MS12.2 - Human-Induced Vibrations in Floors, Staircases and Stadia

In the past massive earthquakes, many people suffered severe damage. Among them, not a few people were injured as a result of evacuation actions during the shaking. Therefore, it is important to estimate the human response to shaking and the possibility of evacuation behav-ior in order to evaluate human injury during an earthquake. Considering the background men-tioned above, this study proposes the evaluation method of fall and walking difficulty in earthquake considering behavior of human by constructing the seismic response analysis model of human body. First, the shaking table tests with human subject were conducted to observe the behavior of a human during strong motion. Then the human body model considering walking and falling was de-veloped based on a cart-type double inverted pendulum with feedback control system. In order to set appropriate feedback gains of the control system, the displacement and velocity of head of the human body model was compared to that of the human subject in the shaking table test. Then, the behavior and mechanism of falling of the human due to walking during shaking was investigated by inputting the strong motion record, which observed during 1995 Hyogo-ken Nanbu earthquake, to the human body model. Next, sinusoidal waves of various amplitudes and periods were input to the human body model to clarify the relationship between the vibration characteristics of the input wave and the response of the human body. The period of the sinusoidal wave was varied from 0.26 to 3.3 seconds. The peak floor velocity ranged from 0.5 m/s to 2.0 m/s. The high limit of the peak floor acceleration was 20 m/s2, and the peak floor displacement was 0.5 m. The human body model was made to walk 25 m while exposed to shaking, and its behavior was analyzed. As a result, the human body model fell when the period of the sinusoidal wave was 0.5 to 1 second and the peak response velocity was 1.5 m/s. When the period of the input wave was 0.45 seconds or less, the human model walked without much disturbance. On the other hand, in the periodic band of 1 second or longer, the human model tended to be more disturbed with longer periods, even when the maximum velocity was small. Finally, the validity of evaluation method for evacuation possibility during an earthquake is in-vestigated by comparing the behavior of the human body excited by sinusoidal wave with that excited by an earthquake shaking.

There were many cases of people falling and being seriously injured in the past earthquakes. It is feared that many human casualties will occur in the earthquake that is expected to occur in the near future. In order to reduce human damage, it is important to quickly ascertain whether or not there are injured people. In this study, we propose a method to automatically detect human falls during an earthquake. Furthermore, in order to verify the validity of the proposed method, we performed an indoor earthquake simulation using a physics engine. By applying the proposed method to videos generated by simulation, the possibility of detecting falls of the human body during earthquake was investigated. In order to detect the fall of human, it is assumed that the situation in the room is being filmed by a video camera. The behavior of the human body in the video was detected by a neural network for human skeleton detection using the deep learning framework. The camera is considered as a point light source, and human shadow is projected onto the floor. The shadow length of the human body, which is the distance between the point where the straight line connecting the camera and the human head intersects with the floor and the human foot, is calculated based on the coordinates of human skeleton evaluated by the neural network. If the camera is far enough away from the human body, the shadow length of the human body will be longer than the tall of the human body. When the human falls, the shadow length of the human body matches the height of the human. From these facts, we consider the camera as a point light source, compare the shadow length and the tall of the human body, and if the two match, we can determine that the human has fallen. The indoor model was constructed using the physics engine. Furniture models such as bookshelves and cupboards were placed along the walls of the room, and a human body model was placed in the center of the room. Then the strong motion record observed during the 1995 Hyogo-ken Nanbu earthquake occurred in Japan was input to the floor. When the human body shook with the shaking of the earthquake, the shadow length of the human also changed. When the human fell, the length of the shadow suddenly shortened, and the shadow length became almost equal to the tall of the human body. This means that the human had fallen. It was shown that the method proposed in this study can automatically determine human falls and injuries during an earthquake.

Rhythmic jumping is the largest human-induced load that occurs on assembly structures. It involves a repeated alternation between contact and flight phases at a given frequency. Human loading on the structure takes place during the contact phase while the load is zero during the flight phase. When a structure vibrates, the displacement profile of the structure interacts with human movement during the contact phase. This structure-to-human interaction could result in an alteration in the jump height and the contact duration, and thus the peak ground reaction force (GRF) attained. This paper presents an experimental study that investigates the influence of jump timing relative to the platform position in its vibration cycle on rhythmic jumping. The vertical harmonic vibrations of the platform had a magnitude of 2.0 m/s2 and a frequency of 2.8 Hz. Data were collected from a test subject who was instructed to jump at a frequency of 2.8 Hz in such a way as to land at four different instances of the vibration cycle. These were landing on the platform while at its (i) reference position and on the way down (mid-down), (ii) lowest position (trough), (iii) reference position and on the way up (mid-up), and (iv) highest position (peak) in the vibrating cycle. The timings were presented to the test subject through a metronome beat. The peak toe clearance, impact ratio, contact ratio and frequency of jumping were compared on a cycle-by-cycle basis between the four cases of target landing timing as well as on a non-vibrating platform. The impact ratio and the peak toe clearance were positively correlated with each other and negatively correlated with the contact ratio. Furthermore, the jump height and consequently the peak GRF were higher, and the contact ratio was lower for landing at the trough and the mid-up positions, compared to those on the non-vibrating platform. This is the result of the platform helping the test subject to perform jumps of greater impact in case of landing near the trough and the mid-up positions of the platform.