Prediction of air voids of asphalt layers by intelligent algorithm - Zhao, Yulong; Zhang, Ke; Zhang, Yao; Luo, Yaofei; Wang, Shaoquan (Construction & building materials, 2022)
•New methods for the air voids of asphalt layers were developed.•The applicability of 7 prediction methods was evaluated to estimate the air voids.•The SVR algorithm was more suitable compared to other intelligent algorithms.•The proposed method by SVR algorithm can estimate the air voids in real time. The objective of the study was to give a suitable prediction method of air voids of asphalt layers in the process of construction. Seven different methods are utilized to predict the air voids of asphalt layers: nonlinear data fitting, Back Propagation neural network (BPNN) algorithm, Radial Basis Function neural network (RBFNN) algorithm, support vector machine for regression (SVR), Gaussian process regression (GPR), regression trees, and random forest regression. The results of laboratory experiments and field tests showed that the intelligent algorithm of SVR is more accurate and suitable for estimating the air voids of asphalt layers. The compaction quality of asphalt layers can be evaluated by this proposed new prediction method of air voids.