Journal of Transportation Engineering, Part B: Pavements

In order to solve the problem of poor mixture performance of cold recycled bitumen emulsion mixtures (CRBEM) during service, the improvement effects of air voids, 15°C indirect tensile strength (ITS), and immersion ITS of CRBEM were evaluated under the synergistic action of functional admixtures such as silica fume, microbeads, and water-reducing admixture based on orthogonal testing. The optimal mixing scheme was selected by verifying the mixture performance of CRBEM with functional admixtures. The micromorphology of cold recycled bitumen emulsion mortar under the optimal mixing scheme of multicomponent filler was observed by scanning electron microscopy (SEM). The mortar morphology was analyzed and labeled. The results show that the maximum decrease of air voids of CRBEM with functional admixtures can reach 23.6%. The maximum increase of ITS and immersion ITS can reach 115% and 104%, respectively. When the stress ratio is 0.3, the fatigue life times of Groups 4 and 5 reach 49,495 and 48,772, respectively, which are 9.91 and 9.77 times higher than those of Group 1. Silica fume plays a leading role in multicomponent fillers. The synergistic effect of multiple admixtures in CRBEM is significantly better than that of single or double admixtures. The morphology of bitumen emulsion composite mortar under the optimal mixing scheme of a multicomponent filler is a three-dimensional dense skeleton structure. The multicomponent filler can effectively improve the size and length of the mortar crystal. The multicomponent filler adding method is recommended to improve the performance of CRBEM. The optimal mixing scheme of the multicomponent filler is 1.5% cement, 2.4% external water, 1.5% silica fume, 2.0% microbeads, and 0.5% water-reducing admixture.

Mesostructure-based simulation technology offers an effective approach to modeling the mesomechanical responses of asphalt concrete and studying its mesomechanisms. However, current exploration depth and utilization level of the simulation data are both very limited. The intricacies of the vast mesomechanical response data necessitate the development and adoption of advanced data-processing methods that can gain valuable insights. To this end, this study proposed a data-mining framework that integrates a three-dimensional (3D) random aggregate method and the finite-element (FE) method for mesoscopic simulation of asphalt concrete. This framework mainly consists of two stages. In the first stage, a random aggregate method is used to establish 3D mesostructures of asphalt concrete, and FE simulations are performed on these mesostructures to model their mesomechanical responses. Based on the simulated responses, several procedures for data export, processing, and mining are developed and sequentially performed in the second stage. The data-export procedure extracts the mesomechanical responses from the FE result files and stores them into a database for efficient management. The required response data are accessed via SQL and then processed preliminarily using statistical and computational methods. Machine-learning methods and variable importance analysis techniques are used to conduct in-depth mining analyses on the processed data. The efficacy of this framework was demonstrated by applying it to investigating the effects of the volume fractions of coarse aggregates (larger than 2.36 mm) of different sizes on 129 simulated mesostructures of asphalt concrete with a nominal maximum aggregate size of 13.2 mm. The results indicated that the coarse aggregates with the particle sizes between 4.75 and 9.5 mm may make the highest contribution to the overall dynamic modulus of asphalt concrete. The coarse aggregates with larger size differences could cause more significant stress concentrations in the surrounding asphalt mortar.

Abstract

During the production and transport of reclaimed asphalt pavement (RAP), the diffusion of recycled aged asphalt binder and virgin asphalt binder can significantly affect the mechanical properties of the virgin and aged asphalt binder system. Therefore, a thorough understanding of the diffusion behavior of virgin and aged asphalt binder system is essential for the production of RAP. In this study, a diffusion model of the virgin and aged asphalt binder system was established based on the Fick’s law to calculate the complex shear modulus ($|{\mathrm{G}}_{\mathrm{T}}^{*}|$) of the system. The calculated $|{\mathrm{G}}_{\mathrm{T}}^{*}|$ values were then compared with the measured values ($|{\mathrm{G}}_{\mathrm{D}}^{*}|$) obtained from the dynamic shear rheological (DSR) test. molecular dynamics (MD) simulations were also carried out to investigate the diffusion behavior of the virgin and aged asphalt binder system and to compare the simulated and calculated values of the diffusivity (D). The influence of different asphalt binder layers and saturates, aromatics, resins, and asphaltenes (SARA) on the diffusion behavior of the system was also analyzed. The results show that the $|{\mathrm{G}}_{\mathrm{T}}^{*}|$ values calculated by the diffusion model are in good correlation with the $|{\mathrm{G}}_{\mathrm{D}}^{*}|$ values measured by the DSR test at different temperatures and times. Furthermore, the diffusivity of virgin asphalt binder was found to be smaller than that of the four individual components, suggesting that the colloid structure and intermolecular interactions of asphalt binder play a crucial role in its diffusion behavior.

