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Mathematical Problems in Engineering
Volume 2014, Article ID 985659, 15 pages
http://dx.doi.org/10.1155/2014/985659
Research Article

Data Mining of the Thermal Performance of Cool-Pipes in Massive Concrete via In Situ Monitoring

1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2Planning and Design Institute of Water Transportation, Beijing 100007, China

Received 27 December 2013; Revised 30 January 2014; Accepted 4 February 2014; Published 5 May 2014

Academic Editor: Ting-Hua Yi

Copyright © 2014 Zheng Zuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Embedded cool-pipes are very important for massive concrete because their cooling effect can effectively avoid thermal cracks. In this study, a data mining approach to analyzing the thermal performance of cool-pipes via in situ monitoring is proposed. Delicate monitoring program is applied in a high arch dam project that provides a good and mass data source. The factors and relations related to the thermal performance of cool-pipes are obtained in a built theory thermal model. The supporting vector machine (SVM) technology is applied to mine the data. The thermal performances of iron pipes and high-density polyethylene (HDPE) pipes are compared. The data mining result shows that iron pipe has a better heat removal performance when flow rate is lower than 50 L/min. It has revealed that a turning flow rate exists for iron pipe which is 80 L/min. The prediction and classification results obtained from the data mining model agree well with the monitored data, which illustrates the validness of the approach.