Pavement images have been utilised to detect distresses. However, existing methods for detecting pavement distresses are not acceptable owing to the various real-world conditions. To complete the task, a novel detection method based on faster region convolutional neural network (Faster R-CNN) was utilised to recognise and locate pavement distresses including crack, pothole, oil bleeding and dot surface autonomously. Twenty Faster R-CNNs were trained and tested by 6498 pavement images. Then the performance of training was analysed to select the optimal Faster R-CNN. At last, a test and a comparative study were presented to verify the stability and superiority of the optimal one. In the testing, average results of the accuracy rates, recall rates and location errors in the optimal one were 90.4%, 89.1% and 6.521 pixels, which were close to average results of training and validation. It indicated the optimal Faster R-CNN had good performance to detect distresses in different pavements. Compared with the CNN and K-value method, the optimal Faster R-CNN located pavement distresses with bounding boxes more precisely.

Lignin, as a bio-based waste, has been utilized in the asphalt industry due to various advantages. This study aimed to investigate the effects of two lignin products (lignin powder and lignin fiber) on the mechanical properties of asphalt mixtures. The raveling, rutting, thermal and fatigue cracking resistance, and moisture susceptibility of different asphalt mixtures were respectively evaluated by the Cantabro test, wheel loading tracking test, semicircular bending test, four-point beam bending test, and freezing-thaw cyclic test. Results show that asphalt mixture with lignin powder-modified asphalt improved the overall mechanical performance. However, lignin fiber showed contradictory effects on certain mechanical properties, i.e., improved rutting resistance and thermal cracking resistance of asphalt mixture, degraded abrasion resistance, fatigue performance, and moisture stability. Therefore, cautions need to be taken when incorporating lignin fiber into asphalt mixture.

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.