Structural Health Monitoring System for the Shandong Binzhou Yellow River Highway Bridge

Abstract:   Structural health monitoring (SHM) provides a useful tool for ensuring safety and detecting the evolution of damage and performance deterioration of civil infrastructures. A great number of civil infrastructures under construction can be used as test beds for SHM systems. The Binzhou Yellow River Highway Bridge is a cable-stayed bridge in Shandong Province, China. An SHM system has been implemented on this bridge during its construction for monitoring its health status and assessing its safety for long-term services. The system includes a sensor module, a data acquisition module, a wired and wireless data transmit module, a structural analysis module, a database module, and a warning module. It is integrated by using LabVIEW software and can be remotely operated via Internet. The database is available freely to all scientists and engineers in the SHM research area. This article introduces the deployment and functions of this system, and presents the measured responses of the bridge subjected to moving vehicle loads.     

Damage Pattern Recognition for Structural Health Monitoring Using Fuzzy Similarity Prescription

Abstract:   Structural health monitoring (SHM) is a systematic method for non-destructive evaluation of a structure's performance by sensing, extracting, patterning, and recognizing features of the structural response. Most SHM approaches focus on statistical analysis for damage identification considering only random uncertainties. This article introduces a method that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness which are statistically non-describable. The proposed method deals primarily with epistemic uncertainty. The method improves damage identification by performing damage pattern recognition using fuzzy sets. In this approach, healthy observations are used to construct a fuzzy set representing healthy performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed. Thus, an optimal group of fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. Piecewise linear functions are used as fuzzy membership functions representing the states of healthy and damaged. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fuzzy pattern recognition based on maximum approaching degree. A case study for damage pattern recognition of a model steel bridge is presented and discussed. The approach is capable of identifying damage patterns accurately.     

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.     

Stationarity‐Based Approach for the Selection of Lag Length in Cointegration Analysis Used for Structural Damage Detection

Cointegration has been recently brought to structural health monitoring (SHM) as a new methodology for dealing with the problem of environmental and/or operational variability in monitored structures. However, it is well known that the choice of lag length in cointegration analysis has a strong influence on damage detection results. The article presents a new approach for optimal lag length selection in cointegration analysis used for structural damage detection. This new method is based on stationarity analysis of data representing undamaged condition. The proposed method is validated using Lamb wave data under the effects of temperature variations and vibroacoustic data obtained from nonlinear vibroacoustic modulation experiments with different low‐frequency vibration (or modal) excitations. The results demonstrate the effectiveness of the method for structural damage detection based on SHM data heavily affected by environmental or operational conditions.     

A scalable cloud-based cyberinfrastructure platform for bridge monitoring

Cloud computing is a computing paradigm wherein computing resources, such as servers, storage and applications, can be provisioned and accessed in real time via advanced communication networks. In the era of Internet of Things (IoT) and big data, cloud computing has been widely developed in many industrial applications involving large volume of data. Appropriate use of cloud computing infrastructure can enhance the long-term deployment of a structural health monitoring (SHM) system which would incur significant amount of data of different types. This paper presents a cloud-based cyberinfrastructure platform designed to support bridge monitoring. The cyberinfrastructure platform enables scalable management of SHM data and facilitates effective information sharing and data utilisation. A cloud-based platform comprises of virtual machines, distributed database and web servers. The peer-to-peer distributed database architecture provides a scalable and fault-tolerant data management system. Platform-neutral web services designed in compliant with the Representational State Transfer (REST) standard enables easy access to the cloud resources and SHM data. For data interoperability, a bridge information model for bridge monitoring applications is adopted. For demonstration, the scalable cloud-based platform is implemented for the monitoring of bridges along the I-275 corridor in the State of Michigan. The results show that the cloud-based cyberinfrastructure platform can effectively manage the sensor data and bridge information and facilitate efficient access of the data as well as the bridge monitoring software services.     

Health monitoring system of a self-anchored suspension bridge (planning,design and installation/operation)

Recent advances in sensing technologies make it feasible and practic al to install sensors on expensive civil infrastructures, such as bridges, for safety monitoring during construction and long-term assessment of the structures condition. This paper presents a case study of a structural health monitoring (SHM) and bridge management system (BMS) installed in Yeongjong Bridge in South Korea. This is a self-anchored suspension bridge on the expressway that links Seoul to Incheon International Airport. Experimental data have been collected from the bridge for two years, since its completion in 2000. This paper presents a list of examples where the structural health monitoring system can be applied and provides general guidelines and recommendations for deploying a monitoring system in terms of (1) sensing and instrumentation, (2) data collection and signal processing, and (3) information processing.     

