Porous asphalt mixture can be used as a road surface paving material with the remarkable advantage to prevent water accumulation and ponding. However, the performance of porous asphalt mixture in low temperature environment has not been thoroughly investigated, and this forms the subject of research in the present study. The mineral aggregate gradation of porous asphalt mixture was designed based on Bailey method, and the low temperature performance of porous asphalt mixture was studied by means of the low temperature bending test. The factors affecting the low temperature performance of porous asphalt mixture were analyzed through the orthogonal experimental design method, and the effects of porosity, modifier content, aging condition, and test temperature on the low temperature performance of porous asphalt mixture were evaluated. The results showed that the modifier content was the most important factor affecting the low temperature performance of porous asphalt mixture, followed by the test temperature, while the porosity and the aging condition were the least. Among the three performance evaluation indicators, namely the flexural tensile strength, maximum bending strain, and bending stiffness modulus, the maximum bending strain had the highest sensitivity to the porosity. It can be seen from the single factor influence test of porosity that there existed an approximately linear relationship between the maximum bending strain and the porosity of porous asphalt mixture, and the maximum bending strain decreased with increasing porosity. Furthermore, in order to ensure the good working performance of porous asphalt mixture in low temperature environment, the porosity should also satisfy the required limits of the maximum bending strain.

Steel slag is the main by-product of the steel industry and can be used to produce steel slag fine aggregate (SSFA). SSFA can be used as a fine aggregate in mortar or concrete. However, SSFA contains f-CaO, which is the main reason for the expansion damage of mortar and concrete. In this study, the carbonation treatment of SSFA was adopted to reduce the f-CaO content; the influence of the carbonation time on the content of f-CaO in the SSFA was studied; and the effects of the carbonated SSFA replacement ratio on the expansion rate, mechanical properties and carbonation depth of mortar were investigated through tests. The results showed that as the carbonation time increased, the content of f-CaO in the SSFA gradually decreased. Compared to the mortar specimens with carbonated SSFA, the specimens with uncarbonated SSFA showed faster and more severe damage and a higher expansion rate. When the replacement ratio of carbonated SSFA was less than 45%, the carbonated SSFA had an inhibitory effect on the expansion development of the specimens. The compressive strengths of the specimens with a carbonated SSFA replacement ratio of 60% and 45% were 1.29% and 6.81% higher than those of the specimens with an uncarbonated SSFA replacement ratio of 60% and 45%, respectively. Carbonation treatment could improve the replacement ratio of SSFA while ensuring the compressive strength of specimens. Compared with mortar specimens with uncarbonated SSFA, the anti-carbonation performance of mortar specimens with carbonated SSFA was reduced.

The chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (YN), the replacement ratio of recycled coarse aggregates (r), the bending load level (L), the carbonation time (t) and temperature (T) of RAC, and the replacement ratio of carbonated recycled fine aggregates (f), were considered to conduct a chloride penetration test on RAC. Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. The results showed that the predicted values of all four models were in good agreement with the experimental values, and the XGBoost model had the best prediction performance on the testing set. Based on the XGBoost model, the LIME method was adopted to solve the interpretability problem in the prediction process. The importance ranking of IFs on the electric flux was r > t > f > T > L > YN. A graphical user interface (GUI) was developed based on Python 3.8 software to facilitate the use of machine learning models for the chloride ion permeability resistance of RAC. The research results can provide an accurate prediction of the electric flux of RAC.