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

Zheng Zuo, Yu Hu, Qingbin Li and Liyuan Zhang

Mathematical Problems in Engineering, 2014, vol. 2014, 1-15

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:985659

DOI: 10.1155/2014/985659

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