In this dissertation, a systematic study of image processing including theory, operation, and application has been presented and discussed as it relates to tunnel engineering and construction. In this chapter, conclusions and recommendation will be addressed.
7.1 Conclusions
From a review of the literature, we know that geological logging and rock mass classification are two major tasks to be completed prior to NATM tunnel face excavation.
Most excavation face conditions are presently logged and determined by manual visual investigation. Costly and time-consuming 2D geological maps may then be drawn to show the location and direction of weak planes. Our aim here is to overcome the limitations of these conventional approaches. The primary purpose of this study is to utilize image processing to assist geologists and tunnel project engineers in analyzing and evaluating significant geologic characteristics of tunnel excavation faces.
The contributions of this research can be summarized as follows:
1. A digital image can completely record geologic conditions during tunnel excavation.
Human recording may permit errors or omissions.
2. Owing to the complexities of natural object images like tunnel excavation faces, traditional spatial-domain-based edge detection may not effectively discern tunnel geological features. Adopting wavelet-based image edge detection solves this problem.
Wavelet transform is a superior method because it provides a multi-resolution representation structure to analyze the images. In other words, one can analyze the images at a low resolution and then gradually increase the resolution. This technique is also known as the “coarse-to-fine strategy.”
3. In order to eliminate spurious edge segments, a post-processing algorithm is presented.
Based on the edges features for each geologic week plane, this algorithm contains three approach procedures: thinning process, split-branch and break-point process, and local-linking process. Experimental results show better results with the proposed post-processing than without it.
4. The issue of automatic image recognition is addressed. Based on the intra-inter relationships of common geologic weak planes such as bedding planes, joints, faults and folds, a tunnel image recognition algorithm was created. Experimental results show the proposed algorithm can accurately identify the geologic conditions of a tunnel excavation face.
7.2 Recommendations
Based on the above research, we recommend some directions for future research:
1. A study testing image processing in a variety of real-case scenarios may prove the workability and effectiveness of this approach with different rock formations in tunnel excavation face images.
2. Although wavelet-based image edge detection can effectively improve image extraction by combining the proposed post-processing methods, the image features of such tunnel excavation faces are quite complex in actuality. Therefore, other image segmentation techniques such as texture analysis and edge detection integrative could
be applied. The applications may simplify the post-processing procedures and enhance image analysis efficiency.
3. If complete tunnel excavation images can be collected to provide sufficient image data, this data might then be used in modern neurology, enabling a neural network, fuzzy theory, etc. to compile important recognition factors. When augmented by human experience, the goal of “automatic identification” could be achieved.
4. In order to achieve superior visualization, it is recommended that image processing techniques be expanded from 2D to 3D to predict the orientation of geologic weak planes. In other words, in conjunction with the 3D reconstruction technique, the geologic weak plane of tunnel excavation face is displayed in 3D. Applying a 3D measurement would then quantify the direction of the dip of the weak plane. After attaining quantified weak plane data, insert-type image techniques may be applied to predict the weak plane distribution for the remaining unexcavated tunnel.
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