Zuverlässigkeitsorientierte Bewertung von Bauwerken auf Grundlage der Bauwerksüberwachung

Reliability‐based system assessment of civil engineering structures based on structural health monitoring The safety and usability of transport infrastructure is of great importance for the entire society, because disturbances of traffic networks can have significant financial and environmental consequences. Especially in times when bad news about deteriorated structures and shortened public budgets are omnipresent, innovated solutions are in demand. Structural health monitoring (SHM) can help to assess the safety of deteriorated structures. With the help of suitable monitoring strategies the service life, which may elapse up to a renovation or a renewal without endangering the users, can be determined. So far however a regular monitoring of buildings – with few exceptions – did not become generally accepted yet, above all because it is time‐consuming and expensive. Owing to world‐wide research and development in this field however inspection and monitoring strategies can be optimized meanwhile in such a way becoming attractive for various applications. Innovative methods for structural health monitoring (SHM) are developed by the Collaborative Research Centre (CRC) 477 at the Braunschweig University of Technology. In project field A1 a framework for the probabilistic safety assessment of structures based on data from SHM is developed. This paper describes and explains the methodology of the framework and shows its application using a substitute structure of the CRC 477 as an example.     

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.     

Large-scale structural health monitoring using composite recurrent neural networks and grid environments

The demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data-driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field-acquired data. However, the majority of unsupervised learning SHM research focuses on detecting damage in simple structures or components and possibly low-resolution damage localization. In this study, an unsupervised learning, novelty detection framework for detecting and localizing damage in large-scale structures is proposed. The framework relies on a 5D, time-dependent grid environment and a novel spatiotemporal composite autoencoder network. This network is a hybrid of autoencoder convolutional neural networks and long short-term memory networks. A 10-story, 10-bay, numerical structure is used to evaluate the proposed framework damage diagnosis capabilities. The framework was successful in diagnosing the structure health state with average accuracies of 93% and 85% for damage detection and localization, respectively.     

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.     

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.     

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.     

On the Value of Monitoring Information for the Structural Integrity and Risk Management

This article introduces an approach and framework for the quantification of the value of structural health monitoring (SHM) in the context of the structural risk and integrity management for systems. The quantification of the value of SHM builds upon the Bayesian decision and utility theory, which facilitates the assessment of the value of information associated with SHM. The principal approach for the quantification of the value of SHM is formulated by modeling the fundamental decision of performing SHM or not in conjunction with their expected utilities. The expected utilities are calculated accounting for the probabilistic performance of a system in conjunction with the associated structural integrity and risk management actions throughout the life cycle, the associated benefits, structural risks, and costs and when performing SHM, the SHM information, their probabilistic outcomes, and costs. The calculation of the expected utilities necessitates a comprehensive and rigorous modeling, which is introduced close to the original formulations and for which analysis characteristics and simplifications are described and derived. The framework provides the basis for the optimization of the structural risk and integrity management based on utility gains including or excluding SHM and inspection information. Studies of fatigue deteriorating structural systems and their characteristics (1) provide decision support for the performance of SHM, (2) explicate the influence of the structural component and system characteristics on the value of SHM, and (3) demonstrate how an integral optimization of SHM and inspection strategies for an efficient structural risk and integrity management can be performed.     

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.     

Korrosionsmonitoring und Bruchortung in Spannbetonbauwerken – Möglichkeiten und Grenzen

Corrosion Monitoring and Fracture Detection in Prestressed Concrete Structures – Possibilities and Limits Corrosive influences may affect the long‐term functionality of prestressed elements of prestressed concrete constructions and may lead to catastrophic failure of the member. Hence, the objective of the condition assessment of prestressed components has to be detecting existing defects and damages of the prestressed elements, e.g. grouting faults, corrosion and steel fractures in time. For an objectively early diagnosis of corrosion damages on prestressing elements, the Structural Health Monitoring (SHM) based on non‐destructive testing and monitoring methods is predestined. In this contribution causes of tendon corrosion in prestressed concrete (PC) bridges will first be specified, furthermore advantages and the strategy of corrosion monitoring are presented. Afterwards the state of the research and art of non‐destructive techniques and evaluation (NDT/NDE) of the corrosion state and of fracture detection of the prestressed elements in bridges with its possibilities and limits will be discussed. In this context also innovative sensors and measuring methods developed by the authors at the iBMB within the collaborative research center (CRC) 477 “Structural Health Monitoring” will be introduced.     

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。     

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.     

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.     

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.     

Effects of model updating on the estimation of stochastic seismic response of a concrete-filled steel tubular arch bridge

The objectives of this paper are to integrate the structural health monitoring (SHM) technique with the structural seismic analysis, and to make the SHM technique serve, benefit and promote the structural seismic analysis integrally. Therefore, considering a concrete-filled steel tubular arch bridge structure, the SHM technique is used to calibrate the finite element (FE) model through the model-updating scheme to minimise the structural response differences caused by FE model errors. Effects of model updating on structural seismic responses are investigated using the stochastic vibration analysis approach. It is observed that effects of model updating are significant on structural seismic responses, and these effects may become more evident in structural nonlinear dynamic analysis. Hence, it is of prime importance to calibrate the FE models through the SHM technique for seismic evaluations of some operational critical structures.     

