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Non-Destructive Evaluation of Concrete Structures Using High Resolution Digital Image and Infrared Thermography Technology
Title
Non-Destructive Evaluation of Concrete Structures Using High Resolution Digital Image and Infrared Thermography Technology.
Creator
Watase, Azusa, Catbas, Fikret, Tatari, Mehmet, Nam, Boo Hyun, University of Central Florida
Abstract / Description
As existing bridge structures age, they are susceptible to the effects of deterioration, damage and other deleterious processes. These effects hamper the capacity and efficiency of transportation networks and adversely impact local, regional and national economic growth. As a result, bridge authorities and other professionals have become more sensitive to maintenance issues related to this aging infrastructure. While highway bridge condition have been monitored by visual inspection, non...
Show moreAs existing bridge structures age, they are susceptible to the effects of deterioration, damage and other deleterious processes. These effects hamper the capacity and efficiency of transportation networks and adversely impact local, regional and national economic growth. As a result, bridge authorities and other professionals have become more sensitive to maintenance issues related to this aging infrastructure. While highway bridge condition have been monitored by visual inspection, non-destructive evaluation (NDE) technologies have also been developing and they are expected to be utilized for effective management of highway bridges or other civil infrastructure systems. Efficient use of these technologies saves time spent or bridge inspections, and also helps the bridge authorities for management decision-making. One of the NDE technologies is the image-based technology. In this thesis research, image-based technologies using high resolution digital images (HRDI) and infrared thermography image (IRTI) are introduced, described and implemented.First, a review of the mechanisms of these technologies is presented. Due to the specific engineering utilization and recent technological development, there is a need to validate effectiveness of HRDI and IRTI for their practical use for engineering purpose. For this reason, a pilot project using these technologies was conducted at an in-service bridge and a parking structure with the support of Florida Department of Transportation District 5 and the results are presented in this thesis. Secondly, in order to explore and enhance the usability of infrared thermography technology (IRTI), experiments on campus and on another bridge were conducted to determine the best time to test bridges and the sensitivity of IRTI to delamination volume. Since the accuracy of damage detection using infrared thermography technology is greatly affected by daily temperature variation, it is quite important to estimate an appropriate duration for infrared thermography inspection prior to the inspection. However, in current practice, the way to estimate the duration is to monitor the temperature of the concrete surface. Since the temperature varies depending on the area or region, there is a need to visit the bridge before the actual test and monitor the temperature variation. This requires additional visits to the bridge site and also access to the bridge for measuring concrete temperature. Sometimes, this can be a practical issue. In this research, in order to estimate an appropriate duration without visiting bridges, a practical method is explored by monitoring and analyzing variation of concrete surface temperature at one location and projected to another location by also incorporating other factors that affect the concrete temperature, such as air temperature and humidity. For this analysis, specially-designed concrete plates of a few types of thickness and shapes are used and the regression analysis is employed to establish a relationship between environmental effects and temperature variation between two different sites. The results have been promising for this research study and it is shown that HRDI and IRTI are excellent technologies for assessing concrete structures in a very practical manner.
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Date Issued
2013
Identifier
CFE0004956, ucf:49581
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004956
Analytical And Experimental Study Of Monitoring For Chain-Like Nonlinear Dynamic Systems
Title
Analytical And Experimental Study Of Monitoring For Chain-Like Nonlinear Dynamic Systems.
Creator
Paul, Bryan, Yun, Hae-Bum, Catbas, Fikret, Chopra, Manoj, University of Central Florida
Abstract / Description
Inverse analysis of nonlinear dynamic systems is an important area of research in the ?eld of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system...
Show moreInverse analysis of nonlinear dynamic systems is an important area of research in the ?eld of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system identification of multi-degree-of-freedom dynamic systems. However, those methods are usually limited to linear systems and known for poor sensitivity to localized damage. On the other hand, non-parametric identification methods (e.g., artificial neural networks) are advantageous to identify time-varying nonlinear systems due to unpredictable damage. However, physical interpretation of non-parametric identification results is not as straightforward as those of the parametric methods. In this study, the Multidegree-of-Freedom Restoring Force Method (MRFM) is employed as a semi-parametric nonlinear identification method to take the advantages of both the parametric and non-parametric identification methods.The MRFM is validated using two realistic experimental nonlinear dynamic tests: (i) large-scale shake table tests using building models with different foundation types, and (ii) impact test using wind blades. The large-scale shake table test was conducted at Tongji University using 1:10 scale 12-story reinforced concrete building models tested on three different foundations, including pile, box and fixed foundation. The nonlinear dynamic signatures of the building models collected from the shake table tests were processed using MRFM (i) to investigate the effects of foundation types on nonlinear behavior of the superstructure and (ii) to detect localized damage during the shake table tests. Secondly, the MRFM was applied to investigate the applicability of this method to wind turbine blades. Results are promising, showing a high level of nonlinearity of the system and how the MRFM can be applied to wind-turbine blades. Future studies were planned for the comparison of physical characteristic of this blade with blades created made of other material.
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Date Issued
2013
Identifier
CFE0004734, ucf:49818
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004734
The Effects of Assumption on Subspace Identification Methods Using Simulation and Experimental Data
Title
The Effects of Assumption on Subspace Identification Methods Using Simulation and Experimental Data.
Creator
Kim, Yoonhwak, Yun, Hae-Bum, Catbas, Fikret, Mackie, Kevin, Nam, Boo Hyun, Behal, Aman, University of Central Florida
Abstract / Description
In the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of...
