Conference Paper
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Condition assessment of aging structures has reached at a critical level in the US. Evaluation of accurate and fast methods without creating even further damage has become a necessity in structural health monitoring area. This study tries to explain the new methods and frameworks that may be replaced with conventional evaluation methods to conduct the same studies faster, requiring less cost and labor and with the same or better accuracy. The main idea is to implement computer vision and infrared imaging based technologies into local and global structural health monitoring practices. Brief explanations of their working principle along with the results from past studies and future recommendations are given. Finally, a possible decision-making framework combining the explained methods is proposed.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... With the growing potential of camera-based methods, a complete noncontact SHM using NDE along with effective utilization in decision making is possible. Figure 1 summarizes the complete methodology proposed by Catbas et al. (2017) [7]. ...
... With the growing potential of camera-based methods, a complete noncontact SHM using NDE along with effective utilization in decision making is possible. Figure 1 summarizes the complete methodology proposed by Catbas et al. (2017) [7]. ...
Article
Full-text available
Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.
... With daily traffic and other external effects, bridges are undergoing with structural changes, deterioration and damages over time. Currently, human visual inspection is still a common approach to detect defects and most of the decisions are made by inspectors' experiences ( Catbas et al. 2017). For safe operation, timely maintenance and convenient management in aspect of structural problems, effective sensing technologies and analytical approaches are necessary to detect the structural changes and damages and give reliable condition assessment and performance evaluation timely and sufficiently ( Zaurin et al. 2015). ...
... With the benefits of interdisciplinary integrations, various advanced sensing technologies such as elastomagneto-electric (EME) sensor for in-service steel cable forces measurement ( Duan et al. 2015), wireless sensors for dynamic monitoring (Celik et al. 2018b), fiber Bragg grating (FBG) sensor for strain monitoring ( Ye et al. 2016dYe et al. , 2017, LiDAR scanning for structural condition assessment ( Chen et al. 2012), skin-type sensor for strain measurement ( Kong et al. 2018), infrared thermography for automated concrete deck inspection ( Catbas et al. 2017) and visionbased bridge monitoring at global level ( Catbas et al. 2018), etc. have been employed in current research and practice. Among these technologies, vision-based approaches are gathering increasing attention in the field of SHM ( Dong and Catbas 2019;Ye et al. 2016a) due to the advantages such as non-contact, long distance, low cost, time saving and ease of use. ...
Preprint
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.
Chapter
Full-text available
Smart infrastructures aim more efficient and accurate methods of routine inspection for long-term monitoring of the infrastructure to make smarter decision on maintenance and rehabilitation. Although some recent technologies (i.e., robotic techniques) that are currently in practice can collect objective, quantified data, the inspector’s own expertise is still critical in many instances. Yet, these technologies are designed to replace human expertise, or are ineffective in terms of saving time and labor. This chapter investigates a new methodology for structural inspections with the help of mixed reality technology and real-time machine learning to accelerate certain tasks of the inspector such as detection, measurement, and assessment of defects, and easy accessibility to defect locations. A functional, real-time machine learning system that can be ideally deployed in mixed reality devices and headsets which can be used by inspectors during their routine concrete infrastructure inspection is introduced. The deep learning models to be employed in the AI system can localize a concrete defect in real time and further analyze it by performing pixel wise segmentation while running on a mobile device architecture. First, a sufficiently large database of concrete defect images is gathered from various sources including publicly available crack and spalling datasets, real-world images taken during bridge inspections, and the public images from the internet search results. For defect localization, various state-of-the-art deep learning model architectures are investigated based on their memory allocation, inference speed, and flexibility to deploy different deep learning platforms. YoloV5s model was found to be the optimal model architecture for concrete defect localization to be deployed in the mixed reality system. For defect quantification, several segmentation architectures with three different classification backbones are trained on the collected image dataset with segmentation labels. Based on the model evaluation results, the PSPNet with EfficientNet-b0 backbone is found to be the best performing model in terms of inference speed and accuracy. The selected models for defect localization and quantification are deployed to the mixed reality platform and image tracking libraries are configured in the platform environment, and accurate distance estimation is accomplished using a calibration process. Lastly, a methodology for condition assessment of concrete defects using the mixed reality system is discussed. The proposed methodology can locate and track the defects using the mixed reality platform, which can eventually be transferred to cloud data and potentially used for remote assessments or updating a digital twins or BIMs.
Preprint
Full-text available
Structural Health Monitoring (SHM) has been continuously benefiting from the advancements in the field of data science. Various types of Artificial Intelligence (AI) methods have been utilized for the assessment and evaluation of civil structures. In AI, Machine Learning (ML) and Deep Learning (DL) algorithms require plenty of datasets to train; particularly, the more data DL models are trained with, the better output it yields. Yet, in SHM applications, collecting data from civil structures through sensors is expensive and obtaining useful data (damage associated data) is challenging. In this paper, 1-D Wasserstein loss Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage associated vibration datasets that are similar to the input. For the purpose of vibration-based damage diagnostics, a 1-D Deep Convolutional Neural Network (1-D DCNN) is built, trained, and tested on both real and generated datasets. The classification results from the 1-D DCNN on both datasets resulted to be very similar to each other. The presented work in this paper shows that for the cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP can successfully generate data for the model to be trained on. Keywords: 1-D Generative Adversarial Networks (GAN), Deep Convolutional Generative Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), 1-D Convolutional Neural Networks (CNN), Structural Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage Detection
Article
Full-text available
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.
Article
Most of the existing vision-based displacement measurement methods require manual speckles or targets to improve the measurement performance in non-stationary imagery environments. To minimize the use of manual speckles and targets, feature points regarded as the virtual markers can be a utilized for non-target measurement. In this study, an advanced feature matching strategy is presented, which replaces the hand-crafted descriptors with learned descriptors, named Visual Geometry Group of University of Oxford (VGG) descriptors to achieve better performance. The feasibility and performance of the proposed method is verified by comparative studies with a laboratory experiment on a two-span bridge model and then with a field application on a railway bridge. The proposed approach of integrated use of Scale Invariant Feature Transform (SIFT) and VGG improved the measurement accuracy by about 24% when compared to commonly utilized existing feature matching-based displacement measurement method using SIFT feature and descriptor.
ResearchGate has not been able to resolve any references for this publication.