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。     

Theoretical and experimental modal analysis of the Guangzhou New TV Tower

The Guangzhou New TV Tower (GNTVT) in Guangzhou, China, is a supertall tube-in-tube structure with a total height of 600 m. A complicated structural health monitoring (SHM) system consisting of over 800 sensors has been implemented to the GNTVT for both in-construction and in-service real-time monitoring. By making use of this SHM system, the ambient vibration measurement is carried out in a continuous and long-term manner. This paper presents the analytical and experimental modal analysis of the tower and the field ambient vibration measurement at different construction stages and under different excitation conditions, particularly addressing the following issues: (1) a reduced-order FE model for the GNTVT; (2) field vibration measurement and modal parameter identification of the tower under construction and two environmental excitations (typhoon and earthquake); and (3) comparison of results under different excitation events in the time-frequency domain and correlation between natural frequencies and air temperature using linear regression analysis. The experimental dynamic characteristics of the tower can be used to update the finite element of the tower, so that the updated finite element model of the tower can be obtained, which will serve as the baseline model for future health monitoring and damage detection. They can also be used to verify the effectiveness of vibration control devices installed on the tower.     

大跨缆索支承型桥梁健康监测与评估系统的设计研究

随着我国经济迅速发展,对于交通运输能力的要求不断提高,结构健康监测（Structural Health Monitoring,SHM）系统越来越成为大型桥梁运营、养护管理、安全评估与维护的重要技术支撑。本文针对目前国内外大跨桥梁结构健康监测系统中存在的问题,围绕“结构健康监测、日常养护检测系统、周期检测相结合,综合建立大跨桥梁结构状态识别与安全评估系统”的思想,主要就建立大跨缆索支承型桥梁结构健康监测系统中的问题进行了研究,提出了明确的系统的总体设计原则、构成以及功能目标,对系统设计与安全评估目标的关系问题进行了分析研究,阐述了监测项目选择、传感器子系统、数据采集与传输子系统、数据处理与控制子系统、结构状态识别与安全评估子系统的设计原则或思路。     

Application of distributed optical fiber sensors for the health monitoring of two real structures in Barcelona

The versatility and ease of installation of Distributed Optical Fibre Sensors (DOFS) compared with traditional monitoring systems are important characteristics to consider when facing the Structural Health Monitoring (SHM) of real world structures. The DOFS used in this study provide continuous (in space) strain data along the optical fibre with high spatial resolution. The main issues and results of two different existing structures monitored with DOFS, are described in this paper. The main SHM results of the rehabilitation of an historical building used as hospital and the enlargement of a pre-stressed concrete bridge are presented. The results are obtained using a novel DOFS based on an Optical Backscattered Reflectometry (OBR) technique. The application of the optical fibre monitoring system to two different materials (masonry and concrete) provides also important insights on the great possibilities of this technique when monitoring existing structures. In fact, the influence of strain transfer between the DOFS and the bonding surface is one of the principal effects that should be considered in the application of the OBR technique to real structures. Moreover, and because structural surfaces generally present considerable roughness, the procedure to attach the optical fibre to the two monitored structures is described.     

Health monitoring of flexural steel structures based on distributed fibre optic sensors

In our previous study, a distributed long-gauge fibre optic sensing system (with the ability to obtain effective average strain, or macro-strain, distributions) for practical adaptation in civil structural health monitoring was developed and verified. The present paper is devoted to proposing an integrated health monitoring scheme for elastic beam-like structures, where flexure dominates structural responses, based on static testing and measurements using the developed sensors. A series of experimental investigations on steel beams with different levels of damage are first carried out. The static strain measurements from distributed sensors are characterised and some concerns on the data processing and feature extraction are discussed. On the basis of the extracted features, structural health monitoring (SHM) investigations are deployed in three parts: damage identification with no requirement for a structural analytical model, parametric estimation based on finite element (FE) models, and evaluation of structural global behaviour. By comparing to traditional transducers such as linear variable displacement transducers (LVDTs) and foil ‘point’ strain gauges, the ability and ascendancy of the sensors developed here for SHM purposes are verified. A comprehensive health monitoring strategy for steel flexural structures based on the distributed strain sensors array is proposed finally.     

Structural Displacement Measurement Using an Unmanned Aerial System

Vibration‐based Structural Health Monitoring (SHM) is one of the most popular solutions to assess the safety of civil infrastructure. SHM applications all begin with measuring the dynamic response of structures, but displacement measurement has been limited by the difficulty in requiring a fixed reference point, high cost, and/or low accuracy. Recently, researchers have conducted studies on vision‐based structural health monitoring, which provides noncontact and efficient measurement. However, these approaches have been limited to stationary cameras, which have the challenge of finding a location to deploy the cameras with appropriate line‐of‐sight, especially to monitor critical civil infrastructures such as bridges. The Unmanned Aerial System (UAS) can potentially overcome the limitation of finding optimal locations to deploy the camera, but existing vision‐based displacement measurement methods rely on the assumption that the camera is stationary. The displacements obtained by such methods will be a relative displacement of a structure to the camera motion, not an absolute displacement. Therefore, this article presents a framework to achieve absolute displacement of a structure from a video taken from an UAS using the following phased approach. First, a target‐free method is implemented to extract the relative structural displacement from the video. Next, the 6 degree‐of‐freedom camera motion (three translations and three rotations) is estimated by tracking the background feature points. Finally, the absolute structural displacement is recovered by combining the relative structural displacement and the camera motion. The performance of the proposed system has been validated in the laboratory using a commercial UAS. Displacement of a pinned‐connected railroad truss bridge in Rockford, IL subjected to revenue‐service traffic loading was reproduced on a hydraulic simulator, while the UAS was flown from a distance of 4.6 m (simulating the track clearance required by the Federal Railroad Administration), resulting in estimated displacements with an RMS error of 2.14 mm.