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To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

This paper aims to deal with the development of a software tool to simulate and study vehicle – road interaction (VRI) to quantify the forces induced and energy released from vehicles to the road pavement, in different vehicle motion scenarios, and the energy absorbed by the road surface, speed reducers or a specific energy harvester surface or device. The software tool also enables users to quantify the energetic efficiency of the process.

Existing software tools were analysed and its limitations were identified in terms of performing energetic analysis on the interaction between the vehicle and the road pavement elements, such as speed reducers or energy harvest devices. The software tool presented in this paper intends to overcome those limitations and precisely quantify the energy transfer.

Different vehicle models and VRI models were evaluated, allowing to conclude about each model precision: bicycle car model has a 60 per cent higher precision when compared with quarter-car model, and contact patch analysis model has a 67 per cent higher precision than single force analysis model. Also, a technical study was performed for different equipment surface shapes and displacements, concluding that these variables have a great influence on the energy released by the vehicle and on the energy harvested by the equipment surface.

The developed software tool allows to study VRI with a higher precision than existing tools, especially when energetic analyses are performed and when speed reduction or energy harvesting devices are applied on the pavement.

A single system to replace the four existing methods has been proposed for world‐wide use; if adopted by the Council, all users will benefit—airport and aircraft operators and aircraft manufacturers …

Highways are one of the most critical infrastructure projects with strategic impact on the countries’ development. Asphalt has been historically the main pavement material used in all highway projects, especially in Egypt. However, with the booming in concrete technology in the past two decades, concrete has become a strong rival to asphalt, especially in highway applications. Several factors impact the decision-making criteria for any highway, which differ according to the priorities and requirements of each decision-maker and the nature of the project.

This research focuses on studying and analyzing the different factors that impact the decision for selecting the material type for highways in Egypt’s pavement construction industry. The outputs of the analysis are then incorporated into a multi-decision-making tool to assess the optimum solution as per the priorities of the decision-maker. A holistic framework is developed to compare asphalt and concrete pavements solutions considering; initial cost, maintenance cost on the life cycle, construction duration, embodied energy and fuel consumption. The data collection on local highways was performed through interviewing and surveying experts in the consulting, contracting and building materials fields (total of 15 respondents).

A multi-decision-making tool developed using the superiority and inferiority ranking method for selecting the best alternate. To illustrate the practicality of the proposed framework, a case study for assessment and validation has been done on Sokhna–Quarries highway in Egypt. The framework results reveal that despite a lower initial cost and faster construction of asphalt, concrete pavement is more cost-efficient on the lifecycle time. The multi-decision-making model indicates that concrete is a better alternate for highway applications given the cost, time and energy factors considered.

The proposed model takes into consideration the important parameters in selecting the type of pavement to be constructed considering two alternates asphalt and concrete.

This study aims to investigate the influence of surface texture distribution in respect to the procedure of pavement surface wear on friction performance.

The Weierstrass–Mandelbrot (W-M) equation is used to appropriate pavement surface profile. Through this approximation, artificial rough profiles by combining fractal parameters and conventional statistical parameters for different macro-texture are created to simulate the procedure of pavement surface wear. Those artificial profiles are then imported into discrete element model to calculate the interaction forces and friction coefficient between rolling tire and road. Furthermore, wavelet theory is used to decompose the profiles into different scales and explore the correlation between the profiles of each scale and pavement friction.

The influence of tire vertical displacement (TVD) on friction coefficient is greater than fractal dimension of road surface texture. When TVD decreases, the profiles can provide higher friction, but the rolling stability of tire is poor. The optimal fractal dimension of road surface is about 1.5 when considering friction performance. The pavement friction performance improves with wavelength from 0.4 to 6.4mm and decreases with wavelength from 12.8 to 51.2mm.

Artificial fractal curves are generated and analyzed by combining W-M function with traditional parameter, which can also be used to analyze the influence of texture distribution on other pavement performance. The preliminary research provides a potential approach for the evaluation of pavement friction performance.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0499/

The time-varying equivalent linear temperature gradient (ELTG) significantly affects the development of faulting and must therefore be accounted for in pavement design. The same is true for faulting of bonded concrete overlays of asphalt (BCOA) with slabs larger than 3 x 3 m. However, the evaluation of ELTG in Mechanistic-Empirical (ME) BCOA design is highly time-consuming. The use of an effective ELTG (EELTG) is an efficient alternative to calculating ELTG. In this study, a model to quickly evaluate EELTG was developed for faulting in BCOA for panels 3 m or longer in size, whose faulting is sensitive to ELTG.

A database of EELTG responses was generated for 144 BCOAs at 169 locations throughout the continental United States, which was used to develop a series of prediction models. Three methods were evaluated: multiple linear regression (MLR), artificial neural networks (ANNs), and multi-gene genetic programming (MGGP). The performance of each method was compared, considering both accuracy and model complexity.

It was shown that ANNs display the highest accuracy, with an R² of 0.90 on the validation dataset. MLR and MGGP models achieved R² of 0.73 and 0.71, respectively. However, these models consisted of far fewer free parameters as compared to the ANNs. The model comparison performed in this study highlights the need for researchers to consider the complexity of models so that their direct implementation is feasible.

This research produced a rapid EELTG prediction model for BCOAs that can be incorporated into the existing faulting model framework.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

To explore the economical and reasonable semi-rigid permeable base layer ratio, solve the problems caused by rainwater washing over the pavement base layer on the slope, improve its drainage function, improve the water stability and service life of the roadbed pavement and promote the application of semi-rigid permeable base layer materials in the construction of asphalt pavement in cold regions.

In this study, three semi-rigid base course materials were designed, the mechanical strength and drainage properties were tested and the effect and correlation of air voids on their performance indexes were analyzed.

It was found that increasing the cement content increased the strength but reduced the air voids and water permeability coefficient. The permeability performance of the sandless material was superior to the dense; the performance of the two sandless materials was basically the same when the cement content was 7%. Overall, the skeleton void (sand-containing) type gradation between the sandless and dense types is more suitable as permeable semi-rigid base material; its gradation is relatively continuous, with cement content? 4.5%, strength? 1.5 MPa, water permeability coefficient? 0.8 cm/s and voids of 18–20%.

The study of permeable semi-rigid base material with large air voids could help to solve the problems of water damage and freeze-thaw damage of the base layer of asphalt pavements in cold regions and ensure the comfort and durability of asphalt pavements while having good economic and social benefits.