Pavement Design Research Articles

THE IMPACT OF PROJECTED CANADIAN REGIONAL CLIMATE MODEL DATA ON FLEXIBLE PAVEMENT PERFORMANCE

This paper explores the impact of projected climatic loading parameters, utilizing Environment and Climate Change Canada (ECCC) data, on flexible pavement performance and design. Despite the expectation that flexible roads should endure various structural and environmental conditions throughout their design life, premature damage often occurs within the initial three to five years of service. Therefore, understanding the impact of climate change on flexible pavements in the historical, short, intermediate, and long term becomes crucial. The Pavement Mechanistic Empirical Design (PMED) was employed to assess climatic loading effects on pavement design and performance. PMED predicted rutting performances showed sensitivity when comparing historical and projected climatic loading files up to 2093. Preliminary results based on a 25-year design life for City of Windsor revealed a 72% higher rutting impact compared to historical data, shortening the pavement's design life by 28%, 56%, and 68% for short, intermediate, and long terms, respectively.

Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles.

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.

Predicting resilient modulus for pavement design: a comprehensive review of artificial neural network applications

ABSTRACT The Resilient Modulus (M R) is an essential parameter describing the mechanical behaviour of pavement materials, but the tests to obtain it are time-consuming and costly. Therefore, machine learning, especially Artificial Neural Networks (ANN), has recently been an effective alternative for predicting M R. Although there has been an increase in articles on ANN for predicting M R, no systematic review has yet categorised the publications, algorithm structures, predictive parameters, and model accuracy. This review provides a comprehensive overview of studies using ANN to predict M R for pavement materials. It examines various ANN subtypes and model structures, addressing a gap in understanding these models and identifying prevalent predictive parameters. Twenty-five peer-reviewed articles published in English-language journals were identified using keywords related to neural networks and M R. For fine soils, all models reviewed use moisture content as a predictive parameter. In contrast, granular soils models typically include stress state and physical characteristics of the materials. For recycled or stabilised materials, there is significant variability in model inputs. This review underscores the potential of ANN in enhancing predictive models for M R in pavement engineering, pointing towards future research and application refinement in this area of study.

Geosynthetic-stabilized aggregate: quantitative modulus evaluation via Bender Element

Geosynthetics, mainly geogrids and geotextiles, are commonly used to mechanically stabilize unbound aggregate layers in paved road applications. They provide lateral restraint to aggregates to initially increase the layer modulus during construction and minimize its time-dependent decrease under vehicular traffic loading. Different types of geosynthetics may provide different improvement mechanisms and stiffness enhancement levels. Quantitative evaluation of various stabilization geosynthetics is presented herein to compare modulus improvement trends and help incorporate geosynthetic benefits into state-of-the-art mechanistic-empirical (M-E) pavement design procedures. Five geogrids (three extruded, one woven, and one welded) and two geotextiles (one woven and one nonwoven) were studied with a dense graded aggregate in laboratory triaxial testing. Geosynthetics were placed at specimen mid-height and three pairs of Bender Element (BE) sensors were placed at various heights above the geosynthetic to collect shear waves and quantify the degree of mechanical stabilization during resilient modulus testing. While resilient moduli did not distinguish effectiveness of different geosynthetics, shear wave measurements could clearly show local stiffness increases achieved with geosynthetic inclusions. Meanwhile, trends observed in shear modulus profiles at various distances from the geosynthetic, when compared to that of control specimen, properly established the modulus enhancement level achieved in mechanical stabilization with geosynthetics.

Evaluating the impact of temperature variations and subgrade reactions under traffic-load on airport concrete pavement performance

This study delves into the intricate dynamics of airport concrete pavement slabs under the influence of temperature fluctuations and traffic loads. It presents a comprehensive analysis of the stress distribution and subsequent damage in concrete slabs, factoring in the critical role of dowels in load transfer. The research utilizes advanced finite element simulation ABAQUS to model concrete slabs with varying subgrade reactions, subjected to both temperature gradients and aircraft gear wheel load. Findings reveal that lower subgrade reactions mitigate stress levels under temperature variations, while higher subgrade reactions amplify the stress, particularly around dowel areas and leading to potential damage. The application of the Concrete Damage Plasticity (CDP) model demonstrates that temperature-induced stress combined with traffic load can lead to complete damage around the dowel, compromising the load transfer capability. This paper underscores the necessity of integrating temperature effects and traffic loads into pavement design and maintenance strategies to enhance durability and reduce repair costs.

