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.     

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 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.     

Development of an integrated structural health monitoring system for bridge structures in operational conditions

This paper presents an overview of development of an integrated structural health monitoring system. The integrated system includes vibration and guided-wave based structural health monitoring. It integrates the real-time heterogeneous sensor data acquiring system, data analysis and interpretation, physical-based numerical simulation of complex structural system under operational conditions and structural evaluation. The study is mainly focused on developing: integrated sensor technology, integrated structural damage identification with operational loads monitoring, and integrated structural evaluation with results from system identification. Numerical simulation and its implementation in laboratory show that the system is effective and reliable to detect local damage and global conditions of bridge structures.     

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.     

基于变形的城市桥梁健康监测系统初探

国内外不断发生的桥梁跨塌事故,引起人们对桥梁健康监测的日益重视。大跨径桥梁健康监测系统通常大而全且成本高昂,探索研究适合城市桥梁的健康监测系统具有一定的理论价值和现实意义。本文基于某城市桥梁的变形监测和定期检测,尝试提出基于变形的城市桥梁健康监测系统,工程实践表明,该系统操作方便,投资少,能及时进行桥梁安全与预警,可供目前广泛开展的城市桥梁健康状态评估参考借鉴。     

Structural health monitoring system of the long-span bridges in Turkey

Abstract

This study presents the general features of the structural health monitoring (SHM) system of the long-span cable-supported bridges in Turkey, namely the First Bosphorus Bridge, the Second Bosphorus Bridge (Fatih Sultan Mehmet Bridge), the newly constructed the Third Bosphorus Bridge (Yavuz Sultan Selim) and the Osman Gazi Bridge (Izmit Bay Bridge). Due to the critical function of the bridges in the transportation networks of Istanbul, structural health under extreme and operational load cases without interruption of their operation is essential. The SHM systems are already available and in use for the First and Second Bosphorus Bridges; however, the Yavuz Sultan Selim and the Osman Gazi Bridges, only have the preliminary SHM systems established. General preferences of the current and planned SHM systems of the bridges are given based on the monitoring objectives and requirements. Data collected from the SHM systems are also exhibited as an illustration for structural modal identification of the First Bosphorus Bridge. Based on the experiences from the current SHM systems, future works and planning are recommended to effectively design new SHM system and to efficiently update the current ones.     

一种改进的工程结构全寿命设计理论指标体系

以结构耐久性为主线，对现有工程结构全寿命设计理论框架进行重组，将结构的全寿命设计目标总结为可靠性目标和可持续性目标两方面，建立了包含安全性、适用性、耐久性、经济影响、环境影响和社会影响等设计目标的全寿命设计体系，并确定了基于结构动态性能的全寿命设计思路。通过建立结构可持续发展指标，解决了现有全寿命设计理论中概念模糊和指标重复的问题，并完善了全寿命成本的内涵。改进后的全寿命设计理论指标体系具有更为完善合理的构架，能够广泛适应各种结构类型和使用情况。     

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.     

Extreme value theory-based structural health prognosis method using reduced sensor data

This paper presents an extreme value theory (EVT)-based structural health prognosis method that can be used for estimating the quantile values of remaining useful life (RUL) of monitored structure with reduced sensor data. Massive sensor data generated from online structural health monitoring system can be utilised to provide more refined prognosis results such as statistical distribution of RULs. By using the moment estimator from EVT, only a small portion of the full sensor data-set is actually used for estimating the quantile values of the RUL. This can considerably cut the computing time required for structural health prognosis. As a requirement for implementing the EVT-based prognosis, monotonicity relation between damage index (either measured or derived from sensor data) and RUL values has to be satisfied though. Common prognosis problems in civil engineering include fatigue cracking in steel structures and steel rebar corrosion in reinforced concrete structures. To illustrate the EVT-based prognosis, the monotonicity condition is shown for the selected degradation models in two case studies involving fatigue life estimation and pitting corrosion life of steel reinforcing rebar, respectively. The results show that EVT-based structural health prognosis method is computationally efficient without loss of much accuracy.     

