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

Abstract EN

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.