## 4 Conclusions  
In this study, the shake table test was performed to verify the conversion methods that were employed to convert acceleration data measured in a high-rise building into velocity and displacement data. Then, the displacement obtained by high-speed imaging during the shaking table test was  
Figure 21: Comparison of measured displacement with displacements converted from acceleration data: (**a**) Manjil earthquake; (**b**) Superstition Hills earthquake; (**c**) Kobe earthquake. Figure 20: Analysis process for the 10 th-story acceleration during the Kobe earthquake using Method 3: (**a**) 2 nd-order polynomial equation for correcting acceleration; (**b**) 4 th-order Butterworth high-pass filter; (**c**) acceleration; (**d**) velocity; (**e**) displacement. compared with the displacement obtained by processing acceleration data. This study applied three methods for the correction and conversion of acceleration data into velocity and displacement data. The resulting characteristics and applicability of the conversion methods for evaluating the diagnosis of high-rise buildings were derived as follows. Method 1 used the transfer function \(H_{2}(\omega)\), which reflects the dynamic characteristics of the system. Displacements converted using this method showed the lowest accuracy, because the transfer function depends on the dynamic characteristics of the structure. Method 1 did not use the zero padding and baseline correction methods. Therefore, even if discrete Fourier transformation was employed, there were a lot of differences between the analyzed and measured displacement values occurring based on a non-fixed starting point and the integration of acceleration. Method 2 used the cosine Fourier series transform and baseline correction. Discrete input data was calculated as the sum of the cosine functions. Method 2 used the zero padding and baseline correction techniques in order to correct the initial value of the displacement. In this way, the accumulated displacement errors were corrected. Furthermore, even though the cosine correction filter functions in the time domain, the displacements were very close to the experimental values. Method 3 used the least-squares fitting in the first step in order to remove the linear drift in the acceleration, and applied a high-pass Butterworth filter. Method 3 did not used a zero padding; however, least-square fitting was used as the first filter. After that, the corrected acceleration data was passed through a Butterworth filter. The first filtered data was used to make the second filter efficient. Therefore, the analyzed data obtained using Method 3 are also very close to the experimental data. For this study, there were a number of reasons for which a scale model was implemented: (1) The researchers had difficulty obtaining real data for a high-rise building; and (2) if the scale building model was to be used, the conditions and data related to strong-motion would be simplified. However, if the AVD methods for this study arweree applied to a real high-rise building, the accuracy would decrease. Furthermore, the techniques adopted for these three methods could be separated and combined with each other. Based on Methods 1-3, the application and importance of each method is known. Based on this research, the analytical method for converting acceleration into displacement can be summarized by the following three steps:  
1. First step: A technique is required to establish a fixed point, so that the integration of acceleration data will not be subject to accumulated errors. 2. Second step: Methods for eliminating baseline drift are also taken into consideration, including baseline correction filters and least-square fitting. These could be referred to as a first filter. 3. Third step: After these 2 steps, the noise contained in the real data needs to be eliminated. This step is the most important step.