Noise reduction performance and maintenance time of porous asphalt pavement

The noise reduction performance of porous asphalt (PA) pavement is significantly influenced by the presence of air voids, which act as sound absorbers. However, factors such as traffic-induced compaction and clogging due to the accumulation of dust, sand, and debris can alter the morphology and quantity of these voids, consequently compromising noise reduction effectiveness. In this study, a comprehensive investigation into the noise reduction capabilities and maintenance requirements of porous asphalt pavement was conducted by a novel approach. Initially, a three-dimensional structural model of the PA mixture was constructed using X-ray computed tomography. Subsequently, this model was integrated into finite element analysis software to develop a "tire-pavement-air" coupled model. It is important to note that in our numerical approach, we have made several simplifications. For instance, the "tire-pavement-air" coupled model was simplified within the air model is set to fully absorbing boundary condition (*Nonreflecting), and the pavement material was assumed to be linear elastic to ensure computational efficiency and convergence of the model. The findings revealed that as the air-void content increased, the average sound pressure level (SPL) of tire/pavement noise exhibited a gradual decrease. Furthermore, when air voids became obstructed due to factors like rainwater and clogging materials, the SPL of tire/pavement noise initially increased with rising air-void content before subsequently decreasing. Notably, porous asphalt with an air-void content of 20 % demonstrated relatively effective noise reduction capabilities. Based on simulation results obtained under varying conditions, including rainfall and clogging, critical thresholds for air-void content were identified to determine recommended maintenance time for PA pavements. Specifically, an air-void content of 17 % was proposed as the threshold for cleaning maintenance, while a content of 14 % was suggested as an indicator for failure alert, respectively. These findings contribute valuable insights into optimizing the design and maintenance strategies for porous asphalt pavements to enhance their noise reduction performance and prolong their functional lifespan.

Laboratory and Field Evaluation of Asphalt Concrete Modulus &Critical Strain Inputs for M-E Flexible Pavement Design in the State of Illinois

This study aims to update the current algorithm for modulus prediction of hot mix asphalt in the state of Illinois-USA. Numerically, laboratory, and field-obtained modulus and strain levels were connected to advances in asphalt materials and current design criteria. Four mix designs were selected and combined with seven different binders for modulus measurements. The reference experimental results were compared to: the current Illinois Department of Transportation (IDOT) modulus algorithm, traditional regression models, a Bayesian Neural Network (BNN) model, and field measurements obtained using the Traffic Speed Deflectometer. Results showed that the current IDOT algorithm under-predicts modulus and BNN (840 data points, R^2 = 0.986) outperformed the Witczak and Hirsch models. Field modulus and critical strains were sensitive to temperature and showed considerable variability, but confirmed that HMA in perpetual pavement sections performs under low strain levels. These findings guided considerations for an updated design method: realistic modulus prediction can be achieved using data-driven methods and less conservative strain criterion might be considered.

Prediction of Resilient Modulus Value of Cohesive and Non-Cohesive Soils Using Artificial Neural Network.

This paper investigates the application of Artificial Neural Networks (ANNs) for predicting the resilient modulus (Mr) of subgrade and subbase soils, which is a critical parameter in pavement design. Utilizing a dataset of 1683 Mr observations, the ANN model incorporates eight input variables, including soil gradation, plasticity, and stress conditions. The model was optimized using a quasi-Newton method, achieving high predictive accuracy, with a coefficient of determination (R2) of 0.9613 and low error rates for both selection and testing datasets. To further enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, revealing the significant influence of specific input parameters, such as saturation ratio, plasticity index and soil gradation, on Mr predictions. This study underscores the potential of ANNs as a practical tool for estimating resilient modulus, offering a reliable alternative to conventional laboratory testing methods. The findings suggest that integrating ANNs into pavement design processes can lead to more accurate predictions of pavement performance, ultimately supporting the development of more efficient and durable road structures.

