Search

The deflection of railroad bridges under in‐service loads is an important indicator of the structure's health. Over the past decade, an increasing number of studies have demonstrated the efficacy of using vision‐based approaches for displacement tracking of civil infrastructure. These studies have relied primarily on external processing of manually recorded videos of a structure's motion to estimate displacements. To date, vision‐based techniques applied to long‐term structural health monitoring have yet to be proven effective as an alternative to the traditional displacement measurement methods, such as linear variable differential transformers. This paper proposes a wireless SmartVision system (WSVS) that uses edge computing to directly output bridge displacements that can be sent to the end user. The system estimates displacements using both target‐free and target‐based approaches. A synchronized sensing framework is developed for multipoint displacement estimation using several wireless vision‐based nodes for full‐scale displacement‐based modal analysis of structures. Pose estimation using an AprilTag, a fiducial marker, is employed with a modified algorithm for improved displacement tracking of targets installed on a bridge, yielding subpixel accuracy. The robustness of the results in field conditions is enhanced by linking a tracking quality factor to each timestamp to handle vision‐related uncertainties. To meet the need for precise error metrics evaluation, an inexpensive cyber‐physical setup using a synthetic testing environment is also developed in this study. Following laboratory validation, field tests on a cable‐stayed pedestrian bridge were performed to demonstrate the efficacy of the proposed WSVS. [ABSTRACT FROM AUTHOR]

Structural vibration measurement is a crucial and necessary step for structural health monitoring. Recently, computer vision‐based techniques have been proposed by researchers to measure structural motion remotely. However, the direct application of vision‐based measurement to practical applications still faces some challenges, mainly because intrinsic camera vibration can introduce significant errors to the measurement results. In this study, a three‐stage approach using an embedded inertial measurement unit is proposed to compensate for the camera movement. First, camera rotations are estimated by employing a complementary filter with an adaptive gain to fuse gyroscope measurement and accelerometer data. Next, binary robust invariant scalable key‐point features are detected from the region of interest and tracked between video frames using a Kanade–Lucas–Tomasi tracker. Finally, structural acceleration is obtained by combining the information for the obtained structural features and the estimated nonstationary camera motion. The performance of the proposed approach is investigated using both a moving handheld camera and a camera mounted on the unmanned aerial vehicle in the laboratory. These results demonstrate that the proposed method can be effectively applied to measure structural vibration, without requiring stationary background features to be available in the video. [ABSTRACT FROM AUTHOR]

Structural displacement is an important quantity to assess the health of civil infrastructure. Vision‐based approaches using unmanned aerial vehicles (UAV) mounted with high‐resolution cameras have been proposed for this purpose. However, because the camera itself is moving with the UAV, any video obtained will contain both the motion of the structure and the motion of the camera. Planar homography can be used to eliminate the errors induced by the camera movement without the need for camera parameters. However, its direct application to large structures still has limitations, because capturing the undeformed regions, along with the measurement points on the structure, within a single image with sufficient resolution is seldom feasible. In this study, a new framework is presented to address these issues and facilitate the extraction of the structural displacement from videos taken by a UAV‐mounted camera. First, a two‐layer feedforward neural network (FNN) is adopted to obtain the image coordinates of the selected features of the structure on its stationary position, which are further used as homography features. Next, the structural displacement is estimated with the homography transformation matrix determined from the obtained homography features. Finally, the proposed approach is validated on both a six‐story shear‐building model in the laboratory and an elevator tower located in Zhongshan City, China. These results demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

This research investigates vision‐based automated bridge component recognition, which is critical for automating visual inspection of bridges during initial response after earthquakes. Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from images of complex scenes. One of the challenges in such scenarios is to get the recognition results consistent with high‐level scene structure using limited amount of training data. To impose the high‐level scene consistency, this research combines 10‐class scene classification and 5‐class bridge component classification. Three approaches are investigated to combine scene classification results into bridge component classification: (a) naïve configuration, (b) parallel configuration, and (c) sequential configuration of classifiers. The proposed approaches, sequential configuration in particular, are demonstrated to be effective in recognizing bridge components in complex scenes, showing less than 1% of accuracy loss from the naïve/parallel configuration for bridge images, and less than 1% false positives for the nonbridge images. [ABSTRACT FROM AUTHOR]

