Bridge Health Monitoring System Research Articles

Dynamic reliability prediction of bridges based on nonlinear MGPF algorithm and SHM data

Bridge health monitoring system produces much monitored data during the long-term service periods. How to properly handle with these data is one of the main difficulties in the field of Structural Health Monitoring (SHM). To predict the structural reliability with these data, the objectives of this paper are to present: (1) a nonlinear dynamic model, (2) nonlinear Mixed Gaussian Particle Filtering (MGPF) algorithm, (3) a dynamic reliability prediction approach combining nonlinear MGPF algorithm and SHM data with first order second moment (FOSM) method. And the monitored extreme stress data of an actual bridge is provided to illustrate the application and feasibility of the proposed models and methods.

Damage Detection Sensitivity of a Vehicle-based Bridge Health Monitoring System

As one solution to the problem for condition assessment of existing short and medium span (10-30m) reinforced/prestressed concrete bridges, a new monitoring method using a public bus as part of a public transit system (called “Bus monitoring system”) was proposed, along with safety indices, namely, “characteristic deflection”, which is relatively free from the influence of dynamic disturbances due to such factors as the roughness of the road surface, and a structural anomaly parameter. In this study, to evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. In here, although there are some useful monitoring methods for short and medium span bridges based on the qualitative or quantitative information, the sensitivity of damage detection was newly discussed for safety assessment based on long term health monitoring data. The verification results thus obtained are also described in this paper, and also evaluates the sensitivity of the “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. Furthermore, the sensitivity of “characteristic deflection” is verified by 3D FEM analysis.

Structural health monitoring system of bridges

The main scope of the research is to develop an integrated monitoring system for durability assessment of bridges. The system must interface and integrate the actual practice mainly based on visual inspections and combine the response of a number of different reliable sensors, installed on the structure to monitor the progress of damage, with enhanced realistic deterioration models. The system and the sensors were developed to cover the parameters for the most important deterioration mechanisms: corrosion of reinforcement in bridges, carbonation of concrete, freeze-thaw cycles, alkali-silica reaction and mechanical damage, as well as the changes in the structures behavior and safety: static deformation, strains; crack widths and vibrations (frequencies, amplitudes, accelerations and vibration modes).The progress of the various types of damage mechanisms can be predicted by monitoring the key physical and chemical parameters of the materials (such as temperature, humidity and pH for the concrete, measured on the surface of the structure and as a profile through the concrete thickness, and/rate of corrosion of reinforcement), and key mechanical parameters (such as strains, deflections, vibrations).The use of permanent monitoring systems has several advantages once the system is installed: (i) to reduce the operating costs of inspections and maintenance by 25%, and the traffic-related costs by 30 % by reducing the number and extent of site inspections and (ii) to reduce the overall life costs of bridges by 10 % by applying the improved lifetime prediction models already from the design stage.

Design and Implementation of Remote Health Monitoring System for 3D Visual Bridge

With the growing concern about the safety, durability and normal use function of large and important bridge, the research and development of bridge health monitoring system is developed. The purpose of this paper is to realize the system of real-time monitoring and security analysis results show the three-dimensional visualization, this paper studies the three-dimensional visual model of the bridge real-time security monitoring system. According to the functional requirements, the system is divided into three parts, such as 3D graphics engine, 2D graphics engine, database interface and so on. According to the idea of modern software engineering, a complete set of system architecture is designed, which makes the above modules work with high cohesion and low coupling. The system to the system development methods of the modern software engineering based, to all kinds of design patterns as a skeleton, integrated use of 3D modeling and rendering technology and GDI + and SQL database technology on the MFC platform is a great progress of the combination of computer software technology and monitoring system. At the same time, the success of the system development, the trial prove greatly improves the efficiency of the monitoring system of bridge, to predict bridge safety hidden trouble, ensuring the safe operation of the bridge to the very important role to promote the bridge monitoring technology development.