Characterising the permanent deformation of subgrade soils under seasonal variation

Rutting, a prevalent failure mode in flexible pavements, largely stems from subgrade issues. Despite this, there is a lack of standard protocols to evaluate subgrade rutting or permanent deformation (PD). This study attempted to characterise PD in subgrades, focusing on a poorly graded sand and two silty sands. Moisture contents above and below optimum levels were considered to account for seasonal variations. The research involved adapting a test to assess the PD by determining typical stresses on the subgrade. Moreover, given these soils' unsaturated state and medium- to fine-grained nature, suction is an important factor. Suction-controlled multi-stage Repeated Load Triaxial tests were conducted, and the results were fitted by a PD model modified to account for suction. The characterisation was compared with the subgrade strain criterion used in pavement design solutions. Results indicated discrepancies between the PD characterisation and strain criteria predictions, with the silty sands performing better than the poorly graded sand, consistent with the shakedown theory.

Impact of Debris Removal Post-Wildfires on Pavement Fatigue and Rutting Lives: Case Studies of California’s Camp and Carr Fires

Between 2017 and 2018, California experienced a series of four devastating fires, including the Camp and Carr Fires, which ranked among the most destructive fires in U.S. history. During these fires, roads were critical in the evacuation, rescue operations, goods transportation, and access to critical services. Additionally, postfire, road infrastructure became crucial for removing hazardous and nonhazardous waste from fire-affected areas to major landfills and recycling facilities. Despite the significance of pavements in this process, previous studies have not quantitatively assessed the potential damage caused to pavements by the additional trucks used in debris removal operations. This research aimed to address this knowledge gap by collecting precise traffic data for the routes taken to waste management facilities, including data on the number of trips involved in debris transportation. The traffic information was then utilized to calculate changes in equivalent single axle loads and traffic index values for pavement design. Pavement structures were obtained from the available core database. Pavement simulation results showed that of the nine studied highways, only one exhibited a reduction in cracking life of about 2 years. However, Skyway, the main artery in the town of Paradise, demonstrated a significantly accelerated fatigue cracking failure by 14.3 years. A sensitivity analysis of fire intensity showed other highway sections that were structurally adequate could be affected by larger fires. The presented methodology could be used in traffic planning as part of debris management operations to avoid vulnerable pavement sections.

THE IMPACT OF EXTREME WEATHER EVENTS OF PROJECTED CANADIAN REGIONAL CLIMATE MODEL DATA ON FLEXIBLE PAVEMENT

This paper presents findings about the influence of projected data from the Canadian regional climate model on the performance of flexible pavement, building upon the results from previous work where the data was generated and published, in which a general trend of decreasing the design life was observed. Projected temperature is the most important extreme climate impact on flexible roads. Adopting a conservative approach demonstrated that two extreme events of Maximum Mean Annual Air Temperature (MMAAT) and Maximum Summer Average Air Temperature (MSAAT) resulted in significant reduction of 25 years road design life. The observed trend indicates a severity range of 7% to 15% in terms of design service life loss when considering events every year compared to every five years. The findings revealed a reduction in pavement design life by 34%, 50%, 73%, and 90% for historical, short, intermediate, and long-term life cycles in the city of Windsor, respectively.

An efficient conditional GAN-based framework for high-resolution prediction of tyre-pavement contact stresses – a contribution towards a digital twin of the road system

ABSTRACT The high-resolution prediction of tyre-pavement contact stresses is crucial for advancing pavement design and vehicle safety. A significant knowledge gap exists in accurately and quickly predicting tyre-pavement contact stresses, especially under varying conditions. Addressing this challenge, this study is part of the concerted effort within the project CRC/TRR 339, contributing to the series ‘A Contribution towards a Digital Twin of the Road System’. It aims to bridge this gap by introducing a novel Multimodal Conditional Deep Convolutional Generative Adversarial Network (MC-DCGAN) model for forecasting tyre-pavement contact stresses, offering an efficient and accurate method. The incorporation of a multimodal conditional module within the GAN-based framework allows for controllable outputs tailored to specific tyre types, inflation pressures, and loads. Leveraging the DCGAN architecture significantly enhances prediction accuracy. Experimental validation confirms the method’s high accuracy, strong generalisation capabilities, and a substantial reduction in computational time – nearly two orders of magnitude lower compared to traditional Finite Element simulations. This research not only fills a critical void in the high-precision prediction of tyre-pavement contact stresses, but also holds significant implications for optimising pavement design and enhancing road traffic safety, marking a significant step towards realising a digital twin of the road system.

