This research addresses the critical issue of load transfer efficiency (LTE) in jointed plain concrete pavements (JPCP), with a specific focus on the role of dowel bars in ensuring optimal load transfer and providing a comfortable ride for vehicles. While experimental studies have investigated factors like joint width, slab thickness, concrete strength, and dowel bar size that influence LTE, they are limited in their ability to accurately replicate real-world conditions and can be time-consuming. To overcome these limitations, finite element modelling (FEM) is employed as a powerful tool for simulating complex loading conditions and analyzing stress and strain distributions in pavements. The primary objective of this research is to develop an advanced FE model that incorporates the forklift tire-pavement interaction, enabling precise analysis of complex loading conditions in industrial pavements and the impact of various rigid pavement parameters on load transfer. By explicitly considering the interaction between the tire and pavement, the proposed model will provide an extensive and robust numerical tool for designers and engineers. Additionally, this study represents a novel framework to integrate concrete pavement dowel bars and complex tire modelling using FEM. The developed methodology holds significant promise in optimizing the design of dowel bar systems and back-calculating the pavement parameters for rolling weight deflectometers.

The British Pendulum Test (BPT) is commonly used to assess the skid resistance characteristics of road surfaces. However, the wide range of slider stiffness calibration limits of the BPT tester specified by standards can lead to significant inconsistencies in British Pendulum Numbers (BPNs) generated using different machines. This renders the test impractical for precise evaluation of pavement skid resistance. This study therefore proposes a mechanistic-empirical homogenisation technique which is easy to implement and does not require heavy computational power to resolve the issue. Linear equations between slider force variation and BPN output are developed using a detailed finite element simulation model. The linear relationships proposed in the study can transform BPNs from different BPT machines, with a standard deviation of 10 BPN units, into a reference BPN result, with a standard deviation of less than 1 BPN unit, for most test specimens. The proposed method is found to perform well across specimen gradations by reducing the maximum percentage difference from nearly 25% to less than 5% without requiring computationally intensive simulations. This technique can be used to reduce the variation in BPT measurements, leading to a more precise assessment of low-speed skid resistance properties of roads.

Drainage capacity of pervious pavement mixtures is commonly measured using a falling head permeameter at hydraulic heads much higher than expected in the field. Recent advancements in computational fluid dynamics (CFD)- and X-ray computed tomography (XRCT)-based modeling eliminates the laboratory challenges of maintaining lower hydraulic heads. However, improper characterization in digital image processing (DIP) and finite-volume simulations resulted in significant errors in permeability measurements and fluid flow behavior. In addition, past studies have identified non-Darcy fluid flow characteristics in pervious pavement mixtures following the Izbash and Forchheimer laws. This paper attempts to bridge this research gap by comparing the Darcy and non-Darcy permeability parameters at different laboratory and field hydraulic heads using advanced XRCT-based modeling. It was found from the analyses that the use of laboratory hydraulic head could result in significant underestimation of permeability parameters compared with the field hydraulic heads for Darcy and Izbash equations (by up to 73%), and overestimation for Forchheimer equations (by up to 216%). Fluid flow behavior in pervious mixtures was found to be in transition flow regime (neither laminar nor turbulent) at both laboratory and field hydraulic gradients. Overall, this study can help in a better fundamental understanding of the current limitations of laboratory measurements and the need for XRCT-based numerical modeling to bridge field and laboratory permeabilities of pervious pavement mixtures.

Asphalt pavements are subjected to various environmental factors such as rainfall, sunlight, humidity and wind that causes oxidative aging of bitumen, leading to reduced structural and functional performances in the longer run. Antioxidants are often added to asphalt binders to enhance their resistance to oxidative ageing. In the current study, two different antioxidants, Zinc Diethyldithiocarbamate and Lignin were evaluated for their effectiveness in improving the performance of asphalt binders. The laboratory mixing procedures were conducted at two different percentages, and laboratory aging were performed. Rheological and chemical tests were then conducted to evaluate the performance of the binders at different temperatures. The current study provides valuable insights into the use of antioxidants for improving the performance and service life of asphalt pavements, which will help in the development of perpetual asphalt pavements in the future.

Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, and property damage. Therefore, preserving pipeline integrity is crucial for a safe and sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that can effectively tackle these challenges. This review article mainly focuses on the novelty of using machine and deep learning techniques, specifically artificial neural networks (ANNs), support vector machines (SVMs) and hybrid machine learning (HML) algorithms, for predicting different pipeline failures in the oil and gas industry. In contrast to existing noncomprehensive reviews on pipeline defects, this article explicitly addresses the application of ML techniques, parameters, and data reliability for this purpose. The article surveys research in this specific area, offering a coherent discussion and identifying the motivations and challenges associated with using ML for predicting different types of defects in pipelines. This review also includes a bibliometric analysis of the literature, highlighting common ML techniques, investigated failures, and experimental tests. It also provides in-depth details, summarized in tables, on different failure types, commonly used ML algorithms, and data resources, with critical discussions. Based on a comprehensive review aforementioned, it was found that ML approaches, specifically ANNs and SVMs, can accurately predict oil and gas pipeline failures compared to conventional methods. However, it is highly recommended to combine multiple ML algorithms to enhance accuracy and prediction time further. Comparing ML predictive models based on field, experimental, and simulation data for various pipeline failures can establish reliable and cost-effective monitoring systems for the entire pipeline network. This systematic review is expected to aid in understanding the existing research gaps and provide options for other researchers interested in predicting oil and gas pipeline failures.

Pavement condition monitoring (PCM) systems are essential for making decisions on road maintenance and rehabilitation toward preserving roads and airports assets in a good performance for a longer time. Modern smartphones are equipped with adequate storage, computing and communication properties, besides built-in sensors that show an excellent capability to capture information about users and the environment around us. Therefore, it is worthy to be used for efficient and cost-effective PCM. This review aims to survey the researchers' efforts on the application of smartphones for PCM, mapping the researchers' views from the literature into coherent discussions and highlighting the motivations and challenges of using such technology for pavement defects detection. Based on the existing literature, it was found that the smartphone applications technology is feasible and accurate to some extent as an alternative for conventional technologies for rural, highways and airports PCM. However, this technology is still in the first stage and many factors, calibrations and standards need to be studied and developed in future research in different countries at the various environments and different smartphone features. For example, one of the shortcomings of using smartphone-based sensors technology is the collected data is not directly collected from the pavement surface but is inferred from the data that resulted from the interaction among the vehicle, driver and pavement. This data processing could create limitations on the accuracy of such technology. It is also expected that data generated by sensors will vary according to the smartphone properties, sensor conditions, behavior of drivers, vehicle dynamics and conditions that lead to differences in recorded data. Therefore, such technology still needs further investigations and evaluations, especially in data collection accuracy. This review is expected to help in understanding the existing development, motivations, challenges, research gaps and future directions in the application of smartphones for PCM.

The widespread production and consumption of plastics is a pressing global issue that requires multifaceted approaches and solutions. In terms of recycling, one of the ways to repurpose waste plastics in the construction industry would be to utilize them for asphalt pavement-related applications. Although this approach can potentially provide a value-added recycling outlet for plastics, several challenges need to be resolved to maximize its usage to the highest possible extent. Based on this, the present review article provides a comprehensive background on the different pertinent aspects associated with the use of waste plastics in asphalt mixtures. Besides examining the mechanical performance of asphalt mixtures containing waste plastic, the associated environmental concerns and life cycle assessment related attributes are also thoroughly deliberated. In addition, the successful demonstration of this technology through field trials in several countries is also discussed. Some of the main challenges related to the use of plastics in asphalt mixtures include the variability of plastic properties and composition, which can influence its mechanical performance and associated environmental impact. In general, the incorporation of waste plastics using certain tailored approaches can adequately meet and even enhance the typical performance parameters of asphalt mixtures. However, the effect of plastics modified asphalt mixtures on fuming and microplastics release remains unclear and needs further research. Nevertheless, the increasing number of field trials and widespread interest from transportation agencies around the world indicate the likelihood for the adoption of this technique as a sustainable practice in the pavement industry.

The optimization of energy consumption during asphalt mixture production and compaction is a challenge in producing durable, sustainable, and environmentally friendly asphalt products. This study investigated the effects of crude palm oil (CPO) and/or tire pyrolysis oil (TPO) on shear viscosity and mixing and compaction temperatures of asphalt. Moreover, the possibility of using response surface methodology (RSM) and machine learning (ML) to develop predictive models for the shear viscosity and mixing and compaction temperatures of CPO- and/or TPO-modified asphalt was studied and compared. The results showed that the mixing and compaction temperatures significantly decreased with increasing CPO and TPO, and the shear viscosity consequently declined because of the light components, resulting in softer binders. However, at 5% of both materials, a balance between the required temperatures and a similar or better viscosity compared to the base asphalt were demonstrated. RSM analysis showed that CPO had a significant effect on the viscosity and production temperatures of the base and modified asphalts compared with TPO, which had no significant effects. The developed predictive models based on RSM exhibited a correlation coefficient (R²) of more than 0.82 for all responses. In addition, it was found that extreme gradient boosting (XGB) regression was the best among all evaluated algorithms for predicting shear viscosity, whereas random forest regression (RFR) was the best for mixing and compaction temperatures, with R² values greater than 0.93. The performance evaluations of XGB and RFR showed extremely small differences between the predicted and experimental data. ML outperformed RSM in terms of prediction accuracy.

Digital Image Processing (DIP) algorithms are often required as a precursor to measure the internal characteristics of pavement structures during X-ray computed tomography (XRCT) based non-destructive evaluation (NDE) of pavement materials. The improper use of DIP algorithms can result in the significant under- or over-estimation of internal pavement characteristics, thereby affecting pavement design and maintenance strategies. Past research studies highlighted the significance of threshold segmentation algorithms and binarization of greyscale images on the porosity and permeability characteristics of pervious pavement mixtures. In addition, the use of a watershed segmentation algorithm was introduced to separate interconnected pore network structure into multiple pores. However, isolated pores were not removed in past analyses found in the literature due to a lack of consideration in using ungrouping algorithm to segregate connected and isolated pores. The main objective of this study is to select the appropriate DIP algorithms that can be used to evaluate pervious pavement pore network properties from three-dimensional XRCT based images. In this paper, a key microstructural pore parameter was investigated using various DIP algorithms for different pervious pavement mixtures and recommendations are made. It is expected that the results presented in this paper can help researchers understand the importance of DIP algorithms on XRCT-based pavement evaluation studies.

Pervious pavements can help mitigate climate change effects while improving transportation safety by improving wet pavement friction and reducing splash and spray. Prevailing pervious pavement mix design procedures adopt laboratory-scale friction experiments which cannot capture field wet tire-pavement friction performance. To bridge this gap, this paper presents the application of a newly developed discharge-based thresholding algorithm for wet pervious pavement skid resistance estimation. In particular, x-ray computed tomography (XRCT) scanning, digital image processing (DIP) algorithms and finite-element modelling of wet tire-pavement interaction are adopted to bridge laboratory experiments and field performance. Our developed algorithm is found to be superior in performance when compared against other existing global thresholding algorithms in the literature. It was found from the case study that our developed framework is capable of predicting field skid resistance of various pervious pavement mixtures at the design stage, thereby aiding in the selection of friction-efficient pervious pavement mixtures.

Digital image processing of the X-ray computed tomography images involves the crucial step of image segmentation which affects the subsequent pore structure quantitative analysis. The main objective of this study is to investigate the effect of ten different global thresholding algorithms based on the grey scale histogram, clustering, entropy and laboratory volumetric characteristics on the internal pore structure properties of the pervious concrete. The key microstructural parameters of the pervious concrete air voids such as porosity, tortuosity, throat number, pore coordination number and distributions of pore volume, throat area, pore sphericity, shape factor and throat eccentricity were analyzed for different thresholding algorithms. It was found from the analysis that the nine histogram, clustering and entropy based algorithms are found to be either under or over estimating the air void voxels compared to the volumetric segmentation method. And as the threshold value increases, effective porosity and number of throats increases and isolated porosity and tortuosity decreases due to the increase of air void voxels and pore connectivity. Overall, it is expected that the present study will help in understanding the importance of threshold segmentation in the field of pavement image processing.

Pervious concrete is a special class of concrete with sufficient continuous void structure resulting in the increase of drainage, skid resistance and acoustic characteristics. The paper attempts to investigate the effect of pore network properties obtained using advanced image processing techniques on the non-Darcy permeability characteristics of pervious concrete samples obtained from the same batch mixing process. Twelve different pervious concrete samples for a single pervious concrete mixture were produced in the laboratory using batch mixing and its internal pore network structure was obtained using medical X-ray computed tomography (XRCT) and digital image processing. The pore network structure from the XRCT scan is then adopted into a finite-volume computational fluid dynamics permeability simulation model to evaluate how pore network characteristics can affect non-Darcy permeability coefficients. The key microstructural parameters of the pervious concrete air voids and solids were analyzed in the paper, and it was found that an increase in non-Darcy permeability coefficient can be attributed to higher effective porosity, mean effective pore volume, throat area and coordination number properties. Overall, the findings presented in the paper can help in future optimization of pervious concrete mixture design and provide an understanding towards future works on pavement mixture quality control.

