Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys? [PDF]
Fiorentini N +3 more
europepmc +1 more source
Correlation between Rheological Fatigue Tests on Bitumen and Various Cracking Tests on Asphalt Mixtures. [PDF]
Ishaq MA, Giustozzi F.
europepmc +1 more source
Prediction of average annual surface temperature for both flexible and rigid pavements
The surface temperature of pavements is a critical attribute during pavement design. Surface temperature must be measured at locations of interest based on time-consuming field tests.
Karthikeyan LOGANATHAN, Mena SOULIMAN
doaj
The accurate prediction of rutting severity levels is vital for the maintenance and safety of flexible pavements, facilitating timely, cost-effective measures to avert further degradation and prolong road life.
Ali Alnaqbi +3 more
doaj +1 more source
Pavement condition prediction under small-sample conditions using a particle swarm optimization-based support vector machine. [PDF]
Xu W, Yang Z, Ji Y, Huang P.
europepmc +1 more source
Robust Pavement Modulus Prediction Using Time-Structured Deep Models and Perturbation-Based Evaluation on FWD Data. [PDF]
Guo X, Chen Y, Sun N.
europepmc +1 more source
LiDAR-Based Road Surface Damage Classification: A Survey. [PDF]
Greene T +5 more
europepmc +1 more source
A comparative evaluation of sustainable asphalt binder modifiers for enhanced performance. [PDF]
Saudy M +4 more
europepmc +1 more source
Statistical and machine learning models for predicting spalling in CRCP. [PDF]
Al-Khateeb GG, Alnaqbi A, Zeiada W.
europepmc +1 more source
Determining Optimal Dosage of High-Modulus Asphalt Binders Through Comprehensive Rheological Assessment Across Full Temperature Range. [PDF]
Wang Y, Ye B, Wang Q, Bai Q, Jiang J.
europepmc +1 more source

