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论文类型：期刊论文

发表时间：2019-10-15

发表刊物：KNOWLEDGE-BASED SYSTEMS

收录刊物：SCIE、EI

卷号：182

ISSN号：0950-7051

关键字：Data clustering; Attribute correlation; TBM; Operation data

摘要：Tunnel boring machine (TBM) is a complex engineering system used for tunnel construction, and its design is mainly based on knowledge from previous projects. With the development of measurement techniques, massive operation data have been recorded and partitioning these data can provide useful references to the designers and help in the design of TBM. In this paper, a fuzzy c-means algorithm guided by attribute correlations, named attribute correlation-guided fuzzy c-means algorithm (ACFCM), is proposed to accomplish this work. The proposed algorithm is based on fuzzy c-means algorithm (FCM) and involves a new objective function in which the attribute correlation is described by the linear model. A synthetic dataset is used to evaluate the performance of the ACFCM algorithm, which demonstrates its higher effectiveness and advantages compared with conventional FCM. The ACFCM algorithm is applied to cluster the TBM operation data from a tunnel in China, and the load and penetration rate of the TBM are predicted based on the clustering results. The results indicate that the ACFCM algorithm can not only provide competitive clustering results but also significantly increase prediction accuracy. This work also addresses the applicability and potential of data clustering in the design and analysis of other complex engineering systems similar to TBMs. (C) 2019 Elsevier B.V. All rights reserved.