FLUCTUATION FACTORS IN DYNAMIC CHARACTERISTICS OF A SIX-STORY WOODEN FRAMING BUILDING

Abstract

 The Building Research Institute and the Japan 2x4 Home Builders Association jointly built a full-scale six-story wooden experimental building. The building was densely equipped with two types of accelerometers to investigate its dynamic characteristics in detail.

 The experimental building has a wooden framing structure with a building area of 38.95 m², a total floor area of 206.09 m², and an eaves height of 16.91 m. The building is supported by eleven steel piles, each being 12 m in length. The floor plan and cross section of the building are illustrated in Fig. 1. Strong motion observation for this experimental building started in April 2016 using two types of instruments. One is a high-performance type equipped with a tri-axial force-balanced accelerometer. The other is an economical type equipped with a tri-axial MEMS (Micro Electronic Mechanical Systems) accelerometer. The sensor configuration is shown in Fig. 1. More than 100 strong motion data were recorded until December 2018. Event parameters and peak accelerations on the ground (GL), first floor (1F) and sixth floor (6F) of the major earthquakes are listed in Table 1.

 From all strong motion data, the fundamental natural frequencies and damping ratios in two horizontal directions of the building were identified using a parameter optimization technique¹⁶⁾. With a single-degree-of-freedom system, the natural frequency and damping ratio that had the most fitted response acceleration were determined using the grid search. Figure 2 indicates changes in natural frequency and damping ratio of the experimental building with time. The natural frequencies and damping ratios widely varied. There are three factors that affect dynamic characteristics of the building. Water pools, that were placed on each floor to substitute for the weight of finishing and live loads during the period from December 2016 to October 2017, had the effect of decreasing the natural frequencies and increasing the damping ratios of the building. Such effect was examined by the numerical analysis as shown in Fig. 3.

 The upper and middle plots in Fig. 4 indicate natural frequencies and damping ratios identified using ambient vibration data. The seasonal fluctuation could be recognized in the natural frequencies. It seems that the fluctuation has correlation with the temperature and relative humidity indicated in the lower plot in Fig. 4. The influence of the temperature and relative humidity can be explained by the regression formula, Equations (5) and (6). To remove the influence of the temperature and relative humidity, the daily natural frequencies are converted to the values corresponding to the temperature of 15°C and relative humidity of 75% using the regression results. The converted natural frequencies are plotted in Fig. 5. The seasonal fluctuations of the natural frequencies were almost eliminated and the changes in the natural frequencies during the period with the additional loading were clarified.

 The identified natural frequencies from the strong motion data were converted to the values corresponding to the temperature of 15°C and relative humidity of 75% as well and the relationships of the converted natural frequencies and damping factors to the maximum displacement angles defined by Equation (7) are plotted in Fig. 6. Looking at Fig. 6, the dependence of the dynamic characteristics on response amplitude could be clearly observed in the analytical result considering the effect of the sloshing, temperature and relative humidity. The natural frequencies decrease with increase of the response amplitude. In contrast, the damping ratios increase as the response amplitudes increase.