Show moreIn the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of sensors is limited by cost and difficulty of data archive. Furthermore, some informative data has technical difficulty when measuring input force and, even if obtaining the desired data were possible, it could include a lot of noise in the measuring data. As a result, researchers have developed many analytical tools with limited informative data. Subspace identification method is used one of tools in these achievements.Subspace identification method includes three different approaches: Deterministic Subspace Identification (DSI), Stochastic Subspace Identification (SSI), and Deterministic-Stochastic Subspace Identification (DSSI). The subspace identification method is widely used for fast computational speed and its accuracy. Based on the given information, such as output only, input/output, and input/output with noises, DSI, SSI, and DSSI are differently applied under specific assumptions, which could affect the analytical results. The objective of this study is to observe the effect of assumptions on subspace identification with various data conditions. Firstly, an analytical simulation study is performed using a six-degree-of-freedom mass-damper-spring system which is created using MATLAB. Various conditions of excitation insert to the simulation test model, and its excitation and response are analyzed using the subspace identification method. For stochastic problems, artificial noise is contained to the excitation and followed the same steps. Through this simulation test, the effects of assumption on subspace identification are quantified.Once the effects of the assumptions are studied using the simulation model, the subspace identification method is applied to dynamic response data collected from large-scale 12-story buildings with different foundation types that are tested at Tongji University, Shanghai, China. Noise effects are verified using three different excitation types. Furthermore, using the DSSI, which has the most accurate result, the effect of different foundations on the superstructure are analyzed.
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Date Issued
2013
Identifier
CFE0004703, ucf:49822
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004703
Structural Health Monitoring using Novel Sensing Technologies and Data Analysis Methods
Title
Structural Health Monitoring using Novel Sensing Technologies and Data Analysis Methods.
Creator
Malekzadeh, Seyedmasoud, Catbas, Fikret, Yun, Hae-Bum, Tatari, Mehmet, Moslehy, Faissal, Gul, Mustafa, University of Central Florida
Abstract / Description
The main objective of this research is to explore, investigate and develop the new data analysis techniques along with novel sensing technologies for structural health monitoring applications. The study has three main parts. First, a systematic comparative evaluation of some of the most common and promising methods is carried out along with a combined method proposed in this study for mitigating drawbacks of some of the techniques. Secondly, non-parametric methods are evaluated on a real life...
Show moreThe main objective of this research is to explore, investigate and develop the new data analysis techniques along with novel sensing technologies for structural health monitoring applications. The study has three main parts. First, a systematic comparative evaluation of some of the most common and promising methods is carried out along with a combined method proposed in this study for mitigating drawbacks of some of the techniques. Secondly, non-parametric methods are evaluated on a real life movable bridge. Finally, a hybrid approach for non-parametric and parametric method is proposed and demonstrated for more in depth understanding of the structural performance. In view of that, it is shown in the literature that four efficient non-parametric algorithms including, Cross Correlation Analysis (CCA), Robust Regression Analysis (RRA), Moving Cross Correlation Analysis (MCCA) and Moving Principal Component Analysis (MPCA) have shown promise with respect to the conducted numerical studies. As a result, these methods are selected for further systematic and comparative evaluation using experimental data. A comprehensive experimental test is designed utilizing Fiber Bragg Grating (FBG) sensors simulating some of the most critical and common damage scenarios on a unique experimental structure in the laboratory. Subsequently the SHM data, that is generated and collected under different damage scenarios, are employed for comparative study of the selected techniques based on critical criteria such as detectability, time to detection, effect of noise, computational time and size of the window. The observations indicate that while MPCA has the best detectability, it does not perform very reliable results in terms of time to detection. As a result, a machine-learning based algorithm is explored that not only reduces the associated delay with MPCA but further improves the detectability performance. Accordingly, the MPCA and MCCA are combined to introduce an improved algorithm named MPCA-CCA. The new algorithm is evaluated through both experimental and real-life studies. It is realized that while the methods identified above have failed to detect the simulated damage on a movable bridge, the MPCA-CCA algorithm successfully identified the induced damage. An investigative study for automated data processing method is developed using non-parametric data analysis methods for real-time condition maintenance monitoring of critical mechanical components of a movable bridge. A maintenance condition index is defined for identifying and tracking the critical maintenance issues. The efficiency of the maintenance condition index is then investigated and demonstrated against some of the corresponding maintenance problems that have been visually and independently identified for the bridge.Finally, a hybrid data interpretation framework is designed taking advantage of the benefits of both parametric and non-parametric approaches and mitigating their shortcomings. The proposed approach can then be employed not only to detect the damage but also to assess the identified abnormal behavior. This approach is also employed for optimized sensor number and locations on the structure.
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Date Issued
2014
Identifier
CFE0005207, ucf:50648
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005207
Structural Identification through Monitoring, Modeling and Predictive Analysis under Uncertainty
Title
Structural Identification through Monitoring, Modeling and Predictive Analysis under Uncertainty.
Creator
Gokce, Hasan, Catbas, Fikret, Chopra, Manoj, Mackie, Kevin, Yun, Hae-Bum, DeMara, Ronald, University of Central Florida
Abstract / Description
Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior...
Show moreBridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure's response and performance in a probabilistic manner.The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric St-Id approach is employed without the use of an analytical model. The new methodology, which is successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels.
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Date Issued
2012
Identifier
CFE0004232, ucf:48997
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004232