Pervious concrete is widely used as pavement surfaces as means to increase water infiltration for water storage or conservation purposes or to reduce surface runoff (and increase skid resistance) for roadway safety. A proper evaluation of pervious concrete pore network properties is important to ascertain the ability of the material to serve the intended purposes and X-ray computed tomography (CT) scan is one method that allows for the non-destructive evaluation of the pervious concrete specimens. Pore network structures can be derived from X-ray CT scan images through the use of segmentation algorithms. Current image processing-based segmentation algorithms, however, can yield significant errors when deriving pervious concrete pore network properties. This paper describes the use of the watershed segmentation algorithm on X-ray CT scans of pervious concrete pavement mix and evaluate essential pore network properties such as pore volume, flatness, elongation, and shape factor distributions. First, the fundamentals of the watershed segmentation algorithms are described. The paper next presents on the experimental program in testing pervious concrete mix and the use of X-ray CT scans in deriving images of the samples. The watershed algorithm of different elevation functions are then applied to derive the pore network properties and the results are presented. Finally, the advantages of this algorithm over existing image processing techniques are discussed.

The functional performance of pervious concrete pavement surfaces (such as hydraulic, acoustic, and frictional performances) is greatly influenced by the properties of its internal pore structure (such as effective porosity, intrinsic permeability, tortuosity, and pore size distribution). Nondestructive evaluation (NDE) using X-ray computed tomography (CT) and digital image processing (DIP) involves the crucial step of image segmentation of grayscale histograms, which can significantly affect subsequent pore structure analysis and fluid flow simulations. This paper presents a new discharge-based segmentation algorithm capable of predicting non-Darcy permeability of pervious concrete mixtures. The algorithm uses X-ray CT image-based finite-volume permeability simulations to determine the specific discharge at various hydraulic gradients. Experimental results of a falling-head permeability test were used to calibrate and validate the developed finite-volume models. The permeability simulation results from the developed thresholding algorithm were compared against simulation results obtained from 10 different global thresholding algorithms. It was found from the analyses that the developed discharge-based thresholding algorithm predicts non-Darcy permeability characteristics and the effective porosity of the pervious concrete mixtures more accurately than other global thresholding algorithms.

Improvement in rheological performance of asphalt binder with the addition of crumb rubber (CR) depends on the interaction between CR and the asphalt binder matrix. Overall, the change in any rheological property of asphalt binder with the addition of CR can be attributed to two factors: the chemical interaction between CR and the asphalt binder matrix, termed the interaction effect (IE); and the filler nature of CR particles, termed the particle effect (PE). The present research was undertaken to quantify the relative contribution of IE and PE to various parameters responsible for changes in mixing and compaction temperature, high-temperature properties, and intermediate-temperature properties through various rheological approaches. Accordingly, viscosity, G*=Sinδ, multiple stress creep recovery (MSCR), and linear amplitude sweep (LAS) were tested using base binder modified with two CR particle sizes (ASTM# 30-40 and ASTM# 60-80) and keeping the amount of CR constant at 15% by base binder weight. In order to understand the contribution of IE and PE on different rheological parameters, three sets of samples for each CR size were prepared: (1) base binder, (2) undrained CR-modified binder, and (3) drained CR-modified binder. The effect of CR particle filler nature (through PE) was found to be significantly greater than the effect of chemical interaction (through IE) on viscosity, G*=Sind, recovery, and fatigue life. On the other hand, along with PE, the chemical interaction between CR and asphalt binder matrix (IE) was found to play a significant role in improving nonrecoverable creep compliance response. Additionally, based on comparative analysis of IE and PE in various performance-related parameters, the filler effect produced with the addition of 30-40CR particles was found to be more than the filler effect produced with the addition of 60-80CR particles. This indicates that increased CR particle size may cause changes in the filler nature of the CR-asphalt binder composite. Additionally, hightemperature storage stability was evaluated. Base binder with 60-80CR particles exhibited better stability than base binder with 30-40CR particles.