Search

The availability of high-quality aggregate for infrastructure development in Canada has significantly decreased. This reduction has posed significant challenges, particularly in large urban areas across Canada. Moreover, exploring the potential of incorporating innovative by-products into pavement structures has displayed potential advantages in technical, economic, and environmental aspects. The methodology presented in this paper is based on several research projects from over the past twenty years. This case study is based on the several research projects that have been conducted at the Centre for Pavement and Transportation Technology (CPATT) in Canada. The focus of this paper is developing a framework that can be used to evaluate the use of recycled materials in asphalt pavement. These include recycled concrete aggregate (RCA); recycled asphalt roof shingles (RAS); recycled crumb rubber (RCR); and recycled asphalt roof shingles (RAS) in asphalt pavement. In addition, the use of RCA in concrete pavement will also be evaluated. In addition, the use of RCA in concrete pavement will also be evaluated. A methodology and strategy for implementing these materials and designs into specifications, while also managing risk, are outlined. Finally, the work presents a life cycle assessment analysis, including the analysis of cost and sustainability. Overall, the paper illustrates the process of evaluating products, technologies, and designs from technical, economic, and environmental viewpoints, and then implementing them in a robustly engineered way. Generally, the collected data results from the lab, field performance of the test sites, and sustainability and Life Cycle Cost Assessment (LCCA) analyses are very promising. For example, the Hot Mix Asphalt (HMA) pavement design with a 40% RAP was the most environmentally friendly. Meanwhile, it was determined that the HMA mix comprising 20% RAP and 20% Crumb Rubber Modifier (CRM) was found to be the most creative and environmentally friendly option. According to another study, higher storage durations were shown to improve RAP and virgin binders' blending, thereby raising the general quality of HMA and RAP mixtures. Furthermore, the study discovered that mixtures containing various types of untreated CRCA in different amounts had higher Indirect Tensile Strength (ITS), rutting resistance, and stiffness modulus than the control mix, which contained 0% coarse recycled concrete aggregate (CRCA). This suggests that the effective utilization of recycled materials is a viable approach with the capacity to significantly enhance the adoption of recycled components in asphalt pavements. [ABSTRACT FROM AUTHOR]

Semirigid pavements are gaining importance in the pavement design field due to their improved performance in freezing zones. These semirigid pavements are constructed similarly to asphalt pavements, but with a cement-stabilized base layer. However, there is a limited study that quantifies the influence of design factors on the performance of semirigid pavements. Therefore, an attempt was made to investigate the role of subgrade, temperature, moisture, traffic, crack joint spacing, speed, asphalt thickness, cement-treated base (CTB) layer thickness, elastic modulus, and modulus of rupture of CTB layer on semirigid pavement design. Different combinations of factors were formulated using fractional factorial design in the design of the experiment (DoE). All factors influencing the design of semirigid pavements were considered on two levels (low and high). This analysis used data from the Federal Highway Administration's (FHWA's) Long-Term Pavement Performance Program (LTPP). These data were incorporated into the AASHTOWare Pavement ME Design 2.6.1 (MEPDG) software. Design-Expert was used to determine the significance of various factors on the performance indexes. In addition, an optimization technique was used to determine the optimal design thickness for cement-treated base under various subgrade and climatic conditions. The result suggests that the thickness of a CTB layer is greater on fine-grained soil than on coarse-grained soil. [ABSTRACT FROM AUTHOR]

• We comprehensively analyze various factors affecting pavement condition index (PCI) • We identify the important determinant features for building PCI prediction models. • We develop a new FCM-XGBoost modelling framework to effectively predict the PCI. Predicting Pavement Condition Index (PCI) is crucial for identifying potential pavement distresses and developing effective pavement maintenance strategies. Here, a new Fuzzy C-Means-eXtreme Gradient Boosting (FCM-XGBoost) PCI prediction system is developed. First, asphalt pavement sections and concrete pavement sections are distinguished, and the Fuzzy C-Means (FCM) clustering algorithm is employed to cluster the asphalt and concrete expressway sections respectively based on multiple factors influencing PCI. Next, an eXtreme Gradient Boosting (XGBoost) model is trained for each cluster of expressway sections. Finally, in the system application stage, the PCI of an expressway section for the next year is predicted using the XGBoost model trained for the specific cluster that the expressway section belongs to. Validation results based on actual pavement condition data indicate that the proposed FCM-XGBoost PCI prediction system can achieve slightly higher accuracy compared with several high performance benchmark models, which include the Random Forest model, the Gradient Boosting Decision Tree model, the Light Gradient Boosting Machine model and the XGBoost model. The developed PCI prediction system contributes to the improvement of PCI prediction approaches and can be potentially applied in deploying effective and cost-efficient pavement maintenance strategies. [ABSTRACT FROM AUTHOR]

Pavement segregation usually cause engineering quality problems such as early damage of asphalt pavement. Therefore, it is of great significance to carry out segregation detection in the construction process. However, existing image detection technology has two problems: 1) the segregation boundary and the index are not clear; 2) the segregation assessment is localized. In order to find useful solutions to these two problems, this study proposed an Aggregation Segregation Generative Adversarial Network (AG-GAN) to generate virtual pavement images with segregation failure. Moreover, a multi-scale fusion method of Recurrent Neural Network (RNN) and the Global Averaging Sliding Window (GASW) algorithm is developed to achieve globalized aggregation evaluations. Based on extensive experiments tests, conclusions obtained that the accuracy rate of re-current neural network for segregation determination of pavement sequence can be up to 84.62%. In addition, automatically assessment of the segregation condition of pavement section can be achieved, which proves the practical application effectiveness of the proposed algorithm in the actual paving process. [Display omitted] [ABSTRACT FROM AUTHOR]