The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.
Wireless sensor networks (WSNs) are promising solutions for large infrastructure monitoring because of their ease of installation, computing and communication capability, and cost-effectiveness. Long-term Civil structural health monitoring (SHM), however, is still a challenge because it requires continuous data acquisition for the detection of random events such as earthquakes and structural collapse. To achieve long-term operation, it is necessary to reduce the power consumption of sensor nodes designed to capture random events and, thus, enhance structural safety. In this paper, we present an event-based sensing system design based on an ultra-low-power microcontroller with programmable event-detection mechanism to allow continuous monitoring; the device is triggered by vibration, strain, or a timer and has a programmed threshold, resulting in ultra-low-power consumption of the sensor node. Furthermore, the proposed system can be easily reconfigured to any existing wireless sensor platform to enable ultra-low power operation. For validation, the proposed system was integrated with a commercial wireless platform to allow strain, acceleration, and time-based triggering with programmed thresholds and current consumptions of 7.43 and 0.85 mA in active and inactive modes, respectively.
Abstract. With the aims of ensuring safety and decreasing maintenance costs, previous studies in bridge inspection research have worked to elucidate damage indicators and understand their correspondence to structural deficiency. During this process, understanding how an inspector looks at a structure comprehensively as well as how they localize on damage is vital to examining diagnostic bias and how it can play a role in the preservation and maintenance process. To understand human perception and assess the humaninfrastructure interaction during the feature extraction process, eye tracking can be useful. Eye tracking data can accurately map where a human is looking and what they are focusing on based on metrics such as fixation, saccade, pupil dilation, and scan path. The present research highlights the use of eye tracking metrics for recognizing and inferring human implicit attention and intention while performing a structural inspection. These metrics will be used to learn the behavior of human eyes and how detection tasks can change a person’s overall behavior. A preliminary study has been carried out for damage detection to analyze key features that are important for understanding human-infrastructure interaction during damage assessment. These eye tracking features will lay the foundation for human intent prediction and how an inspector performs inspection on historic structures for existing types of damage. In future, the results of this work will be used to train a machine learning agent for autonomous and reactive decision making.
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