A Vehicle-based Health Monitoring System for Short and Medium Span Bridges and Damage Detection Sensitivity

In this paper, one practical solution to the problem for condition assessment of short and medium span(10-30m) bridges is discussed. It uses vehicle-based measurements with public buses as part of a public transit system (called bus monitoring system). The details of theoretical background and the system are described with detailed flow of the bridge monitoring process that uses an in-service fixed-route bus. Advantages and principle of the bridge structure anomaly detection (condition assessment) method using the under-spring vibration of an in-service bus (public bus) are described in detail. This paper also describes the procedure for calculating "characteristic deflection," which is an indicator used to evaluate the degree of deterioration (bridge condition) of short and medium span bridges. Finally, the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection is verified by 3D FEM analysis.

Structural health monitoring system for Sutong Cable-stayed Bridge

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring the health status and performance deterioration of engineering structures during long-term service periods. The objective of its installation is to provide reasonable suggestions for structural maintenance and management, and therefore ensure the structural safety based on the information extracted from the real-time measured data. In this paper, the SHMS implemented on a world-famous kilometer-level cable-stayed bridge, named as Sutong Cable-stayed Bridge (SCB), is introduced in detail. The composition and core functions of the SHMS on SCB are elaborately presented. The system consists of four main subsystems including sensory subsystem, data acquisition and transmission subsystem, data management and control subsystem and structural health evaluation subsystem. All of the four parts are decomposed to separately describe their own constitutions and connected to illustrate the systematic functions. Accordingly, the main techniques and strategies adopted in the SHMS establishment are presented and some extension researches based on structural health monitoring are discussed. The introduction of the SHMS on SCB is expected to provide references for the establishment of SHMSs on long-span bridges with similar features as well as the implementation of potential researches based on structural health monitoring.

Dynamic extreme stress prediction of bridges based on nonlinear mixed Gaussian particle filtering algorithm and structural health monitoring data

In the long-term service period, the bridge health monitoring system produced a huge amount of monitoring stress data; proper handling of these data is one of the main difficulties in the field of structural health monitoring, especially to predict the structural stress based on the monitored data. The objectives of this article are to present: (1) a nonlinear dynamic model, (2) a nonlinear mixed Gaussian particle filtering algorithm for predicting the monitored data based on the dynamic model, and (3) an approach combining nonlinear mixed Gaussian particle filtering algorithm with structural health monitoring data to predict the structural stress under uncertainty in real time. And an actual example is provided to illustrate the application and feasibility of the proposed models and methods.

New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System.

This research introduces an improvement plan for the reliability of Global Navigation Satellite System (GNSS) positioning solutions. It should be considered the most suitable methodology in terms of the adjustment and positioning of GNSS in order to maximize the utilization of GNSS applications. Though various studies have been conducted with regards to Bridge Health Monitoring System (BHMS) based on GNSS, the outliers which depend on the signal reception environment could not be considered until now. Since these outliers may be connected to GNSS data collected from major bridge members, which can reduce the reliability of a whole monitoring system through the delivery of false information, they should be detected and eliminated in the previous adjustment stage. In this investigation, the Detection, Identification, Adaptation (DIA) technique was applied and implemented through an algorithm. Moreover, it can be directly applied to GNSS data collected from long span cable stayed bridges and most of outliers were efficiently detected and eliminated simultaneously. By these effects, the reliability of GNSS should be enormously improved. Improvement on GNSS positioning accuracy is directly linked to the safety of bridges itself, and at the same time, the reliability of monitoring systems in terms of the system operation can also be increased.

Structural Health Monitoring of Bridges Via Energy Harvesting Sensor Nodes

This paper deals with the application of novel sensing technologies to an existing Structural Health Monitoring (SHM) system for bridges. A vibration based SHM algorithm already in use to detect the structural performance degradation of a suspension highway bridge is modified to investigate the feasibility of replacing traditional wired accelerometers with state of the art wireless energy-harvesting sensors. The remodeled SHM algorithm benefits from the sensor nodes’ ability to support automated triggering and data pre-processing. The Random Decrement technique was included in the algorithm as a pre-processing tool to simultaneously reduce noise and amount of stored and transmitted data. Simulations based on available data were used to calibrate the triggering strategy, to verify the effectiveness of the data pre-processing, and to demonstrate power consumption improvements arising from the algorithm modification.

SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach

A major bottleneck preventing widespread use of Structural Health Monitoring (SHM) systems for bridges is the difficulty in making sense of the collected data. Characterising environmental effects in measured bridge behaviour, and in particular the influence of temperature variations, remains a significant challenge. This paper proposes a novel data-driven approach referred to as Temperature-Based Measurement Interpretation (TB-MI) approach to solve this challenge. The approach is composed of two key steps—(1) characterisation of thermal effects in bridges using a methodology referred to as Regression-Based Thermal Response Prediction (RBTRP) methodology, and (2) detection of anomaly events by analysing differences between measured and predicted structural behaviour. Measurements from a laboratory truss structure that is setup to simulate a range of structural scenarios are employed to evaluate the performance of the TB-MI approach. The study examines how the predictive capability of the RBTRP methodology is influenced by dimensionality reduction and measurement down-sampling, which are common pre-processing techniques used to deal with high spatial and temporal density in measurements. It also proposes a novel anomaly detection technique referred to as signal subtraction method that detects anomaly events from time-series of prediction errors, which are computed as the difference between in situ measurements and predictions obtained using the RBTRP methodology. Results demonstrate that the TB-MI approach has potential for integration within data interpretation frameworks of SHM systems of full-scale bridges.

Research and design of bridge health monitoring system based on sensor network

Research and design of bridge health monitoring system based on sensor network

Single input-single output identification thermal response model of bridge using nonlinear ARX with wavelet networks

Model identification of a system can be used for a variety of purposes, including model updates, damage assessment, active control and original design evaluation. We used single input-single output (SISO) nonlinear regression with least square solution and nonlinear AutoRegresive with eXogenous inputs (NLARX) with wavelet neural networks models to identify the thermal response of a bridge under hard environmental effects and estimated the nonlinearity model parameters. Fu-Sui Bridge strain structural health monitoring measurements were used as a case study. Nonlinear regression analysis showed that the thermal response is a nonlinear effect with temperature changes; and the NLARX with wavelet network solution is capable of accurately predicting thermal response and can help with interpreting measurements from continuous bridge health monitoring systems.

The state of the art in structural health monitoring of cable-stayed bridges

Cable-stayed bridges are a type of long-span bridges that have been extensively constructed throughout the world. However, cable-stayed bridges suffer from a variety of performance deteriorations and natural disasters during their service time. Structural health monitoring, having emerged since the early 1990s, is regarded as an in situ field experimental technique to understand the behaviors and performance of real, full-scale bridges under real loadings and environmental conditions and to further ensure the safety, serviceability, durability, and sustainability of bridges. In this paper, the existing various monitoring technologies for cable-stayed bridges developed are overviewed. The design approaches of structural health monitoring systems for cable-stayed bridges are introduced. The data analysis, modeling, and safety evaluation of cable-stayed bridges based on structural health monitoring technology are addressed. The applications of structural health monitoring technology for cable-stayed bridges are summarized. The design code for structural health monitoring systems is described for large highway bridges (including cable-stayed bridges) that are authorized by the Ministry of Transportation in China. Finally, this study discusses the challenges and future trends in structural health monitoring technologies for cable-stayed bridges, particularly for data-driven science and technology in structural health monitoring field.

Study Based on Bridge Health Monitoring System on Multihazard Load Combinations of Earthquake and Truck Loads for Bridge Design in the Southeast Coastal Areas of China

Similar to AmericanLRFD Bridge Design Specifications, the current Chinese bridge design code is fully calibrated against gravity load and live load. Earthquake load is generally considered alone and has its own methodology, however, which is not covered in the code in a consistent probability-based fashion. Earthquake load and truck load are the main loads considered in the basis of bridge design in more than 70% of seismic areas in China. They are random processes, and their combination is the main subject of this paper. Seismic characteristics of southeast coastal areas of China are discussed and an earthquake probability curve is calculated through seismic risk analysis. Using measured truck load data from a Bridge Health Monitoring System, the multimodal characteristics of truck load are analyzed and a probability model for a time intervaltis obtained by fitting results and reliability theory. Then, a methodology is presented to combine earthquake load and truck load on a probabilistic basis. To illustrate this method, truck load and earthquake load combinations are used. Results conceptually illustrate that truck load and earthquake load are not dominant in southeast coastal areas of China, but the effect of their combination is. This methodology quantitatively demonstrates that the design is controlled by truck load in most ranges; that is, truck load is more important to bridge design in the region.

