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A structural health monitoring (SHM) system provides an efficient way to diagnose the condition of critical and large‐scale structures such as long‐span bridges. With the development of SHM techniques, numerous condition assessment and damage diagnosis methods have been developed to monitor the evolution of deterioration and long‐term structural performance of such structures, as well as to conduct rapid damage and post‐disaster assessments. However, the condition assessment and the damage detection methods described in the literature are usually validated by numerical simulation and/or laboratory testing of small‐scale structures with assumed deterioration models and artificial damage, which makes the comparison of different methods invalid and unconvincing to a certain extent. This paper presents a full‐scale bridge benchmark problem organized by the Center of Structural Monitoring and Control at the Harbin Institute of Technology. The benchmark bridge structure, the SHM system, the finite element model of the bridge, and the monitored data are presented in detail. Focusing on two critical and vulnerable components of cable‐stayed bridges, two benchmark problems are proposed on the basis of the field monitoring data from the full‐scale bridge, that is, condition assessment of stay cables (Benchmark Problem 1) and damage detection of bridge girders (Benchmark Problem 2). For Benchmark Problem 1, the monitored cable stresses and the fatigue properties of the deteriorated steel wires and cables are presented. The fatigue life prediction model and the residual fatigue life assessment of the cables are the foci of this problem. For Benchmark Problem 2, several damage patterns were observed for the cable‐stayed bridge. The acceleration time histories, together with the environmental conditions during the damage development process of the bridge, are provided. Researchers are encouraged to detect and to localize the damage and the damage development process. All the datasets and detailed descriptions, including the cable stresses, the acceleration datasets, and the finite element model, are available on the Structural Monitoring and Control website (http://smc.hit.edu.cn). Copyright © 2013 John Wiley & Sons, Ltd.

The need for large-scale bridges is constantly growing due to the enormous infrastructure development around the world. Since the 1970s many of them have been cable-stayed bridges. In 1975 the largest span length was 404 m, in 1995 it increased to 856 m, and today it is 1104 m. Thus the economically efficient range of cable-stayed bridges is tending to move towards even larger spans, and cable-stayed bridges are increasingly the focus of interest worldwide. This book describes the fundamentals of design analysis, fabrication and construction, in which the author refers to 250 built examples to illustrate all aspects. International or national codes and technical regulations are referred to only as examples, such as bridges that were designed to German DIN, Eurocode, AASHTO, British Standards. The chapters on cables and erection are a major focus of this work as they represent the most important difference from other types of bridges. The examples were chosen from the bridges in which the author was personally involved, or where the consulting engineers, Leonhardt, Andra and Partners (LAP), participated significantly. Other bridges are included for their special structural characteristics or their record span lengths. The most important design engineers are also presented. Note: The lecture videos which are attached to the print book on DVD are not part of the e-book.

In order to improve the efficiency of bridge construction, a bridge construction monitoring method based on MIDAS civil finite element software is designed in this paper. Taking the construction monitoring of the main bridge of Huangshuihe bridge on Tonghai road in Xining City as an example, the monitoring parameters of the top deflection of the main tower and the simulation of temperature change during construction are described in detail. The effectiveness of the method is verified by the comparative analysis of theoretical data and measured data.

Summary Monitoring strain data provides possibilities for the fatigue assessment of the critical components in real structures under the operational conditions. This paper presents a case study on the investigation of fatigue performance of welded details. Long‐term monitoring strain data of 4 years between 2006 and 2009 collected by the structural health monitoring system mounted on the Runyang Suspension Bridge and Runyang Cable‐stayed Bridge are utilized. The study focuses on two aspects: (i) the effects of different strain components in the raw strain data on the fatigue damage assessment and (ii) the necessity of long‐term strain measurement for fatigue evaluation. The results indicate that temperature effect on the stress range spectrum is negligible. The enormous low‐level stress cycles caused by random interference would result in erroneous equivalent stress ranges and number of cycles, and thus should be eliminated from the stress range spectrum. Through in‐depth analysis of monitoring strain data in three special months and in 3 years, respectively, it is revealed that short‐term data monitored in a few days and medium‐term data monitored in a few months are not adequate enough to reveal the actual fatigue behaviors of steel bridges and would give inaccurate fatigue life prediction. Finally, the fatigue life predictions of two types of welded details by considering traffic flow growth are carried out. Lessons learned from the long‐term monitoring are expected to enhance our understanding in fatigue performances of steel bridges. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Structural health monitoring (SHM) strategies should ideally consist of continuous on‐line damage detection processes, which do not need to rely on the comparison of newly acquired data with baseline references, previously defined assuming that structural systems are undamaged and unchanged during a given period of time. The present paper addresses the topic of SHM and describes an original strategy for detecting damage in an early stage without relying on the definition of data references. This strategy resorts to the combination of two statistical learning methods. Neural networks were used to estimate the structural response, and clustering methods were adopted for automatically classifying the neural networks' estimation errors. To ensure an on‐line continuous process, these methods were sequentially applied in a moving windows process. The proposed original strategy was tested and validated on numerical and experimental data obtained from a cable‐stayed bridge. It proved highly robust to false detections and sensitive to early damage by detecting small stiffness reductions in single stay cables as well as the detachment of neoprene pads in anchoring devices, resorting only to a small amount of inexpensive sensors. Copyright © 2015 John Wiley & Sons, Ltd.

