結構物的損傷在工程上一直是個很重視的課題，若能及時的檢測出來，則可以預防許多災難的發生。近年來，小波轉換被廣泛應用於各個方面，如影像壓縮、雜訊去除、邊緣偵測、通訊等，本研究將利用二維離散小波轉換於偵測平板的裂縫位置。而類神經網路可以快速處理大量的資訊，建立輸入與輸出間的相對關係，並且不斷的學習、修正，因此本文將以類神經網路配合二維離散小波轉換來判別裂縫的損傷程度。
　　在數值模擬上，先探討雙裂縫平板的損傷指標趨勢與裂縫位置及長度變化的關係，之後嘗試改變損傷指標的選取範圍，並改以單裂縫平板之損傷指標做為類神經網路的訓練樣本，來判別多裂縫平板上各單一裂縫的破損程度，建立一個可以適用於多裂縫平板的偵測方法。實驗部分，會將實驗量測無裂縫平板與模擬無裂縫平板間的模態差異，來修正含裂縫平板之模態振型，讓實驗更貼近模擬的情況，探討此方法對於損傷程度判別誤差的影響。

Structural damage has been a very important issue in engineering. If damage can be detected in a timely manner, many disasters can be prevented. In recent years, wavelet transform methods have been widely used in various domains, such as image compression, noise removal, edge detection, communication, etc.This study will use the two-dimensional discrete wavelet transform method to detect the position of cracks in plate. Running data through the artificial neural network allows for large computations to be calculated at rapid speeds, and relationships between input and output are quickly achieved. The network will continuously learn and make corrections. The artificial neural network along with the two-dimensional discrete wavelet transform method is used to identify the degree of damage in this study.
During the analytical simulation, several plates with cracks are analyzed and damage indexes are assigned to represent the length and the distance between the two cracks. After which, we narrowed our selection area to a single crack on the board to obtain new damage indexes. These new damage indexes, which is the training sample is then inputted into the artificial neural network. We are then able to take the damage indexes from other plate samples to be analyzed with-in the artificial neural network to obtain the length of the crack. As a result we were able to devise a method for analyzing cracks on a plate efficiently and systematically. Lastly, in order to narrow the gap between the simulations and the actual experiments, we eliminated the mode shapes’ difference between simulations and experiments, with the intention to obtain more precise results from the artificial neural network.

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