由於隧道工程的進行大多在地底下或山嶽間，施工階段可能遭遇之困難與風險比建築或其他土木工程高。國內岩石隧道目前大多採用新奧工法(NATM)開挖。為了驗證設計階段地質調查結果的正確性，在施工期間，由地質工程師於每一個開挖面鑽炸、出渣之後，以目視手繪方式，完成地質紀錄、岩體評分表以及地質展開圖等作業。這樣的處理方式，除了易受人為主觀判斷影響外，目前大多為長隧道，隨著工程不斷的進行，對於資料的紀錄、保存以及展現等，都將面臨相當程度之挑戰。
為了改善施工階段傳統的地質紀錄、地質研判等方式。本研究擬以數位影像處理技術為理論基礎，有效擷取隧道影像上的重要地質特徵。接著，進一步探討如何利用影像辨識技術，將重要的地質弱面予以判別。從影像處理的觀點，影像辨識的過程主要包括三個步驟: (1)影像前處理(低級處理);(2)影像分析(中級處理)以及(3)圖形辨識(高級處理)。低級處理與中級處理又可分成空間域以及頻率域。由於隧道開挖面影像比較複雜，本研究試著利用一種具有多解析特性的小波轉換進行影像特徵的擷取。為了強化影像特徵擷取的效果，配合影像後處理(image post-processing)。由測試結果發現，配合影像後處理的確提升了特徵影像分割(segmentation)的效果。
此外，為能正確地辨識隧道開挖面上各種地質弱面，圖形識別為達成本研究最終目的之重要方法。本研究主要是分析常見的隧道地質弱面，如層面(bedding plane)、節理(joint)、斷層(fault)以及褶曲(fold)等，並且找出各種地質弱面間intra與 inter的特性關係，例如幾何形狀，方位關係以及重要地質特徵等。然後，訂出一套完整的辨識分析與判斷準則(criterion)。接著，利用人工模擬邊緣影像(edge image)以及實際隧道開挖面影像進行測試。結果發現，本研究所提的影像自動化分析方式，的確可以分析隧道開挖面的弱面情況，迅速地提供現地工程人員參考之用。

Since tunnel engineering methods are mostly carried out underground and in mountains, the possible difficulties and risks during tunnel construction are higher than other civil engineering construction. Currently, the excavation of domestic rock tunnels is mostly carried out by using the new Austrian tunnel method (NATM). In order to improve the accuracy of the results obtained from the geological survey conducted during the design stage, engineers carry out investigations to ascertain the location and strikes of geological weak planes, such as fault, joint, fractured zone, etc. These investigations are conducted by using visual and hand-drawing techniques during tunnel construction.
In addition to the above mentioned processes will be affected by artificial subjective judgments, the recording, preservation, and exhibition of tunnel data face a considerable challenge since the majority of the tunnels are very long at present. In order to improve the traditional geological recording method and judgment during construction, this dissertation studies on digital image processing technology to analyze some significant information for the tunnel excavation face images
In general, the process of image identification involves three steps mainly: (1) preprocessing of the image (low-level processing); (2) image analysis (intermediate-level processing); and (3) pattern recognition (high-level processing). The low-level and intermediate -level processing can be performed in the spatial and frequency domains. Due to the tunnel excavation face images are more complex, a wavelet-based image analysis is applied in this research. Furthermore, to promote the efficiency of image characteristics extraction, it is coupled with image post-processing, i.e., (1) thinning process; (2) split-branch and break-point process; and (3) local linking process. From the test results, it was found that this coupling with post-processing methods indeed improves the quality of image characteristics extraction.
In order to identify the various geological weak planes on the tunnel excavation face, this research applies intra- and inter-correlations between various geological weak planes, for example, geometric shape, direction, and geology conditions. Next, a complete identification analysis and judging criterion is developed. Further, a set of image tests are conducted, including the artificial simulation edge image and the actual tunnel image.
From the results of these experiments, it was found that through this application, the situation of weak planes that existed on the tunnel excavation face can be correctly identified. As a result, the automated image analysis approach could be used to obtain considerable information of the tunnel excavation face image to assist the engineer made a decision in tunnel engineering.

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