Practical Applications

Asphalt pavement is a common form of pavement, but it can be seen in life that asphalt pavement often has diseases such as cracks and potholes. The cause of some pavement diseases is the decrease of the performance of asphalt binder in the pavement. Under the action of natural factors, the phenomenon that the performance of asphalt binder gradually changes with time is called asphalt aging. Therefore, if the principle of asphalt aging can be mastered, the influencing factors of asphalt aging can be clarified, and a rejuvenator that can restore the performance of aged asphalt binder to the greatest extent can be designed. It can delay the aging rate of asphalt binder and improve the durability of asphalt pavement, conversely, it can also effectively recycle and renovate old asphalt pavement, reuse waste asphalt concrete, and reduce the waste of resources. This article mainly uses molecular dynamics simulation to achieve the preceding purpose. The result is helpful in providing guidance for the production of RAP in practical application.

The Pavement Management System (PMS) includes constant assessments of the structural and, mainly, functional conditions of the pavement. An automated collection, performed at traffic speeds, in which human error can be minimized, presents perspectives for PMS optimization. To contribute to the functional pavement assessment process, this study investigated the applicability of the functional inventory process for rigid pavements using equipment with laser crack measurement system (LCMS) technology. Test sections were defined following the traditional method of assessing the surface of the pavement proposed by the Brazilian National Department of Transport Infrastructure (DNIT). Additionally, pavement surface macrotexture was evaluated to serve as a basis for comparison with the results obtained with the use of LCMS technology. The analysis of the repeatability and reproducibility of each technique demonstrated that the use of the LCMS equipment and the procedure of analysis is feasible, and could provide equations to correlate the results obtained via the mechanized and traditional procedures.

The evaluation of the comfort of landing operations through pilot scores may be a viable quality indicator to be adopted in airport runway management. However, some factors, such as the aircraft model, can influence this perception. This paper aims to evaluate the physical characteristics of aircraft that affect the pilots’ assessment related to comfort during landing operations at runways. We analyzed 11 runways with different conditions of roughness and measured using the International Roughness Index (IRI), associated with the scores given by pilots in the same runways. In addition, we performed a statistical analysis of four physical characteristics of the aircraft: wingspan, length, base, and gauge. The results indicate a relationship between pilot scores and runway roughness, where the gauge is the physical characteristic with the greatest influence on pilot scores. More specifically, for IRI values between 2.0 and $2.5\mathrm{m}/\mathrm{km}$, these characteristics influenced the perception, with better comfort during landings attributed to smaller aircraft.

Pavement growth (PG) refers to the expansion of the integrated slab length when all the contraction joints of the concrete pavement are close within the expansion joint (EJ). It results from the complex interactions of numerous factors, such as climatic conditions (extremely high temperature and moisture levels), material, and the quantity of incompressible particles infiltrating the pavement joints structure. Currently, the methods and guidelines available for evaluating the annual PG and predictions of concrete pavement blowup are considerably limited. In this study, a mechanistic design framework was developed for estimating the PG and predicting pavement blowup potential. Moreover, a computer tool called Pavement Growth and Blowup Analysis (PGBA) was developed using MATLAB. This tool considers factors such as the pavement configuration, climatic conditions, configuration of EJs, and design reliability. It was used to evaluate the effectiveness of EJs and predict the blowup occurrence times for concrete pavements. To evaluate the PGBA tool, PG data from field measurements conducted by the Maryland DOT were compared with the results obtained using the PGBA tool. The PG predicted by the PGBA tool shows good agreement with the field measurements. However, because concrete pavement blowup data corresponding to PG measurement were not available, a comparison of the PGBA blowup results was not possible. The PGBA tool can be used to predict the service life of EJs and the blowup occurrence time according to the climatic data, pavement structure and materials, joint design, base friction characteristics, and engineering judgment.

This research aims to comprehensively evaluate the performance of cold recycled mixtures treated with cement and plasticizer in laboratory and field settings. The study employed two recycled asphalt fractions, 0–40 mm and 0–20 mm, combined with varying proportions of portland cement (M500), water, and a plasticizer (ZHBI-S). Initial performance assessments were conducted using standard tests, followed by additional tests focusing on rutting resistance, beam fatigue, and elastic modulus of selected recycled asphalt pavement (RAP) compositions. Furthermore, field performance evaluation involved the collection of RAP mixture samples from the asphalt paver’s bunker and core samples from the finished pavement. The investigation’s outcomes suggest that the cold RAP mixture treated with cement and plasticizer can be applied as a binder or base course.

Abstract

The global Pavement-mechanistic-empirical (ME) transfer functions have been calibrated using the long-term pavement performance (LTPP) pavement sections at the national level. These functions may need calibration for local conditions for reliable performance predictions. Several calibration studies have been conducted for local conditions, but limited research is available on the impact of calibration on pavement design. This study calibrates and validates the Pavement-ME transfer functions using version 2.6 for Michigan. A total of 256 flexible and 113 rigid sections have been selected based on the performance and availability of Pavement-ME inputs. The authors obtained the Pavement-ME inputs using construction records, plans, job mix formula (JMF), and material testing results. In contrast, the performance data was obtained from the Michigan Department of Transportation (MDOT) pavement management system (PMS) database. The calibrated models were then used for pavement design to estimate the impact of calibration and for comparison with AASHTO93 designs. The paper identifies the controlling distresses for pavement design. A total of 44 new flexible and rigid sections were designed based on the newly calibrated coefficients. The results show a significant improvement in performance prediction after local calibration. A comparison between AASHTO93 and Pavement-ME designs showed a reduction in hot-mix asphalt (HMA) and plain cement concrete (PCC) slab thicknesses for the latter approach. On average, the surface thicknesses using locally calibrated coefficients were thinner by 0.22 in. (0.56 cm) and 0.44 in. (1.12 cm) for flexible and rigid pavements, respectively. Critical design distresses for flexible pavements include bottom-up and thermal cracking. Conversely, transverse cracking and international roughness index (IRI) control the designs for rigid sections. Evaluating the impact of locally calibrated models on pavement designs is essential to obtaining practical and cost-effective design thickness.