Design of Two-Layer Roller Compacted Concrete with Joints by KENSLAB

Roller compacted concrete (RCC) is a type of rigid pavement that is designed in a similar way to conventional concrete pavements reflecting its mode of failure. The thickness design of RCC pavements is based on keeping the flexural stresses and fatigue damage in the pavement caused by wheel loads within allowable limits. As in all jointed concrete pavements, there is a greater effect from loads placed along edges and less at interior locations in the pavement. Joints in RCC pavement are clearly critical areas and form weak points. RCC is typically constructed with saw-cut joints to prevent random cracking, improve the appearance of the pavement surface, and maintain the highest possible load transfer across the joints. This paper presents an approach to designing the thickness of a two-layer RCC with induced cracks (i.e., joints) based on load transfer stiffness and fatigue damage, using the KENSLAB program to obtain pavement life, predict joint deterioration, and pavement damage.

Cement-based and alkali-activated controlled low-strength materials made from cup lump rubber for use as road materials

This research explores the use of cup lump rubber (CLR), an agricultural by-product, as a component in controlled low-strength material (CLSM) for pavement applications in road construction. Two distinct CLSM mixtures were developed: one based on cement and the other on alkali activation. The study evaluated the workability, mechanical properties, and microstructures of both CLSM formulations. Key fresh properties, including slump flow, setting time, and bleeding, were analysed to assess their impact on the self-compaction process. Mechanical characteristics such as unconfined compressive strength, resilient modulus, and wave velocities were also measured. Some CLSM mixtures, both cement-based and alkali-activated, were found to meet the requirements for soil cement bases and subbases. Notably, the resilient modulus values showed significant improvement after 28 days, with certain mixtures achieving subbase-quality gravel standards. The study concludes by recommending the use of both cement-based and alkali-activated CLSMs in pavement design, highlighting their potential to enhance the field of pavement engineering.

Design of a new low-noise pavement

This paper presents the results obtained from the tests carried out on the different asphalt mixes that have been developed as part of the PERSEUS project. This project aims to develop a low-noise pavement in which, in addition to traditional parameters such as texture or absorption, which have been defined based on aggregate size and void content, the aim has been to improve the acoustic response by introducing rubber as a substitute for part of the aggregate in the recipes. Within the framework of the project, porous mixtures and also stone mastic are being developed to determine the solution with the best acoustic characteristics and durability. The paper presents the results of acoustic characterization tests carried out (absorption, airflow resistance) in the phase of design of mixtures, as well as those obtained from the mechanical impedance test, which together with the results of the mechanical tests will conclude which is the ideal mixture to be tested on a stretch of road with real traffic.

Toward understanding the surface morphology and microscopic mechanical properties of asphalt after experiencing tensile and compressive stress

Load-induced stress is a one of the major causes of asphalt pavement failure. It is, therefore, necessary to understand the changes in stress on the microscopic mechanical properties of asphalt in improving pavement design. While much research been carried out concerning effects brought about by tensile, the effects that compressive stress would have on the microscopic mechanical properties of asphalt remains less explored. In the current study, a custom-designed tension–compression device was developed and atomic force microscopy (AFM), complemented by molecular dynamics (MD) simulations, to investigate the microscopic properties of asphalt in both undeformed and deformed states. The findings reveal that asphalt exhibits significantly weaker compressive strength compared to tensile strength. Under tensile stress, the various components in asphalt align loosely in the direction of tension, while asphaltenes are affected the least. These changes in apparent morphology reflect different migration rates of the various components. Under compressive stress, the asphalt surface is more compact, and this reduces the area of the “bee” structure. Under varying stress conditions, asphalt exhibits a smoothing of the rough region with an associated loss in adhesion. Specifically, increased tensile strain leads to smoother topography and increased adhesion, while increasing compressive strain has the opposite effect. These results provide valuable theoretical insights for the design and maintenance of more durable asphalt pavements.

