-span cable-stayed bridges based on structural health monitoring (SHM) technology. First, the framework of structural reliability analysis is recognised based on SHM. The modelling approach of vehicle loads and environmental actions and the extreme value of responses based on SHM are proposed, and then models of vehicle and environmental actions and the extreme value of inner force are statistically obtained using the monitored data of a cable-stayed bridge. For the components without FBG strain sensors, the effects and models (extreme values) of dead load, unit temperature load, and wind load of the bridge can be calculated by the updated finite element model and monitored load models. The bearing capacity of a deteriorated structure can be obtained by the updated finite element model or durability analysis. The reliability index of the bridge's critical components (stiffening girder in this study) can be estimated by using a reliability analysis method, e.g. first order reliability method (FORM) based on the models of extreme value of response and ultimate capacity of the structure. Finally, the proposed approach is validated by a practical long-span cable-stayed bridge with the SHM system. In the example, reliability indices of the bridge's stiffening girder at the stage after repair and replacement after 18 years of operation, and the damaged stage are evaluated.     

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.     

结构健康监测与诊断的研究分析

阐述了土木工程结构健康监测与诊断的相关概念,介绍了一个完整的监测系统的组成,对传统与现在的监测手段进行了对比,最后指出其现存的问题,并对未来发展进行了展望。     

桥梁健康监测研究及发展趋势

回顾了桥梁健康监测的由来，阐述了桥梁健康监测的概念和监测系统的组成，介绍了桥梁健康监测的现状和最新研究方法，对将来的发展亦做了展望。     

广州黄埔大桥南汊桥健康监测方案

采用有限元通用程序ANSYS对该桥进行静力与动力分析确定传感器的布设位置，并依据计算的前20阶动力特性结果，采用遗传算法（GA）进行传感器的优化布设，选用精度较高的光纤光栅传感器，能够满足对桥梁长期监测的需要。     

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.     

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.     

压电材料在土木工程结构健康检测中的应用

本文对压电材料在土木工程结构健康检测中的应用进行了综述，分析了阻抗法和波动法的基本原理、研究进展、优点和存在的问题等。这二种方法都是主动的检测方法，它们适合干各种土木工程结构，并且压电材料／器件能解决如何激励结构的问题。     

新型桥梁健康监测与安全评估的现状与展望

通过对桥梁结构无损检测方法的分类讨论,较为系统的综述了近20年来国内外结构损伤识别的研究和应用情况,同时对桥梁安全评估的主要方法进行了介绍,最后对有待进一步研究的问题进行了展望。     

大型铁路站房结构健康监测研究现状评述

铁路站房作为大型公共建筑,具有结构体系复杂、人流高度密集、使用年限长等特点,一旦结构失效,将会造成严重的社会影响。为了有效监测站房结构的健康状况,及时发现站房结构的损伤,最大程度地保障铁路站房的结构安全,有必要对铁路站房结构进行健康监测。结合大型铁路站房工程案例,总结大型铁路站房的组成和结构特点,介绍站房健康监测系统的组成,考虑施工和运营2个阶段,从屋面层、无柱雨棚和承轨层3个部分,分析大型铁路站房主要监测对象和监测内容,并指出健康监测在铁路站房结构应用中有待解决的问题,以期促进健康监测在大型铁路站房结构中的应用和发展。     

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.     

Long-term structural health monitoring of the Cleddau bridge: evaluation of quasi-static temperature effects on bearing movements

This paper illustrates how long-term measurements can be analysed to understand bridge behaviour under changing environmental conditions and how the developed understanding can help explain the performance of its critical components. Measurements from the Cleddau bridge, a structure that has been continuously monitored for more than two years, are used to investigate thermal effects in steel box-girder bridges and, in particular, their bearings. Observed temperature distributions are very different to the recommended distributions in design codes (BS EN 1991-5: 2003). These temperature distributions create plan bending of the box girder, which in turn impose forces at the bearings that have contributed to its wear. This paper investigates bearing movements of the bridge using numerical models, and estimates the resulting forces at the supports. A physics-based model of the bridge is created to which temperature distributions inferred from in situ measurements are supplied as input. Model predictions are validated against measured deformations at the bearings. Subsequently the model is used to predict forces at the bearings due to plan bending and bearing locking. Results quantify the impact that thermal effects have on the performance of the bearings. They also highlight the significance of considering a range of temperature distribution scenarios that go beyond those given in the design codes in order to reliably evaluate thermal effects at the design stage.     

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.     

应用压电陶瓷传感器于桥墩的损坏诊断

该研究主要为运用压电传感器进行宜兰牛斗桥现地混凝土桥墩结构的健康诊断。所使用的压电传感器为利用压电陶瓷材料所制成,利用基本波动力学及压电材料可发射应力波亦可接收应力波的特性,进行现地混凝土桥墩结构的健康诊断研究。结构健康诊断为将压电传感器埋入RC桥墩结构内,当试体受到破坏时,由压电传感器发射应力波及由不同位置的压电传感器来撷取应力波,利用应力波振幅随着结构破坏程度、裂缝增加而减少振幅的特性,可知RC桥墩结构物的损坏程度。利用数值分析得到损坏指针,即可判断出结构物损坏程度,其量测结果随着结构物的损坏程度越严重,损坏指标也越高。当桥墩结构物达到严重破坏程度时,所量测到的损坏指标也相当接近最大值。该研究为压电传感器第一次应用于现地桥墩结构,除可验证压电材料用于实际结构物检测的可行性,判断混凝土结构的健康程度,另实为土木结构非破坏检测上的一种新的检测方法。     

Non-contact structural health monitoring of a cable-stayed bridge: case study

In this article, the condition assessment of a cable-stayed bridge using remote sensing is presented. The displacement influence line (DIL) of the bridge under the live load tests is measured for a discrete number of target points. Three different remote sensing techniques including, laser scanning, terrestrial robotic total station and digital levelling are adopted for this purpose. It is demonstrated that DIL obtained by non-contact system is capable of identifying an emulated damage in an actual operating system. The contribution of the work is fourfold. First, a damage index based on the displacement profile of the bridge under the weigh-in-motion is extracted from the non-contact sensing system. Second, our study compares three different remote sensing techniques, namely, digital levelling, robotic total station and laser scanning and uses the measurements to validate the finite element model. Third, the effectiveness of the proposed method for structural damage identification is validated in a real-world large-scale operating structure. Finally, it is validated that strain-based influence line is highly likely to misidentify damage especially when the location of damage is not in the close proximity of the sensor; however, DIL is a better damage indicator even if damage occurs far from the measurement point.