Practical Applications

This study provides a framework for the local calibration. Several factors affect the local calibration process, including the field performance data. The recorded performance data may have irregularities due to measurement errors, limitations in distress identification, and conversion to the Pavement-ME units. Therefore, the authors recommend analyzing the raw performance data and filtering it (if required) for practicality. Pavement design is one of the most crucial calibration process steps and is often not considered in practice. It is worth mentioning that pavements were designed based on calibrated models, and based on MDOT feedback, the calibration improved the designs. The authors recommend that the calibration results should not be based only on statistical parameters (SEE, bias, etc.) but also on practical engineering judgments. By understanding which distress types are most relevant to their region, agencies can develop mitigation and maintenance strategies leading to longer pavement service lives. For example, MDOT can mitigate the occurrence of thermal cracking, a critical distress, by using modified and improved binders.

The vehicle dynamic load can greatly affect the service life of pavement. To study the vehicle dynamic load accurately and conveniently under different pavement characteristics and driving conditions, a multibody dynamics method was employed to establish a three-dimensional (3D) vehicle dynamics simulation model of a six-axle heavy vehicle (the most typical heavy vehicle on an expressway). Experimental tests of vehicle component parameters and axle spindle loads were performed to provide simulation parameters and to validate this vehicle dynamics model, respectively. The validation results showed that the accuracy and robustness of the dynamics model were satisfactory. Based on the validated model, the effects of International Roughness Index (IRI) and vehicle speed on the dynamic load coefficient (DLC) were explored. The DLC increased by about 80% when the speed or IRI changed from $30\mathrm{km}/\mathrm{h}$ or $1.184\mathrm{m}/\mathrm{km}$ to $70\mathrm{km}/\mathrm{h}$ or $3.551\mathrm{m}/\mathrm{km}$, respectively. To better evaluate the effect of dynamic load, the relationship between the DLC of the vehicle and the vehicle speed and IRI is proposed according to simulation results. This relation equation can quickly and accurately study the DLC of this heavy vehicle under different IRI pavements and driving speeds. Because the vehicle dynamics model is convenient for studying the dynamic load under different conditions, this method is recommended in vehicle–pavement coupled analysis.

Pavement construction monitoring and quality assurance (QA) practices are based mostly on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in the case of the availability of an inertial profiler. However, most of these practices are costly and/or destructive. Therefore, the key objective of this study was to develop a quality assurance decision-making tool that can predict pavement roughness, in terms of the International Roughness Index (IRI), and aggregate segregation based on digital image analysis, image recognition, and deep machine learning models. The developed models were trained, tested, and validated using 600 pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS). Furthermore, the effectiveness of the convolution neural network (CNN) model was validated using pavement surface images collected at construction sites in Louisiana a few days after paving. The roughness model predicted the International Roughness Index values with a coefficient of determination $R^2$ of 0.98 and a RMS error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved acceptable accuracy. To support the implementation of these results, the models were incorporated into a computer application that can be used by site engineers for quality assurance without the need for coding software on their device.

Road surface condition (RSC) is an important indicator in road safety studies, enabling transportation departments to employ it for conducting surveys, inspections, cleaning, and maintenance, ultimately contributing to improved performance in road upkeep. However, traditional recognition methods can be easily affected when extreme weather frequently occurs such as winter seasonal changes. To achieve real-time and automatic RSC monitoring, this paper proposes an improved Mask–region-based convolutional neural network (RCNN) based on Swin Transformer-PAFPN and a dynamic head detection network. Meanwhile, transfer learning is used to reduce training time, and data enhancement and multiscale training are applied to achieve better performance. The experimental results show that the proposed model achieves an outstanding mean average precision at 0.5 ([email protected]) score of 89.8 under favorable weather conditions characterized by clear visibility, surpassing other popular methods. Notably, the proposed model exhibits lower parameters and GigaFLOPS (GFLOPs) (72.41 and 158.35, respectively) compared to other popular methods, thus demanding fewer computational resources. Furthermore, in challenging weather conditions characterized by poor visibility, such as foggy and nighttime scenarios, the proposed model achieves [email protected] scores of 78.50 and 82.40, respectively. These scores not only outperform those of other popular methods but also underscore the robustness of the proposed model in extreme weather conditions. This exceptional performance demonstrates the proposed model’s effectiveness in addressing complex road conditions under various meteorological circumstances, providing reliable technical support for practical traffic monitoring and road maintenance.