Monitoring concrete bridge decks using infrared thermography with high speed vehicles

Hiasa, Shuhei,Catbas, F. Necati,Matsumoto, Masato,Mitani, Koji Techno-Press 2016 Structural monitoring and maintenance Vol.3 No.3

There is a need for rapid and objective assessment of concrete bridge decks for maintenance decision making. Infrared Thermography (IRT) has great potential to identify deck delaminations more objectively than routine visual inspections or chain drag tests. In addition, it is possible to collect reliable data rapidly with appropriate IRT cameras attached to vehicles and the data are analyzed effectively. This research compares three infrared cameras with different specifications at different times and speeds for data collection, and explores several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspection at normal driving speeds. These results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. It is observed that nighttime would be the most suitable time frame with less false detections and interferences from the sunlight and less adverse effect due to direct sunlight, making more "noise" for the IRT results. This study also revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.

An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis

Masoud Malekzadeh,Necati Catbas,Mustafa Gul,권일범 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.14 No.5

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis

Malekzadeh, Masoud,Gul, Mustafa,Kwon, Il-Bum,Catbas, Necati Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.14 No.5

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

Sensor clustering technique for practical structural monitoring and maintenance

Celik, Ozan,Terrell, Thomas,Gul, Mustafa,Catbas, F. Necati Techno-Press 2018 Structural monitoring and maintenance Vol.5 No.2

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

Structural monitoring of movable bridge mechanical components for maintenance decision-making

Gul, Mustafa,Dumlupinar, Taha,Hattori, Hiroshi,Catbas, Necati Techno-Press 2014 Structural monitoring and maintenance Vol.1 No.3

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

A completely non-contact recognition system for bridge unit influence line using portable cameras and computer vision

Chuan-Zhi Dong,Selcuk Bas,F. Necati Catbas 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.24 No.5

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

A FRF-based algorithm for damage detection using experimentally collected data

Garcia-Palencia, Antonio,Santini-Bell, Erin,Gul, Mustafa,Catbas, Necati Techno-Press 2015 Structural monitoring and maintenance Vol.2 No.4

Automated damage detection through Structural Health Monitoring (SHM) techniques has become an active area of research in the bridge engineering community but widespread implementation on in-service infrastructure still presents some challenges. In the meantime, visual inspection remains as the most common method for condition assessment even though collected information is highly subjective and certain types of damage can be overlooked by the inspector. In this article, a Frequency Response Functions-based model updating algorithm is evaluated using experimentally collected data from the University of Central Florida (UCF)-Benchmark Structure. A protocol for measurement selection and a regularization technique are presented in this work in order to provide the most well-conditioned model updating scenario for the target structure. The proposed technique is composed of two main stages. First, the initial finite element model (FEM) is calibrated through model updating so that it captures the dynamic signature of the UCF Benchmark Structure in its healthy condition. Second, based upon collected data from the damaged condition, the updating process is repeated on the baseline (healthy) FEM. The difference between the updated parameters from subsequent stages revealed both location and extent of damage in a "blind" scenario, without any previous information about type and location of damage.

Multi-point earthquake response of the Bosphorus Bridge to site-specific ground motions

Selcuk Bas,Nurdan Memisoglu Apaydin,Ebru Harmandar,Necati Catbas 국제구조공학회 2018 Steel and Composite Structures, An International J Vol.26 No.2

The study presents the earthquake performance of the Bosphorus Bridge under multi-point earthquake excitation considering the spatially varying site-specific earthquake motions. The elaborate FE model of the bridge is firstly established depending on the new considerations of the used FEM software specifications, such as cable-sag effect, rigid link and gap elements. The modal analysis showed that singular modes of the deck and the tower were relatively effective in the dynamic behavior of the bridge due to higher total mass participation mass ratio of 80%. The parameters and requirements to be considered in simulation process are determined to generate the spatially varying site-specific ground motions. Total number of twelve simulated ground motions are defined for the multi-support earthquake analysis (<i>Mp-sup</i>). In order to easily implement multi-point earthquake excitation to the bridge, the practice-o rientedprocedure is summarized. The results demonstrated that the <i>Mp-sup</i> led to high increase in sectional forces of the critical components of the bridge, especially tower base section and tensile force of the main and back stay cables. A close relationship between the dynamic response and the behavior of the bridge under the <i>Mp-sup</i> was also obtained. Consequently, the outcomes from this study underscored the importance of the utilization of the multi-point earthquake analysis and the necessity of considering specifically generated earthquake motions for suspension bridges. The study presents the earthquake performance of the Bosphorus Bridge under multi-point earthquake excitation considering the spatially varying site-specific earthquake motions. The elaborate FE model of the bridge is firstly established depending on the new considerations of the used FEM software specifications, such as cable-sag effect, rigid link and gap elements. The modal analysis showed that singular modes of the deck and the tower were relatively effective in the dynamic behavior of the bridge due to higher total mass participation mass ratio of 80%. The parameters and requirements to be considered in simulation process are determined to generate the spatially varying site-specific ground motions. Total number of twelve simulated ground motions are defined for the multi-support earthquake analysis (<i>Mp-sup</i>). In order to easily implement multi-point earthquake excitation to the bridge, the practice-oriented procedure is summarized. The results demonstrated that the <i>Mp-sup</i> led to high increase in sectional forces of the critical components of the bridge, especially tower base section and tensile force of the main and back stay cables. A close relationship between the dynamic response and the behavior of the bridge under the <i>Mp-sup</i> was also obtained. Consequently, the outcomes from this study underscored the importance of the utilization of the multi-point earthquake analysis and the necessity of considering specifically generated earthquake motions for suspension bridges.

A structural damage detection approach using train-bridge interaction analysis and soft computing methods

He, Xingwen,Kawatani, Mitsuo,Hayashikawa, Toshiro,Kim, Chul-Woo,Catbas, F. Necati,Furuta, Hitoshi Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.13 No.5

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.

A structural damage detection approach using train-bridge interaction analysis and soft computing methods

Xingwen He,Mitsuo Kawatani,Toshiro Hayashikawa,Chul-Woo Kim,F. Necati Catbas,Hitoshi Furuta 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.13 No.5

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.