Lean duplex stainless steel (LDSS), which is expected to be applied in bridges, exhibits a rounded stress-strain curve. For this reason, a constitutive equation that is able to express the curve accurately is required for ultimate strength analysis of LDSS structures. The authors have already proposed the modified Ramberg-Osgood (MRO) curve as a suitable equation. However, to describe the equation, not only are 0.2% proof stress and tensile strength required, as specified in the common material standard and mill certificates, but also mechanical properties such as proportion limit, etc. The present study collected tension coupon test results for LDSS, and created a simple prediction equation by means of linear regression analysis. It also predicted the mechanical properties by using Random Forest (RF), a machine learning method. When comparison was made, it was revealed that RF predicts mechanical properties as accurately as a prediction equation.

When constructing a 3-D model of a bridge for maintenance purposes, it is considered easier to confirm the position of the damage and the shape of the model by reflecting the actual location of the damage in the model. Cracks are typical damage, and there have been many studies on methods of automatically detecting cracks from images of the concrete surface. However, it is still difficult to detect the cracks if nonconcrete objects are reflected in the image. This study makes it possible to detect cracks from a number of captured images by extracting in advance the concrete area only, using semantic segmentation from the images of the bridge. Furthermore, based on the image captured in detecting a crack, it was possible to construct a 3-D model of the bridge using Structure from Motion, and to construct a 3-D model in which the positions of the cracks were reflected. This paper is the English translation of the authors’ previous work [Yamane, T. et al. (2020). “Reflection of crack locations in 3-D models of bridges using semantic segmentation” Intelligence, Informatics and Infrastructure, Vol. 1, No. J1, 491-497 (in Japanese)].

To date, a large number of inspection records have been compiled following bridge inspections. However, most of these records have been saved either as hard copies or in PDF format, making it difficult to utilize the accumulated data. If data could be automatically extracted from these inspection records, and a database of information records created, it would be possible to analyze the data in a huge number of past inspection records. However, many structural element numbers which are important for obtaining information, such as the location of damaged members, overlap with the lines of structural members in bridge drawings, making it difficult to extract them through general optical character recognition (OCR) processing. This study extracted the element numbers in bridge drawings in the inspection records utilizing object-detection based on deep learning. The results confirm that it is possible to extract the element numbers that overlap with the straight lines with considerable accuracy. This paper is the English translation of the authors’ previous work [Yamane, T. et al.: , (2020). Extracting textual information from bridge inspection records using deep learning, Intelligence, Informatics and Infrastructure, Vol. 1, No. J1, 71-77 (in Japanese)].

Evaluation of snow cover condition is essential to manage the road condition such as snow removal on the roof of a building or on the road. In particular, evaluation of snow depth requires the use of expensive sensors such as lasers in addition to visual evaluation. Image processing techniques such as deep learning have improved in recent years, and many studies have been conducted to evaluate the snow cover state using images from surveillance cameras. In particular, in the surveillance images of the road surface and shoulders by wayside cameras, the location information is clear because the shoot-ing location is fixed, and the angle of view changes relatively little. In this research, an AI method was applied to the evaluation of the snow depth of the snow on the shoulder using a surveillance camera.