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The Ministry of Transportation of Ontario was interested in a rehabilitation strategy that could be used to address deep-seated rutting issues encountered on its 400-series highways. A precast concrete inlay panel (PCIP) rehabilitation design was developed and constructed involving the installation of precast panels into partially-milled asphalt pavement. Sub-surface instrumentation was installed at the PCIP–asphalt interface including earth pressure cells and moisture sensors installed in six instrumentation clusters. This instrumentation has been monitored to gather information regarding the PCIP trial installation. Readings from the moisture sensors indicate that water penetrates beneath the PCIPs in precipitation events, though these moisture levels recede under dry conditions, indicating that the water can exit the sub-slab area. Static load testing using a fully-loaded gravel truck was used to determine the different support reactions caused by different loading configurations. Higher loads were generally found beneath the joints in the two loading situations studied. [ABSTRACT FROM AUTHOR]

Previous analytical studies have demonstrated that low-density lightweight cellular concrete (LCC) subbase pavements can support up to 20 times more traffic loads than unbound granular subbase pavements while protecting the pavement subgrade from adverse freeze–thaw effects in cold regions. This study examines the possibility of providing better-performing pavements on the field through the construction, instrumentation, and monitoring of flexible pavement sections incorporating three LCC densities (400, 475, and 600 kg/m³) as subbase material compared with unbound granular material in Canada. The effects of daily and seasonal temperatures on pavement critical responses to stress and strains were evaluated. The findings showed that these LCC pavements reduced asphalt concrete tensile strain by over two times compared with unbound granular pavements, and that strain increased with a daily temperature increase. Daily subgrade pressure (stress) change was reduced by up to 68%. The study concluded that longer life pavements could be achieved with LCC subbase thicknesses ≥250 mm. [ABSTRACT FROM AUTHOR]

In North America, highway agencies have started using Superpave as it incorporates a performance-based asphalt binder specification and a mix design analysis system. However, in a pavement management system (PMS), the performance of the pavement structure significantly influences management decisions. In this regard, accurate prediction and evaluation of performance is a very important aspect. With this in mind, this study investigates the performance of Superpave through the mechanistic–empirical (M-E) approach, field-evaluated performance, and laboratory performance tests. It considers 15 sections of highways from Ontario. The investigation found that the international roughness index (IRI) and permanent deformation are overpredicted in the M-E approach compared with field observations. Additionally, to better understand the performance, the dynamic modulus of asphalt mixtures and binder rheological testing are also performed. The master curve developed for the surface mixtures suggests a lower level of fatigue resistance that justifies the bottom-up fatigue failure in the field-observed scenarios. [ABSTRACT FROM AUTHOR]

Pavement friction has been identified as crucial in traffic safety. Since the Highway Safety Manual prediction algorithm is often based on crash frequency, the crash severity distribution might be assumed unchanged before and after the countermeasure. However, pavement surface treatments can improve the friction to different levels, by which crash severity outcomes may vary greatly. To explore the implicit effects of pavement friction on vehicle crash severity, this paper first validates the extreme gradient boosting model performance and then the Shapley additive explanations interaction values are employed to interpret individual features and the nonlinear interactions among predictors. Under various scenarios, the extreme gradient boosting (XGBoost) output probability is utilized to convert into dynamic crash severity distributions. Results also indicate that friction becomes more significant when the friction number is less than 38, and immediate corrective actions are needed when the friction number is below 20. [ABSTRACT FROM AUTHOR]

Pavement crack detection has always been an important problem in pavement maintenance. With rapid development in computer vision technology, crack detection using deep learning has become one of the major tasks in automatic pavement condition detection and assessment. This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different scales (10 × 10, 20 × 20, 30 × 30), and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realizes complete crack detection. The experimental results show that 30 × 30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks. Thresholding smaller grids allows better combinations of the differences between objects and backgrounds, which makes segmentation results more accurate. [ABSTRACT FROM AUTHOR]