Development of performance assessment tools for a highway bridge resulting from controlled progressive monitoring

As highway bridges continue to deteriorate given the increased service life, increase in vehicle demand and exposure to harsh environmental climates, new methods of monitoring their in situ performance are of high priority. Damage within the structure can alter various load demand and capacity characteristics, affecting the overall integrity of the bridge. Discussed in this paper is the monitoring of a simple span bridge superstructure under various induced damage states. Strain measurements were recorded at the midspan and north abutment of each girder. Six levels of damage progression were implemented at a rocker bearing and various diaphragms to girder connections. Transverse load distribution factors (DFs) and neutral axis (NA) locations were measured for each damage case and evaluated against the baseline undamaged response. These measurements serve to provide a possible method of damage detection using load-testing parameters already employed by various transportation agencies. Next, a performance index (PI) is developed for this stringer/multi-girder bridge utilising the NA and DF response from the steel girder system and the allowable stress design load-rating data. The ratio of NA to DF was compared to the inventory load rating for each girder at each damaged state. The data were fitted with a power regression model to form the PI. Furthermore, a 95% prediction interval was used around the predicted response to capture all the data from the testing. The model was applied to the damaged structure as well as two additional stringer/multi-girder bridges. The objective of the PI is to complement existing qualitative assessment protocols with quantitative results for improving the condition assessment process.     

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.     

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.     

Effect of tuned mass damper on the interaction of a quarter car model with a damaged bridge

This paper considers the effects of a tuned mass damper (TMD) on damaged bridge–accelerating quarter car vehicle interaction. The damage of the bridge is considered to be an open crack. The incorporation of a TMD to control the vibration response of the bridge and the quarter car vehicle model has been investigated from different aspects. A simplified form for the tuning ratio of the TMD is proposed. The vibration mitigation of the peak displacement, velocity and acceleration of the damaged bridge and the accelerating quarter car vehicle model using such a tuning is observed, along with the effects of possible detuning of the TMD due to the progressive deterioration of the bridge. A detailed parametric study is performed on the system with the TMD, considering the effects of quarter car vehicle model velocity, acceleration and the severity of the damage of the bridge.     

SHM-based F-AHP bridge rating system with application to Tsing Ma Bridge

This paper aims at developing a structural health monitoring (SHM)-based bridge rating method for bridge inspection of long-span cable-supported bridges. The fuzzy based analytic hierarchy approach is employed, and the hierarchical structure for synthetic rating of each structural component of the bridge is proposed. The criticality and vulnerability analyses are performed largely based on the field measurement data from the SHM system installed in the bridge to offer relatively accurate condition evaluation of the bridge and to reduce uncertainties involved in the existing rating method. The procedures for determining relative weighs and fuzzy synthetic ratings for both criticality and vulnerability are then suggested. The fuzzy synthetic decisions for inspection are made in consideration of the synthetic ratings of all structural components. The SHM-based bridge rating method is finally applied to the Tsing Ma suspension bridge in Hong Kong as a case study. The results show that the proposed method is feasible and it can be used in practice for longspan cable-supported bridges with SHM system.     

Damage Identification for Hysteretic Structures Using a Mode Decomposition Method

This article investigates structural health monitoring (SHM) of multidegree of freedom (MDOF) structures after major seismic or environmental events. A recently developed hysteresis loop analysis (HLA) SHM technique has performed robustly for single degree of freedom (SDOF) and single mode dominant MDOF structures. However, strong ground motions can trigger higher vibration modes, resulting in irregular hysteresis loops and making this otherwise robust identification difficult. This study presents a new filtering tool, enabling reconstruction of single mode dominant restoring force‐displacement loops which can be readily used for HLA. The proposed filtering tool is based on a classic modal decomposition using optimized mode shape coefficients. The optimization process is carried out in a modal space and is based on decoupling frequency response spectra of interfering modes. Application of modal decomposition using the optimized mode shape coefficients allows for reconstruction of single‐mode dominant hysteresis loops, which can be effectively identified using HLA. The proposed filtering tool is validated on the reconstruction of hysteresis loops on an experimental bridge pier test structure with notable contributions from at least two modes. The results show the method eliminates the influence of all higher modes that contain significant energy content and yields the reconstruction of “smooth” single mode dominant hysteresis loops. The resulting SHM analysis on the reconstructed experimental hysteresis loops identified degradation in the elastic stiffness profiles, indicating damage within the structure and matching prior published results based on physical inspection of damage. The overall method presented increases the breadth of potential application of the HLA method and can be readily generalized to a range of MDOF structures.     

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.     

