|Geotechnical design using fuzzy sets
|Awarded by Multi-disciplinary Studies, HKPU
Hong Kong Polytechnic University -- Dissertations
|iv, 80 leaves : ill. ; 30 cm
|In Hong Kong, with a combination of hilly terrain and intense development during the past decades, large and steep cuttings have been made into the existing hillside. In many parts of Hong Kong Island where the bedrock is fairly shallow, large and steep rock cuttings have been formed. The granitic or volcanic bedrock in Hong Kong is very strong and the source of instability of these rock slopes arises from daylighting of adverse rock joints. Stability study of rock slopes in Hong Kong is therefore based on the assessment of the presence of predominant adverse rock joints and their effects on the stability of rock blocks along which they slide. For the purpose of rock slope stability study, rock joint data are collected in the field and the data analysed. Conventional methods of analyses involve projection of the rock joint data (i.e. dip angle and dip direction) onto an equal area stereoplot and the data contoured by the counting circle method. The projection of rock joint data on the x-y plane gives clusters of points which could be studied in terms of fuzzy-neural techniques. This dissertation attempts to use fuzzy objective functions of Fuzzy C-Means and Fuzzy Covariance Algorithms to identify the centres of clusters and compares the results with a fuzzy neural network of Fuzzy Kohonen Clustering Nets. Artificial clusters were initially studied to provide a measurement of the appropriateness of the various methods in cluster analyses. Actual rock joint data are then studied to compare with the results of conventional analyses. The results indicate that Fuzzy C-Means fined tuned by Fuzzy Kohonen Clustering Nets is the most appropriate method of cluster analyses.
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