In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 × 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8.

Machine learning models have been developed to perform damage detection using images to improve bridge inspection efficiency. However, in damage detection using images alone, the 3D coordinates of the damage cannot be recorded. Furthermore, the accuracy of the detection depends on the quality of the images. This paper proposes a method to integrate and record the damage detected from multiple images into a 3D model using deep learning to detect the damage from bridge images and structure from motion to identify the shooting position. The proposed method reduces the variability of the detection results between images and can assess the scale of damage or, conversely, where there is no damage and the extent of inspection omissions. The proposed method has been applied to a real bridge, and it has been shown that the actual damage locations can be recorded as a 3D model.

Summary After the 2011 Great East Japan Earthquake, long‐term vibration measurement using high‐density instruments is one of the most critical issues for structural‐health‐monitoring owing to increasing deterioration and threat of future large earthquakes. Because of the high initial and running costs of traditional monitoring systems, smart‐device‐based measurement system is considered as a simple and easy solution. In this paper, the effectiveness of in‐built sensor, data transfer via wireless local area network, data acquisition to a synchronize cloud server, and trigger function using shaking table tests were firstly examined. A measurement system including a group of sensors has been established successfully based on the “control center” from which the trigger command can be send to other sensors immediately as any sensor/sensors is/are triggered. Then, the system is applied to seismic‐response and environment‐vibration measurement at existing structures. Results show that the observable acceleration level of smart devices is more than 5 gal in the frequency range of 0.1 to 10 Hz. The possible sampling rate is 100 Hz. Though it is unstable, correction methods have been proposed. Continuous measurement and data transfer is possible without data loss. Dynamic properties extracted from smart‐device‐based system is very similar to those extracted from high‐quality‐sensor‐based system.

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In this study, a simple and customizable convolution neural network framework was used to train a vibration classification model that can be integrated into the measurement application in order to realize accurate and real-time bridge vibration status on mobile platforms. The inputs for the network model are basically the multichannel time-series signals acquired from the built-in accelerometer sensor of smartphones, while the outputs are the predefined vibration categories. To verify the effectiveness of the proposed framework, data collected from long-term monitoring of bridge were used for training a model, and its classification performance was evaluated on the test set constituting the data collected from the same bridge but not used previously for training. An iOS application program was developed on the smartphone for incorporating the trained model with predefined classification labels so that it can classify vibration datasets measured on any other bridges in real-time. The results justify the practical feasibility of using a low-latency, high-accuracy smartphone-based system amid which bottlenecks of processing large amounts of data will be eliminated, and stable observation of structural conditions can be promoted.

A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage. The proposed method takes into account data type, data and wavelet characteristics, similarity, sharing information, and data recovery accuracy. The novelty of this study lies in integrating multi-criteria which are associated directly with features that correlated well with change in structures due to damage, including common criteria such as energy, entropy, linear correlation index, and variance. Also, it introduces and considers new proposed measures, such as wavelet-based nonlinear correlation such as cosh spectral distance and mutual information, wavelet-based energy fluctuation, measures-based recovery accuracy, such as sensitive feature extraction, noise reduction, and others to evaluate various base wavelets’ function capabilities for appropriate decomposition and reconstruction of structural dynamic responses. The proposed method is verified by experimental and simulated data. The results revealed that the proposed method has a satisfactory performance for base wavelet selection and the small order of Daubechies and Symlet provide the best results, especially order 3. The idea behind our proposed framework can be applied to other structural applications.

Displacement measurement is one of the most important methods for structural health monitoring. However, because of high cost and heavy on-site installation, conducting displacement measurement using conventional sensors is not easy in practice. In this case, with improvements in image sensors in smart devices, it seems possible to measure displacement using image processing methods. In this study, smart device-based bridge displacement monitoring system was developed. Images captured by the image sensor are processed in real time to obtain the feature-responsive displacement. First, shaking table tests based on sine wave loading were conducted so that the reliable domain for frequency and amplitude measurement for different smart devices were identified by comparing with the reference-accurate displacement sensor. Then, on-site application and stability of the proposed system were demonstrated through 1-day field measurement on a real in-service bridge.Comparison of the displacement due to traffic and temperature using smart devices with accurate displacement sensor shows significant potential of the proposed approach.

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