Summary A method for identifying the distribution of moving heavy vehicle loads is proposed for cable‐stayed bridges based on a sparse l1 optimization technique. This method is inspired by the recently developed compressive sensing (CS) theory, which is a technique for obtaining sparse signal representations for underdetermined linear measurement equations. In this study, sparse l1 optimization is employed to localize the moving heavy vehicle loads of cable‐stayed bridges through cable force measurements. First, a simplified equivalent load of vehicles on cable‐stayed bridges is presented. Then, the relationship between the cable forces and the moving heavy vehicle loads is established based on the influence lines. With the hypothesis of a sparse distribution of vehicle loads on the bridge deck (which is practical for long‐span bridges), moving heavy vehicle loads are identified by minimizing the ‘l2‐norm'of the difference between the observed and simulated cable forces caused by the moving vehicles penalized by the ‘l1‐norm’ of the moving heavy vehicle load vector. A numerical example of an actual cable‐stayed bridge is employed to verify the proposed method. The robustness and accuracy of this identification approach (with measurement noise for multi‐vehicle spatial localization) are validated. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARYStay cables are critical components in bridges. However, stay cables suffer from severe fatigue damage. Therefore, a monitoring technique to obtain the time history of the tension in stay cables is important. Because the acceleration of stay cables is readily measurable, approaches to identify cable tension based on frequency analysis and monitored cable acceleration have been widely investigated and used in practice. However, this type of approach can only identify a time‐invariant tension of a stay cable over a specified duration, not the time‐varying tension. This paper proposes an approach to identify the time‐varying tension of stay cables by monitoring cable accelerations. The tension variation in stay cables is caused by vehicles passing over the bridge. The real‐time identification algorithm that determines the time‐varying tension of stay cables is proposed using an extended Kalman filter based on both the transversal monitored acceleration at a single location on the cable and the monitored wind speed on the bridge, where the time‐varying tension is a state variable that is identified. A stay cable from the Nanjing Yangtze River No. 3 Bridge was used for the numerical study. The time‐varying tension of the stay cable can be identified when either a single vehicle or multiple vehicles pass over the bridge. The robustness of the proposed approach is also investigated through deviations in the initial tension, initial displacement, and velocity of the stay cable. An experiment was conducted on a scaled stay cable with time‐varying tension excited by wind. The time‐varying cable tension of the cable was identified by the proposed approach and compared with the real time‐varying cable tension. The identification accuracy and robustness of the proposed approach is verified through the experiment and numerical study. Copyright © 2013 John Wiley & Sons, Ltd.

Summary Structural health monitoring is conceived to detect abnormal behaviors in structural systems. A highly non‐linear objective function built on the discrepancies between true and generated modal features can be minimized for this purpose. After a finite element discretization is built, the design variables are chosen, and the optimization problem solved. Two bio‐inspired metaheuristic tools, namely the artificial bee colony and the firefly algorithm, are employed to proceed toward the global minima. Comparing both identified and analytical stiffness matrices, the damage localization is performed. These methods are tested on a cable‐stayed bridge placed in northern Italy. The efficiency of these tools is compared. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Ship collisions threaten the safety of bridges over navigable waterways in modern times. Postcollision damage and condition assessment is thus of significant importance for decision making on whether closure of bridge to traffic is necessary and for planning the consequent bridge strengthening or retrofitting. Online structural health monitoring systems provide a unique approach to monitor bridge responses during ship collisions and detect the structural damage. The damage information contained in the monitoring data, which is critical for damage detection, however, is largely dependent on the sensor layout. In this paper, an optimal sensor placement method targeting postcollision damage detection of bridges is proposed for selecting the optimal sensor set so that the measured data are most informative for damage detection. The sensor configuration is optimized by a multi‐objective optimization algorithm, which simultaneously minimizes the information entropy index for each possible ship‐bridge collision scenario. One advantage of the proposed method is that it can handle the uncertainty of ship collision position. It also guarantees a redundancy of sensors for the most informative regions and leaves a certain freedom to determine the critical elements for monitoring. The proposed method is applicable in practice to determine the sensor placement, prior to field testing, with the intention of identifying postcollision damage. The cable‐stayed Ting Kau bridge in Hong Kong is employed to demonstrate the feasibility and effectiveness of the proposed method.

To explore the internal resonance between different cables and beam in the cable-stayed bridge, a double-cable-stayed beam model is established in this paper. Unlike previous studies, the two cables have different frequencies. Therefore, it is possible to investigate the in-plane one-to-one-to-two internal resonance among the second-order mode of the beam (global mode) and the first-order modes of two cables (local modes) when the beam is subjected to the primary resonance excitation. Firstly, the partial differential equations (PDEs) governing the behaviors of the beam and cables are discretized using the Galerkin method, resulting in ordinary differential equations (ODEs). It is worth noting that two various trial functions of the cables, namely, the superposition of dragging function with sinusoidal function (TF1) and real modal function (TF2), are adopted to compare the differences in nonlinear behaviors, which is also the motivation of this study. Then, the multiple time scale method is employed to solve the ODEs, and in this way, the corresponding modulation equations are derived. Based on the obtained modulation equations, the steady-state solutions are acquired so as to discuss the nonlinear characteristics of the system using various trial functions. Meanwhile, the accuracy of the perturbation results is verified by solving the ordinary differential equations directly with the Runge–Kutta method. Finally, several interesting findings and conclusions are given.