Development of a Bridge Bumper to Protect Bridge Girders from Overheight Vehicle Impacts

Abstract:   Bridges with low clearance are vulnerable to collision with overheight vehicles. Collisions of overheight vehicles can cause fatalities and injuries to the drivers and passengers of the overheight vehicles, and damage to bridge girders. The repair of the damaged bridges can be costly and time consuming. This article investigates the feasibility of developing a bridge bumper that minimizes the physical injuries and the likelihood of fatalities and protects the structural elements of bridges by absorbing the impact energy. The article presents the results of small-scale impact experiments using the proposed bridge bumper with several options of energy-absorbing materials to protect a reinforced concrete beam. Finite element analyses are carried out to simulate the small-scale impact experiments. Optimization of the finite element model is conducted for the response quantities of interest with respect to the geometrical parameters and the material properties of the proposed bridge bumper. Such analysis can guide the design of an optimal bridge bumper that maximizes the energy dissipation and minimizes the damage to the bridge girder and the likelihood of fatalities and injuries. A possible full-scale implementation of the proposed bridge bumper is also described.     

土木工程结构健康监测的研究进展综述

对于大量进入老龄和维护期的土木工程结构,其安全性倍受重视,而具有多学科交叉特点的结构健康监测技术则是支撑土木工程基础设施安全运行和适时维护的一个有力工具。首先对各种结构损伤检测方法和无线智能传感技术的最新研究成果进行了回顾和评述;讨论了在线结构健康监测系统的关键问题;最后对结构健康监测和损伤检测领域今后的发展方向进行了展望。     

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.     

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.     

斜拉桥结构健康监测系统的设计与实现(I):系统设计

结构智能健康监测愈来愈成为重大工程结构健康与安全的重要保障技术,也愈来愈成为重大工程结构损伤积累乃至灾害演变规律的重要研究手段。斜拉桥健康监测系统是由传感器子系统、数据采集与传输子系统、结构分析子系统和数据管理子系统组成的,不同系统的谐调运行需要通过系统集成技术来实现。首先从监测内容、等级和功能等方面研究健康监测系统的总体设计原则;然后,分局部监测变量和整体监测变量研究传感器的最优测点确定方法和原则,提出传感器的选型原则;提出数据采集系统的总线设计方法和方案,研究数据采集系统硬件和软件设计方法;提出数据传输系统的设计原则和方法;给出斜拉桥基于构件和基于结构体系的安全评定设计方法;提出斜拉桥施工监控、成桥试验、运营健康监测和养护管理四位一体系统的共享设计原则;提出系统集成技术的软件设计方法。     

Estimation of fixity factors of bolted joints in a steel frame structure using a vibration-based health monitoring technique

Framed steel structures frequently have bolted connections to ensure semi-rigid joints that have a significant amount of energy dissipation incorporated in them, to avoid failure of connections and members under cyclic loading, such as wind and earthquake loading. One problem is that due to cyclic action, these bolts may loosen, and compromise subsequent behavior of the structure. A vibration-based health monitoring technique for quantification of the level of loosening of bolted joints in a steel plane frame structure is presented here, to provide a basis for evaluation of the structure state. A numerical model of a plane frame is considered with rotational springs representing semi-rigid joints. A fixity factor is defined in terms of rotational spring stiffness and is considered as a measure of level of loosening of bolts with zero representing fully loose and one representing fully tight condition. Experimental strain time histories are collected and transformed into frequency domain using Fourier transform. A shape co-relation is defined using frequency data obtained from the damaged and the undamaged structures. Using the frequency spectra and shape correlation, an objective function (OF) is developed and minimized by the particle swarm optimization to estimate the fixity factor. It is found that the technique estimates higher value of reduction of the fixity factor in the damaged location, but shows some considerable value at the other springs also. Therefore, the technique is improved using heuristics by identifying probable damage locations prior to applying model updation, in order to estimate the damage severity more accurately. Considering fixity factors at the identified locations as variables, model updating is done for estimation of fixity factors. The improved results clearly indicate actual damage locations and fixity factors for different levels of bolt loosening, and is found satisfactory for possible future application of the technique to multistory framed structures.     

利用少量传感器信息与人工智能的桥梁结构安全监测新方法

分析了当前结构安全监测在工程应用中存在的诸多问题和挑战,提出应发展利用少量传感器信息及基于大数据与人工智能的安全监测新方法,来克服现有系统传感器繁多、造价昂贵、海量数据难以处理的问题。介绍了单测点信息的多维相空间方法和单传感器信息的重构相空间方法;在基于双传感器信息的移动互相关函数法基础上,提出了基于双传感器信息的移动传递熵方法;阐述了基于少量传感器信息的移动主成分分析法的物理意义及其工程应用的适用性和可行性。利用单传感器方法和移动主成分分析法,以及小波包能量法、二次协方差矩阵法对虎门大桥进行长达5年的安全监测。阐述了基于深度学习的结构安全监测人工智能方法及其发展概况,分析了基于结构动力学信息的深度学习方法及其巨大的应用潜力。在此基础上进一步思考了基于少量传感器和人工智能相结合的方法在结构安全监测应用中的发展思路。结果表明:单传感器信息的重构相空间方法更适用于实际工程;基于双传感器信息的移动互相关函数法和移动传递熵方法均能精确定位损伤;移动主成分分析法最适用于实际工程的实时监测。