台灣主要建築材料為鋼筋混凝土及鋼結構。由於鋼結構經過火害後是無法修復，故而探討火害導致建築物的毀損，因此藉由成功大學與內政部研究所合作的火害計畫，是國內少有大型鋼構屋的火害實驗，利用成功大學歸仁校區所搭建實尺寸鋼構實驗屋進行火害實驗，其中我們所負責火害之動力實驗。
動力實驗使用MK-155U 離心質量激震系統(激震器)使結構物產生搖動，並以加速度計、雷射位移計來量測結構物的加速度與位移，再透過希爾伯特-黃轉換(Hilbert-Huang Transform)來找出結構物自然頻率，來探討結構物受損的動態特性。
整體而言，以本研究結果顯示希爾伯特-黃轉換之瞬時頻率、瞬時振幅跟gf.exe程式進行暫態FFT轉換相較下來得好，運用到本實驗的鋼結構屋得到結果顯示，火害前共振頻率為3.9 Hz，經火害後共振頻率到3.8 Hz、最大位移值變大、最大加速度變小。

The main building materials in Taiwan are reinforced concrete and steel structure. Since the steel structure cannot be repaired after the fire damage, so the mainly research of this paper is discussing the damage of buildings caused by fire damage. Architecture and Building Research Institute (ABRI) and National Cheng Kung University had a fire project; there are few large-scale steel house fire experiments in Taiwan. We use the full-scale steel building to perform an actual dynamic experiment.
In the dynamic experiment, use the MK-155U Eccentric Mass Vibrator System to shake the structure. Measure the acceleration and laser displacement meter to analyze the dynamic behavior of the structure. Therefore, the Hilbert-Huang Transform is used to find out the natural frequency, to explore the dynamic characteristics of the structure.
Overall, the results show that the instantaneous frequency and instantaneous amplitude of Hilbert-Huang Transform is better than that of the Gf.exe program. The resonant frequency of the steel house is 3.9 Hz before the fire damage, and the resonant frequency is 3.8 Hz, maximum displacement value and maximum acceleration become smaller.

參考文獻
1. Huang, N.E., et al., Applications of Hilbert–Huang transform to non‐stationary financial time series analysis. Applied stochastic models in business and industry, 2003. 19(3): p. 245-268.
2. Barnhart, B.L., The Hilbert-Huang transform: theory, applications, development. 2011: The University of Iowa.
3. Huang, N.E., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. in Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. 1998. The Royal Society.
4. Wang, G., et al., On intrinsic mode function. Advances in Adaptive Data Analysis, 2010. 2(03): p. 277-293.
5. Huang, N.E. and Z. Wu, A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics, 2008. 46.
6. Rilling, G., P. Flandrin, and P. Goncalves. On empirical mode decomposition and its algorithms. in IEEE-EURASIP workshop on nonlinear signal and image processing. 2003. NSIP-03, Grado (I).
7. Huang, N.E., Hilbert-Huang transform and its applications. Vol. 16. 2014: World Scientific.
8. Wu, Z. and N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 2009. 1(01): p. 1-41.
9. Yeh, J.-R., J.-S. Shieh, and N.E. Huang, Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Advances in adaptive data analysis, 2010. 2(02): p. 135-156.
10. Torres, M.E., et al. A complete ensemble empirical mode decomposition with adaptive noise. in Acoustics, speech and signal processing (ICASSP), 2011 IEEE international conference on. 2011. IEEE.
11. 陳奕豪, 實尺寸鋼構屋之靜態及動態火害實驗研究. 國立成功大學土木工程研究所，碩士論文, 2017.
12. 朱聖浩, 鍾興陽, and 朱世禹, 實尺寸鋼構屋彎矩連接與剪力連接鋼梁之火害結構行為研究. 內政部建築研究所委託研究報告, 2017.