Comprehensive Real-Time Bridge Health Monitoring System of Tongtai Bridge

Tongtai Bridge is the world’s largest suspension curve-girder-skew-arch bridge, which is located in Zhangjiakou, China. The understanding of mechanics characteristics is limited to such complex bridges, so it is necessary to establish reliable health monitoring system to investigate the static and dynamic responses and monitor the safety of the bridge. A comprehensive real-time bridge health monitoring system is establish, which includes four aspects: sensor system, data acquisition and transmission system, data processing and control system, structure early warning and security assessment system. The paper systematically describes the system design principles, sensor layout, and monitoring content, then expounds system integration and function of each subsystem.

Interval Threshold Setting Method of Vertical Support Reaction for Early Warning of Concrete Bridges

Existing bridge health monitoring systems neglect the benefit of support reaction index for the safeguard of bridge safety and generally do not include the monitoring of support reaction values. The basic setting method of vertical support reaction threshold was proposed in this paper. For concrete bridges, which have prominent discreteness, statistical analysis method should be employed to obtain the representative value of the modulus of elasticity. However, in practice, it is very difficult to obtain large amounts of data sample. The small sample analysis is necessary, with intent to reduce the uncertainty. For this purpose, t -distribution was introduced to carry out interval estimation for the mean value of modulus of elasticity. Confidence interval can be obtained and then be used as the representative value of modulus of elasticity. By adopting each endpoint of this interval respectively, two sets of vertical support reaction values under the most unfavorable load combination can be obtained and then multiplied by a reduction factor. For each bearing, there are two corresponding values, which can be used as boundary values and then interval threshold of vertical support reaction for concrete bridges early warning can be obtained. Finally, multiple level early warning based on the interval threshold can be actualized.

The application of a fuzzy inference system and analytical hierarchy process based online evaluation framework to the Donghai Bridge Health Monitoring System

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

A Bridge Health Monitoring System Based on Wireless Smart Aggregates

Researches on health monitoring technology of concrete structures by using piezoelectric smart aggregates have achieved a great development. However, the technique is not widely used so far in practical engineering. Because when constructing large-scale structural health monitoring (SHM) system using wire-based sensors, it requires a lot of cables to form a monitoring network, resulting in huge cost of abundant material of wires and labor for wire placement, and the relatively heavy maintenance work in case of failure of the SHM system. A kind of wireless sensor network based on the protocol for Zigbee802.15.4 and the passive piezoelectric smart health monitoring technology is developed in the paper. Through internal load monitoring tests of a concrete bridge model under impact loading, the developed wireless smart aggregate (WSA) health monitoring system is experimentally validated. The finite element method (FEM) is used to simulate the process as the same as the bridge model test, and the numerical results are consistent with those of the experiment. The experimental results show that the developed wireless system is stable and reliable, and can be applied in concrete bridge structure health monitoring under impact loading.

Some Key Issues and Challenges of Building the Structural Health Monitoring System of Bridges

In view of the issue of bridge structure safety, this study pointed out the necessity and urgency to build the bridge safety operational monitoring system. The basic idea, system composition, current situation of the development and application of structural health monitoring system were summarized. Finally, the key problems and development trend of bride safety monitoring were proposed.

The Visualization of Health Monitoring System for Bridge Using CORS and GIS

CORS is called Continuous Operational Reference System, which comprise a network of Global Positioning System receivers. Traditional sensors were used to determine the working environment parameters and mechanical property parameters of the bridge, and CORS technology was applied to obtain the positions of bridge monitoring points in three axis directions, besides the technology was adapted to combine GIS software with the relational database, in which the function of GIS and SQL Server were to manage the spatial information and the corresponding attribute data. In this paper the technology route was designed in detail to realize the real-time dynamic monitoring and visualization analysis of the deformation of large bridge. This system was developed to evaluate valuable information such as the stability, durability and reliability of the bridge.