Structural optimization has progressed substantially over the last half century. However, the literature on optimization of structures under random excitation is limited. This study provides a framework for structural optimization subject to stochastic dynamic loading. Illustrative examples of structures under nonstationary seismic and stationary wind loads are presented to demonstrate the procedure. Both safety and serviceability are considered concurrently. The objective function for safety is given in terms of both harmful interstory drift and harmful interstory drift angle; whereas for serviceability, the objective function is defined in terms of floor acceleration. Safety and serviceability are shown to be competing objectives under both seismic and wind excitation; however, the contrast is less under wind excitation. The proposed optimization framework provides a rational manner in which engineering design tradeoffs can be made between the two competing objectives. [ABSTRACT FROM AUTHOR]

This work proposes an efficient and reliable method for damage localization in truss structures. The damage is localized on the basis of measured acceleration signals of the structure followed by simple statistical signal processing. It has three main advantages over many existing methods. First, it can be directly applied to real engineering structures without the need of identifying modal parameters or solving any global optimization problem. Second, the proposed method has higher sensitivity to damage than some other frequently used methods and allows to localize damage as small as a few percentages. Third, it is a model-free method, which does not require precise finite element model development or updating. Validation of the method has been conducted on numerical examples and laboratory-scale trusses. Two types of frequently used trusses have been selected for this study, namely, Howe and Bailey trusses. The presented experimental validation of the method shows its efficiency and robustness for damage localization in truss structures. [ABSTRACT FROM AUTHOR]

The traffic safety of a railway bridge is generally evaluated by levels of structural responses such as acceleration, vertical displacement, and deck twist. Whereas acceleration can be readily measured in general, acquiring displacement and twist responses in field testing is often a challenging task due to lack of appropriate sensors. As most existing displacement transducers are designed to measure at a single location, the deck twist which is calculated from four displacements requires costly and labor-intensive sensor instrumentation. To effectively address the issue, this study proposes an integral strategy for the traffic safety evaluation of railway bridges using multisensor data. The proposed approach provides a formulation to estimate the dense displacement necessary for obtaining twist responses using acceleration and strain measurements. Wireless sensors are adopted because of their intrinsic advantages in multimetric sensing of heterogeneous data, convenient sensor instrumentation, and high-fidelity time-synchronized data acquisition. The proposed approach for dense displacement estimation is numerically and experimentally validated using beam models. Subsequently, a full-scale experiment on a railway bridge is conducted to evaluate the traffic safety for high-speed trains at three different speeds of 280 km/h, 300 km/h, and 400 km/h. The acceleration, vertical displacement, and twist are obtained and compared with design limits to determine the traffic safety of the railway bridge. [ABSTRACT FROM AUTHOR]

An inexpensive and robust 3D localization system for tracking the position of a user in GPS- or WLAN-denied environments offers significant potential for improving decision-making tasks for civil and infrastructure engineering applications. To this end, an infrastructure-free approach for 3D event localization on commodity smartphones is presented. In the proposed method, the position of the user is continuously tracked based on the smartphone sensory data (the Forward approach) until the user reaches a certain event. Here, an event location refers to the 3D location of a user conducting value-added activities such as tasks involved in emergency response and field reporting of operational issues. Once an event is observed, the motion trajectory of the user is backtracked from the postevent landmark to reestimate the location of the event (the Backward approach). By integrating probability distributions of the Forward and Backward approaches together, the proposed method derives the most-likely location of the event. To validate the proposed approach, seven case studies are conducted in a multistory parking garage. The experimental results show that the probabilistic integration of the localization results from the Forward and Backward dead reckonings can produce more accurate 3D localization results when compared to a single best estimate from a one-way dead reckoning process. Lessons learned from several real-world case studies and open research challenges in improving localization accuracy are discussed in detail. [ABSTRACT FROM AUTHOR]

Wireless sensors are now becoming practical alternatives to traditional wired sensors in monitoring civil structures. Though many have been reported on acceleration-based monitoring of civil structures using wireless sensor networks, sensor attitude that may be different from instrumentation plan has been a seemingly overlooked issue behind performance validation of the network. In this article, a technique to correct the sensor attitude is proposed for the wireless sensor network that measures 3D acceleration of civil structures. Six simple formulas to assess the well-known 3D Euler angles (i.e., roll, pitch, and yaw) are derived using the gravity extracted from measured 3D acceleration and nonchanging direction of sensors on a stationary structure. The proposed technique is validated at a large-scale wireless sensor network with 22 sensors in the respective attitudes on a truss bridge. First, attitudes assessed by the proposed method are compared with instrumentation plan. Then, mode shapes obtained before and after the correction are compared with those from finite element model. Comparison shows that quality of the mode shapes improves significantly by small amount of attitude correction less than 7°. [ABSTRACT FROM AUTHOR]