Rubber bearings are now commonly used to prevent seismic damage to bridges; however, they can be destroyed by a major earthquake. Even if the bearing is not completely destroyed, it may sustain internal damage. Such internal damage is difficult to detect because they have a rubber coating. By conducting a numerical analysis, this study verified the propagation of elastic waves in a laminated rubber bearing, and used the data to detect damage using a machine learning anomaly detection technique. The results showed that damage inside a rubber bearing can be detected using the one-class support vector machine anomaly detection method.

To reduce the seismic response of small buildings, the authors propose a magnetically levitated foundation insulated from the ground by magnets and supported by a displacement control material. In this study, metal components were used to improve the levitation force and damping characteristics. A static loading test indicated that steel plates on the magnets increased the levitation force. Electromagnetic field analysis showed that a copper plate between the magnets acted as an eddy-current damper and could be represented by a Maxwell material. In addition, the seismic response of a magnetically levitated foundation was discussed based on a shaking table test and structural analysis.

In the 2018 Hokkaido Eastern Iburi Earthquake (M6.7), seismic intensities as high as 7 and 6+ were observed at KiK-net Oiwake (IBUH01) and K-NET Oiwake (HKD127) stations, respectively. The large acceleration records observed at both stations are discussed based on constant-pressure cyclic direct box shear tests, microscopic studies, and elastoplastic theory. As a result, it was shown that at both observation points, downward vertical acceleration (volume expansion) occurred due to the dilatancy of the surface ground, and that the generated vertical and horizontal motions were strongly coupled.

The static compaction sand pile method is a method of increasing liquefaction resistance by compaction of the ground through low-noise and low-vibration construction of sand piles using static press-in force. In this paper, based on a case study of a pile-foundation building, the effect of the narrowing of the improvement area on the stresses generated by the piles was investigated by static incremental analysis using a beamspring model that takes into account the linearity of the soil and pile materials. The analysis was performed by using the earthquake response analysis to obtain the ground displacement and horizontal pile head force, and then using the shape and pile layout of the building, and the location of the area to be improved by the static compaction sand pile method as the parameters. The results of these analyses showed that when the area of improvement is reduced, the horizontal forces are concentrated in the improved area, which requires attention to secure the shear strength of the pile, but the influence on the bending moment is relatively small, and the load on the unimproved area tends to be reduced compared to that of the fully improved area.

In recent years, a quantitative evaluation method for anti-catastrophe has been proposed. However, this is still conceptual, and it cannot be said that an evaluation method based on design practices has been established. Therefore, we focus on redundancy, which is one of the performance items of anti-catastrophe, and propose a quantitative evaluation method of redundancy for railway bridges and viaducts. In this method, the structural redundancy is evaluated from both perspectives, “margin” that means the displacement leading to collapse against the design limit displacement and “parallelism” that means the change in the margin when the column member disappears. And the structural analysis confirmed the validity of the method. By using this method, it becomes possible to explicitly consider redundancy in seismic design, diagnosis, reinforcement, etc.

Furumura et al. (2023) trained convolutional neural network (CNN) models to automatically digitize the waveforms scanned from the Japan Meteorological Agency (JMA) analog strong-motion seismographs recorded on smoked paper. We validated the CNN models using automatically digitized seismograms of the 1940 Kamui-Misaki-Oki earthquake by applying CNN models to the scanned images of the seismograms that were not used for CNN training. We compared the resulting data with manually traced data. The automatically digitized data agreed well with the manually traced data in most cases, although some data required correction. Using the CNN models substantially reduced the effort required to digitize analog records.

In this study, the deep subsurface S-wave velocity (Vs) structure was estimated using microtremor array data on the Amami and Tokara Islands, and Yakushima Island in Japan. The depth of the top of the seismic basement was about 2.0 km, with the largest depth located at Kikaishima Island in the Amami Island Group; the depths at the other islands ranged from 0.2 to 0.6 km. The mean Vs values for each layer and the layer boundary depths at each location under a four-layer model were 0.69, 1.02, 2.18, and 3.49 km/s. These mean values were close to those of the Yaeyama, Miyako, and Okinawa Islands. These results can be used to improve the structural Vs model of the Nansei Islands.