A real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by actual experimental values was used to verify the proposed method. To obtain a realistic vehicle distribution for the bridge, vehicle type and actual headways of moving vehicles were taken, and the measured vehicle distribution was generalized using Pearson Type III theory. Twenty-five load scenarios were created with assumed vehicle speeds of 40 km/h, 60 km/h, and 80 km/h. The results indicate that the model can reasonably predict the overall displacements of the bridge (which is difficult to measure) from the strain (which is relatively easy to measure) in the field in real time.
Spalling of concrete fragments due to the deterioration of concrete structures can cause property damage or serious and even fatal accidents; thus, there is a need to detect such deterioration. Generally, the hammering test is employed as the main inspection method to prevent such concrete spalling; however, it requires close contact with the structure being tested. Getting close to the structure for inspection is expensive and time consuming, and if the structure is high up, there is a risk of falling. Therefore, in this study, we developed a system for inspecting concrete structures without approaching them, using infrared thermography. In order to detect floating and delamination using infrared thermography, it is necessary to find temperature irregularities caused by such damage from an infrared image, but such an inspection method has not been realized so far. There are two main reasons for this. First, it is difficult to evaluate whether the concrete structure is in an appropriate temperature condition suitable for detecting the floating and delamination. Second, it is difficult to detect temperature irregularities caused by floating and delamination among the various causes of temperature irregularities. In this study, we resolved these issues by developing equipment to investigate whether the object is in an appropriate temperature condition for proper photography and by developing a machine learning-based method to automatically detect only the temperature irregularities caused by floating and delamination. By resolving these issues, we have developed a promising novel inspection method for the prevention of concrete spalling, which is reported in this article.
Asama (2021): Innovative technologies for infrastructure construction and maintenance through collaborative robots based on an open design approach, Advanced Robotics,
We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning algorithm to improve the efficiency of damage assessment in the event of slope failure disaster. There is limited research on detecting landslides by deep learning, and the lack of training data is an important issue to be resolved, as aerial photographs are not taken with sufficient frequency during a disaster. This study attempts to use CutMix-based augmentation to improve detection accuracy. We also compare the detection results obtained by augmentation of multiple patterns. In the comparison of the not augmented data case, the recall increased by 0.186 in the case using the augmented data with the shape of the slope failure region maintained. When the image data was augmented while maintaining the shape of the slope failure region, the recall score indicated the low oversights in the prediction result is 0.701. This is an increase of 0.186 compared to the case where no augmentation was performed. In addition, the F1 score was 0.740, this also increased by 0.139, and high values were obtained for other indicators. Therefore, the method proposed in this study is greatly useful for grasping slope failure regions because of the detection with high accuracy, as described above.
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