Evidence-based framework for real-time life-cycle management of fatigue-critical details of structures

Ship structures are subjected to repetitive wave loads that can lead to fatigue damage. Accumulation of fatigue damage may eventually result in structural failure. In order to mitigate fatigue hazard to ship structures, timely inspections and maintenance actions must be planned during their life-span. As delays in the process of decision-making could result in the increase of the risk of failure, decision-supportive frameworks working in a real-time fashion are extremely important. In this paper, a life-cycle management framework for fatigue-critical details is proposed on the basis of Dynamic Bayesian Network (DBN) and Influence Diagram (ID). The proposed framework, established based on a stochastic fatigue crack growth model, leverages exact inference and discretisation of continuous random variables to enable real-time Bayesian updating and utility-based decision-making. Compared with previous life-cycle management studies, the new framework is capable of making decisions regarding different inspection techniques and appropriate repair actions in a holistic manner. The relatively simple structure of DBN and ID also makes the proposed framework especially attractive to stakeholders and practitioners, who may have limited knowledge of statistics, Bayesian updating and utility theories.     

Integrating reliability and structural health monitoring in the fatigue assessment of concrete bridge decks

The overall objective of this article is to demonstrate how the concepts of reliability and structural health monitoring (SHM) can be integrated to create bridge assessment and decision systems. The steel-free concrete bridge deck system was chosen as a specific case study providing tangible focus for the research. The bridge assessment model therefore focused on fatigue cracking issues associated with wheel loads due to heavy truck traffic. The bridge assessment model required five components: a vehicle load model, a fatigue damage accumulation model, a residual strength model, a reliability model and an SHM decision model. Each of these components is discussed within the article with specific reference to previous research on steel-free bridge deck systems. The proposed model is used to develop a decision threshold for monitoring the oldest steel-free bridge deck in service.     

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。     

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。     

Structural health monitoring of a steel stringer bridge with area sensing

The Federal Highway Administration Long-term Bridge Performance Programme initiated an International Bridge Study by selecting a steel stringer bridge as a benchmark structure for structural health monitoring. As a part of this programme, the authors studied the application of the Long-Gauge Fibre Bragg Grating (LG-FBG) sensors on this bridge. This paper aims at illustrating the LG-FBG-related state-of-the-art technologies by taking the bridge as the test bed. (1) The concept of the LG-FBG sensor for area sensing is presented. Most fibre optic sensors measure point strains for local monitoring. In contrast, the developed LG-FBG area sensor has a long gauge (e.g. 1–2 m), and it can be connected to each other to make a sensor array for distributed strain measuring; (2) spectral analyses of the macro-strain time histories are performed to identify structural frequencies, and the results are compared with those estimated from acceleration measurements; (3) the neutral axis position of the girder of the investigated bridge is estimated from the recorded macro-strain time histories, and the results are compared with those from static truck tests and (4) a modal macro-strain-based damage index is applied for damage detection of the steel stringer bridge.     

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.     

Continuous dynamic monitoring of a centenary iron bridge for structural modification assessment

A multi-channel continuous dynamic monitoring system has been installed in a centenary iron arch bridge on late November 2011. The historic infrastructure, completed in 1889 and crossing the Adda river about 50 km far from Milan, is the most important monument of XIX century iron architecture in Italy and is still used as roadway and railway bridge. The monitoring project follows a series of preliminary ambient vibration tests carried out on the bridge since June 2009. The paper describes the bridge structure and its dynamic characteristics identified from the experimental studies developed since 2009, the installed monitoring system and the software developed in LabVIEW for automatically processing the collected data. Subsequently, the tracking of automatically identified natural frequencies over a period of about 18 months is presented and discussed, highlighting the effects of environmental and operational conditions on the bridge dynamic characteristics as well as the detection of structural changes, mainly based on natural frequencies shifts.     

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.     

基于遗传算法与神经网络的桥梁结构健康监测系统研究初探

在前人研究工作的基础上，根据遗传算法和神经网络在处理复杂非线性问题时的各自特点，分别将其用于桥梁结构健康监测系统的不同部分，提出了建立基于遗传算法与神经网络的桥梁结构健康监测系统的基本设想。由于该项研究工作刚刚起步，文中的有些部分已经经过试验验证，而有些部分则尚处于理论研究阶段，需通过试验进一步证实。     

Life-cycle performance,management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1

Our knowledge to model, analyse, design, maintain, monitor, manage, predict and optimise the life-cycle performance of structures and infrastructures under uncertainty is continually growing. However, in many countries, including the United States, the civil infrastructure is no longer within desired levels of performance and safety. Decisions regarding civil infrastructure systems should be supported by an integrated reliability-based life-cycle multi-objective optimisation framework by considering, among other factors, the likelihood of successful performance and the total expected cost accrued over the entire life-cycle. The primary objective of this paper is to highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management and optimisation of structural systems under uncertainty. Challenges are also identified.