Asphalt pavement segregation, characterized by non-uniform aggregate distribution, detrimentally impacts pavement performance and service life. Conventional two-dimensional (2D) image-based approaches are limited in quantifying three-dimensional (3D) texture geometry and multi-scale features, with environmental factors introducing evaluation uncertainties. This study proposes a framework to quantify segregation by decomposing it into two morphological types: surface aggregate clustering (assessed via 2D-entropy coefficient of variation for planar uniformity) and vertical stratification (evaluated using coarse-to-fine aggregate ratio derived from multi-scale relative energy for gradation deviation). By leveraging 3D laser scanning and 2D wavelet decomposition across nine scales, this research investigates five asphalt mixtures (AC-13, AC-16, AC-25, OGFC-13, SMA-13) under varying temperatures (95–165 °C) and compaction passes (12–24). Results reveal that temperature influences fine aggregate surface uniformity in dense-graded mixtures, while compaction enhances uniformity in SMA-13. Vertical segregation in dense-graded mixtures decreases with elevated temperatures, unaffected by compaction intensity. The study provides new perspectives and methods for assessing asphalt pavement segregation in pavement construction quality control scenarios.
The roughness of asphalt pavements directly affects ride comfort and structural performance, making its long-term prediction essential for pavement management. This study investigates the evolution of international roughness index (IRI) across seven typical asphalt pavement structures using full-scale circular track data from RIOHTRACK. Statistical analysis reveals three characteristic deterioration patterns: stable (semi-rigid and rigid with thin pavements), cyclical fluctuation (thick and full-depth asphalt pavements), and nonlinear growth (semi-rigid base with 4 cm, 6 cm, 8 cm surfaces and inverted-base structures). Pavements with a strong-base–thin-asphalt-surface or medium-thickness configuration exhibit the most stable roughness development, whereas nonlinear growth pavements show accelerated deterioration beyond 40–50 million axle loads. Correlation analysis identifies cumulative axle load as the dominant factor in IRI progression, with temperature exerting limited or structure-specific effects. To capture these nonlinear and coupled influences, a hybrid BO-CNN-LSTM model was developed, integrating Bayesian optimization for hyperparameter tuning. The model achieved superior accuracy (root mean squared error: 0.011–0.029, mean absolute percentage error: 0.6%–1.8%) compared with traditional regression approaches, particularly for semi-rigid and thick asphalt concrete pavements. These findings provide both mechanistic insights and a data-driven tool to support proactive pavement maintenance and long-term performance optimization.
The dynamic interactions between aircraft and runway structures under high taxiing speeds and heavy axle loads present critical challenges in airport engineering. Traditional static design methods fail to adequately address these interactions, potentially leading to premature runway structural failures. The introduction of wide-body aircraft, such as the B787, further exacerbates these challenges. This study develops a three-dimensional coupled dynamic model of aircraft-runway systems, focusing on typical narrow-body (B737) and wide-body (B787) aircraft, as well as a dual-layer slab runway structure. Measured runway roughness is incorporated as excitation, and a novel fast explicit numerical integration method is employed to solve the system’s dynamic response. The research then comprehensively analyzes two key runway design indicators: the aircraft dynamic load coefficient (ADLC) and the pavement structural dynamic strain (PSDS). The results demonstrate significant differences in ADLC and PSDS characteristics between the two aircraft types, attributable to variations in landing gear configurations and aerodynamic layouts. The advanced design of the B787 reduces sensitivity to runway roughness, enhances resistance to roughness excitation, and lowers the critical speed. However, the use of bogie landing gear introduces variations in ADLC among individual wheels within the same landing gear assembly—a factor that, if overlooked, may lead to underestimation of actual dynamic loads. The bogie landing gear promotes more uniform load distribution, minimizing PSDS concentrations at any single pavement location even under higher weights. These findings underscore the necessity of accounting for aircraft dynamic loads in runway design. The study provides a theoretical foundation for optimizing runway structures to enhance durability and safety under diverse dynamic loading conditions.
