Pavement performance prediction is necessary for road maintenance and repair (M&R) management and plans. The accuracy of performance prediction affects the allocation of maintenance funds. The international roughness index (IRI) is essential for evaluating pavement performance. In this study, using the road pavement data of LTPP (Long-Term Pavement Performance), we screened the feature parameters used for IRI prediction using the mean decrease impurity (MDI) based on random forest (RF). The effectiveness of this feature selection method was proven suitable. The prediction accuracies of four promising prediction models were compared, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). The two integrated learning algorithms, GBDT and XGBoost, performed well in prediction. GBDT performs best with the lowest root mean square error (RMSE) of 0.096 and the lowest mean absolute error (MAE) of 6.2% and the coefficient of determination (R²) reaching 0.974. However, the prediction accuracy varies in numerical intervals, with some deviations. The stacking fusion model with a powerful generalization capability is proposed to build a new prediction model using GBDT and XGBoost as the base learners and bagging as the meta-learners. The R², RMSE, and MAE of the stacking fusion model are 0.996, 0.040, and 1.3%, which further improves the prediction accuracy and verifies the superiority of this fusion model in pavement performance prediction. Besides, the prediction accuracy is generally consistent across different numerical intervals.

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics for lab and field pavement texture evaluation are lacking. TPIN prediction based on pavement structural and material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens and road pavement to conduct correlation and clustering analysis based on representative 3D texture metrics. We conducted an influence analysis to exclude macroscope pavement detection metrics and macro deformation metrics’ effects (international roughness index, IRI, and mean profile depth, MPD). The cluster analysis results verified the feasibility of texture metrics for evaluating lab and field pavement wear, differentiating the wear states. TPIN prediction accuracy based on texture indicators was high (R² = 0.9958), implying that it is feasible to predict the TPIN level using 3D texture metrics. The effects of pavement texture changes on TPIN can be simulated by laboratory wear.

The increases in the timeliness of maintenance and repair requirements for urban road pavements have created a demand for the timely detection of pavement distress. An effective detection method of multiple types of pavement distress based on low-cost front-view video data was proposed. We constructed a target detection model YOLOv7-RDD modified You Only Look Once version 7 for Road Detection by modifying the YOLOv7 framework to reduce the complexity of the model while improving the detection precision of multiple types of pavement distress. The lightweight efficient aggregation network structure using distributed shift convolution (EDC), the improved spatial feature pyramid structure (SPPCSPD) proposed, and the similarity-based attention mechanism (SimAM) were integrated into our model. We created a dataset called CQURDD containing 12 pavement distress types for urban roads. The SPPCSPD structure has been proven to reduce the running time by nearly 54% compared to the spatial pyramid pooling structure in YOLOv7 while losing little precision. The introduced SimAM module and SIoU (Smooth Intersection Over Union) loss function have improved the model precision, and the Non-Maximum Suppression (NMS) can help reduce redundant detection boxes. A comparative study was carried out based on the CQURDD dataset, YOLOv7-RDD achieved the best precision and efficiency with mAP@0.5 reaching 89.5% and FPS reaching 145. Except for linear cracks, the AP values for the other types of distress detection are all greater than 85%. Moreover, the excellent performance on deformation type distresses of our method forms a relative advantage over professional top-view detection.

Established unmanned aerial vehicle (UAV) highway distress detection (HDD) faces the dual challenges of accuracy and efficiency, this paper conducted a comparative study on the application of the YOLO (You Only Look Once) series of algorithms in UAV-based HDD to provide a reference for the selection of models. YOLOv5-l and v9-c achieved the highest detection accuracy, with YOLOv5-l performing well in mean and classification detection precision and recall, while YOLOv9-c showed poor performance in these aspects. In terms of detection efficiency, YOLOv10-n, v7-t, and v11-n achieved the highest levels, while YOLOv5-n, v8-n, and v10-n had the smallest model sizes. Notably, YOLOv11-n was the best-performing model in terms of combined detection efficiency, model size, and computational complexity, making it a promising candidate for embedded real-time HDD. YOLOv5-s and v11-s were found to balance detection accuracy and model lightweightness, although their efficiency was only average. When comparing t/n and l/c versions, the changes in the backbone network of YOLOv9 had the greatest impact on detection accuracy, followed by the network depth_multiple and width_multiple of YOLOv5. The relative compression degrees of YOLOv5-n and YOLOv8-n were the highest, and v9-t achieved the greatest efficiency improvement in UAV HDD, followed by YOLOv10-n and v11-n.