Journal of Changjiang River Scientific Research Institute ›› 2019, Vol. 36 ›› Issue (12): 144-150.DOI: 10.11988/ckyyb.20180085

• HYDRAULIC STRUCTURE AND MATERIAL • Previous Articles     Next Articles

Influence Factors of Thermal Conductivity of Concrete under Different Temperature Conditions

CAO Guo-ju, GONG Jing-wei, MA Li, ZHANG Ying, ZHANG Meng-li, ZHAO Yu-qi, LIU Xiang-jin   

  1. College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
  • Received:2018-01-23 Published:2019-12-01 Online:2019-12-01

Abstract: The thermal conductivity of concrete was measured by QTM-500 thermal conductivity instrument at different temperatures (-30 ℃-20 ℃) with volume fraction of aggregate, sand ratio, water-binder ratio, fly ash content and slag content as variables. The prediction equation between thermal conductivity of concrete and the aforementioned factors was obtained by analyzing the changes of thermal conductivity affected by these factors at different temperatures. Results revealed that thermal conductivity of concrete was negatively correlated with temperature and sand ratio, while positively correlated with aggregate’s volume fraction; at dry state, thermal conductivity decreased with the increase of water-binder ratio, while at saturated state, thermal conductivity was greater than that under dry condition, and with the decline of temperature, especially at 0 ℃ -10 ℃, thermal conductivity increased dramatically. The thermal conductivity of concrete also reduced with the rise of fly ash and slag dosages. In addition, through multivariate regression analysis of the test results, a calculation model of high prediction accuracy between thermal conductivity of concrete and these factors was obtained. The research findings provide a more important theoretical basis for the accurate calculation of the temperature field in the concrete structure, the thermal insulation performance and the control of surface cracks.

Key words: concrete, temperature, thermal conductivity, influencing factors, multiple regression

CLC Number: