Prediction of thermal conductivity ofconcrete under variable temperatures in cold regions using projection pursuit regression

Abstract

A projection pursuit regression (PPR) model was proposed in this paper to predict the thermal conductivity of concrete (TCC) under variable temperatures in cold regions. The PPR model was established based on experimental data obtained from a single–factor experiment. Unlike the conventional PPR model that selected a random form from an empirical distribution function to describe the ridge functions in advance, the PPR model in this paper can directly apply numerical functions to describe the ridge functions obtained by projection. The comparison study indicated that, for the TCC prediction, the PPR model (R = 0.985) outperformed trained model based on Back–propagation (BP) neural network (R = 0.974). The PPR model simulation results demonstrated that the aggregate volume fraction, sand rate, saturation, water–cement ratio, fly ash content, and slag content all had impacts on the TCC under variable temperatures in cold regions. Especially, in temperature-sensitive zone (0 °C to −10 °C), it was found that the TCC of specimens with high water–cement ratio and high saturation degree increase abruptly but other factors did not cause the similar abrupt change of the TCC during the same temperature range. The correlation between the water-cement ratio and temperature–sensitive zone was explained after an in–depth study of the concrete pore structure.