Structural Health Monitoring: Applications And Data Analysis

This chapter first provides an overview of structural health monitoring (SHM) along with components of a complete monitoring design. These components are associated with fundamental knowledge needs, technology needs and also socio-organizational challenges for applications especially in the area of civil infrastructure systems. A successful SHM design requires addressing each of these considerations with an integrated approach offering various types of application scenarios for decision making. In this chapter, particular emphasis is given to data analysis and interpretation as a very critical aspect of SHM implementation. Some of the data analysis methods are presented with the goal of illustrating particular engineering applications and needs. Critical considerations for SHM data analysis and interpretations are discussed in terms of system characterization, sensing, data quality, presentation and decision making. © 2009 Woodhead Publishing Limited All rights reserved

A Hybrid Data Driven Technique For Long Term Monitoring Of Structures

Data interpretation is a challenging step for the monitoring of structures. Moving Principal Component Analysis (MPCA) has shown promising performance as a model free damage detection algorithm, which can be implemented for long-term monitoring of civil structures. Regardless of all the advantages associated with MPCA, it still has a main drawback which makes it less effective for long-term monitoring of critical structures. MPCA should be improved significantly in terms of time to detection. Therefore, the objective of this paper is to investigate and develop a new data interpretation approach based on principal component analysis but with less delay in detection. The efficiency of this innovative data interpretation approach (MPCASVM) is further investigated with the data from both lab study and a unique real-life structure. It has been observed from both lab and real-life study that MPCA-SVM outperforms MPCA in terms of time to detection

Application Of Two Individual Data-Driven Based Change/Damage Detection Methods For Bridge Monitoring

Fault detection is an important component for Structural Health Monitoring (SHM) applications. Herein, the efficiency of two data-driven based damage detection algorithms for bridge monitoring application will be explored and demonstrated. These algorithms will be based on Robust Regression Analysis (RRA) and Moving Principal Component Analysis (MPCA) as two statistics-based damage detection algorithms, which do not require a mathematical model for implementation. As a result, these methods are classified as data-driven techniques and they are quite effective for practical use in real life as long as the limitations are understood and the uncertainties can be evaluated. These methods will be demonstrated on a phenomenological model developed in the laboratory. This model, the UCF 4-span bridge, is equipped with Fiber Bragg Grating (FBG) sensors at 10 different locations and 2 most common and critical damage scenarios are chosen and induced for fault detection application. In addition to the lab test, the effectiveness of these techniques is tested with a real-life data from a unique structure. © 2013 Taylor & Francis Group, London

Damage Detection Using A Novel Time Series Methodology: Application To The Z24 Bridge Data

In this study, a novel time series analysis methodology is used for detection, localization, and quantification of damage. The methodology is based on creating ARX models (Auto-Regressive models with eXogenous input) for different sensor clusters. The output of each sensor in a cluster is used as an input to the ARX model to predict the output of the reference channel of that sensor cluster. After the ARX models for the healthy structure at each DOF are created, the same models are used for predicting the data from the damaged structure. The difference between the fit ratios is used as damage indicating feature. The methodology is applied to the experimental data coming from the Z24 Bridge. It is shown that the approach is successfully used for identification, localization, and quantification of different damage cases. The potential and advantages of the methodology are discussed along with the analysis results. The limitations and shortcomings of the methodology are also addressed. © 2010 Taylor & Francis Group, London

Critical Issues, Condition Assessment And Monitoring Of Movable Bridges: Image Processing For Open Gear Monitoring

Movable bridges are one of the least studied bridge types. In this paper, examples from a movable bridge evaluation study are presented based on the research conducted on a movable bridge in Florida over the last several years. Movable bridges face operational and maintenance challenges mainly due to complex structural, mechanical and electrical systems which, at the same time, provide their versatility. Although there are a few studies focusing on movable bridges, none of these studies provide a complete list of the problems related to the condition of movable bridge populations in conjunction with possible monitoring applications specific to these bridges. This study summarizes these issues related to movable bridges considering both the structural and mechanical components. After presenting the design and implementation of a monitoring system to a representative bascule bridge, analysis of image data for evaluating the lubrication levels in an open gear is presented. The findings from this analysis are compared with the maintenance logs. It is shown that continuous monitoring may provide invaluable information about safe, reliable and cost-effective operation and maintenance of movable bridges. © 2013 American Society of Civil Engineers

Non-target displacement measurement of structures using vision based approaches

Although vision based methods for displacement measurement have been used in civil engineering for more than a decade, most of those techniques required some sort target attachment. Using target attachment techniques for real-life structures such as bridges is not practical in most of the cases. In this study, a non-target vision based method for displacement measurement is developed by proposing a new type of virtual markers instead of physical targets. The key-points of measured locations are extracted by means of a robust computer vision technique named Scale Invariant Feature Transform (SIFT), and characteristics of the key-points show a potential ability to take the place of classical targets. To calculate the converting ratio between image coordinate and world coordinate, a camera calibration method is implemented while the conventional targets are no longer existence. The proposed method has been verified at the UCF 4-span bridge model and delivering good results at both static and dynamic displacement behavior of the model. Furthermore, the method can be seen as less complicated and more cost-effective than conventional approaches. © 2014 Taylor & Francis Group

Integration Of Computer Imaging And Sensor Data For Structural Health Monitoring Of Bridges

The condition of civil infrastructure systems (CIS) changes over their life cycle for different reasons such as damage, overloading, severe environmental inputs, and ageing due normal continued use. The structural performance often decreases as a result of the change in condition. Objective condition assessment and performance evaluation are challenging activities since they require some type of monitoring to track the response over a period of time. In this paper, integrated use of video images and sensor data in the context of structural health monitoring is demonstrated as promising technologies for the safety of civil structures in general and bridges in particular. First, the challenges and possible solutions to using video images and computer vision techniques for structural health monitoring are presented. Then, the synchronized image and sensing data are analyzed to obtain unit influence line (UIL) as an index for monitoring bridge behavior under identified loading conditions. Subsequently, the UCF 4-span bridge model is used to demonstrate the integration and implementation of imaging devices and traditional sensing technology with UIL for evaluating and tracking the bridge behavior. It is shown that video images and computer vision techniques can be used to detect, classify and track different vehicles with synchronized sensor measurements to establish an input-output relationship to determine the normalized response of the bridge. © 2010 IOP Publishing Ltd

Anomaly Detection With Signal And Image Processing For Structural Health Monitoring

It is widely accepted that Structural Health Monitoring (SHM) is a critical component for creating sustainable Civil Infrastructure Systems (CIS). The effectiveness of the data analysis methods used in the SHM system is one of the key factors that determine the success rate of the implementations. Since various types of measurements, e.g. acceleration, strain and image, can be utilized in the SHM systems, different data analysis methods should be developed for extracting useful information from large amounts of data. In this paper, the authors provide a rather general discussion of the critical aspects of SHM in the context of condition assessment and damage detection. A time series analysis based method is investigated for structural damage detection. Moreover, a computer vision based technique is explored for anomaly (or novelty) detection. It is shown that certain algorithms using these approaches can be developed for rapid extraction of information about the changes in the behavior of the structure. Examples from laboratory and real life tests are presented for verification purposes and the performances of these methodologies are discussed in light of the experimental results. Finally, research needs to improve the accuracy and applicability of SHM systems for advancing the sustainable CIS are discussed

Comparative Performance Analysis Of Two Bridges Designed Using Aashto And Florida I-Beam Girders

Today, almost 50% of all the new bridges built in the US are pre-stressed concrete bridges. Prestressed concrete bridges are considered due to their high strength and durability. Pre-stressed concrete girders perform well for longer spans by the application of a tensile force to reinforcing tendons. There are a number of different pre-stressed concrete girders with a variety of cross-sectional geometries and strands for a required span length and loading. AASHTO I-beams and bulb T-beams have been employed by a many Departments of Transportation as concrete bridge girders. A new prestressed beam called the Florida I-beam (FIB) is developed to replace AASHTO beams in order to enhance the efficiency, to provide a larger vertical clearance and to reduce the overall cost of bridges by reducing the number required girderswhenAASHTObeams are used. FIBs are designed to have higher load carrying capacity, more efficient fabrication, safer construction, increased lateral stiffness because of thicker top and bottom flanges. In this paper, a comparative analysis of two pre-stressed bridges, which have the same length, width and loading conditions, is presented. The first bridge has six AASHTO type III girders and the second bridge with the same general geometry and load-carrying characteristics has four FIBs. The performance of these bridges will be presented in terms of their load carrying capacity and reliability based on a number of scenarios. © 2013 Taylor & Francis Group, London