Machine learning-based climate zoning and asphalt selection for pavement infrastructure under changing climate: A focused study of Ningxia, China

Climate change poses significant challenges to the durability and performance of asphalt pavements. This study presents a comprehensive analysis of climatic factors in Ningxia, China, to establish a robust climate zoning framework for asphalt pavements. Utilizing machine learning techniques, specifically the Fuzzy C-means (FCM) algorithm, three distinct climate zones within Ningxia were divided considering climatic features such as maximum temperature, minimum temperature, average temperature, maximum temperature difference, cumulative precipitation, and cumulative radiation. Based on the historical climate data and LTPP model, five asphalt PG zones were classified in Ningxia Province. Besides, six climate sub-zones, which integrated the asphalt PG zones into climate zones, provided a more refined strategy for the asphalt selection. The study also projected future climate scenarios using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to assess the impact of climate change on asphalt selection in Ningxia. The significant changes in pavement temperature indicated the necessity to adapt asphalt pavement designs to future climate scenarios. Overall, this research contributed to the construction of more climate-resilient pavement infrastructures and provided an analysis framework for other regions facing similar climate-induced challenges.

Prediction of permanent deformation of subgrade soils under F-T cycles using SABO-optimized CNN-BiLSTM network

Accurately predicting the permanent deformation of subgrade soils under combined loading and seasonal freeze-thaw (F-T) effects is crucial for optimizing pavement design and maintenance planning in cold regions. However, existing empirical models have limitations in capturing the non-linear deformation behavior influenced by interacting factors. This study developed a deep learning framework, specifically a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM), for time-series prediction of subgrade strains. Laboratory tests established a comprehensive database to examine soil response across various conditions. Regression analysis revealed the constraints of traditional models. The optimized hybrid architecture directly learned patterns from experimental data, outperforming regression by 29−71 % based on error metrics. Sensitivity analysis confirmed that the model identified primary governing inputs consistent with theory. Hyperparameter tuning with the Subtraction-Average-Based Optimizer further enhanced performance. This data-driven methodology demonstrated the potential of advanced machine learning in simulating complex subsurface deformation behavior under compounding factors in seasonal frozen environments.

Calculation Thickness of the Added Flexural Layer on Composite Layer using a Light Weight Deflectometer (LWD) Pusjatan

Calculating the thickness of the flexible overlay needs to consider several factors, such as traffic load, soil characteristics, existing pavement conditions, and planned changes to the road design. This procedure generally uses recognized pavement design methods, such as the AASHTO 1993 method (Guide for Design of Pavement Structures). This research aims to determine the thickness of the added layer using an asphalt layer on composite roads in accordance with the 1993 AASHTO standards. It is hoped that this method can produce an effective overlay design, extend the life of the pavement, and increase the comfort and safety of road users. The data used in this research includes primary and secondary data. The results of this analysis are to calculate the STA 0 segment, for other STAs use the same method. From the Light Weight Deflectometer (LWD) deflection data, the Subgrade Reaction Modulus (k) value was 473 psi/in, the Concrete Elasticity Modulus (Ec) value was 4,843,105 psi and the Rupture Modulus (Sc') value was 699 psi. The traffic load used is 25.000.000, so the result of calculating the plate thickness to serve future traffic (Df) is 10.79 inches or 27.40 cm and the effective plate thickness value (Deff) is 9.13 inches or 23.30 cm, then the result is the added layer thickness (Dol) is 3.30 inches or 8.39 cm

A novel approach for analyzing linear amplitude sweep (LAS) test data of asphalt binders

Fatigue cracking is one of the crucial distresses considered for the design of flexible pavements. The initiation of fatigue cracking occurs in the binder phase of an asphalt mixture. The linear amplitude sweep (LAS) test was developed to analyze the fatigue damage in the asphalt binder. AASHTO T391 provides a method to analyze the LAS test data by fitting a power equation between the fatigue parameter (G*sinδ) and damage intensity. This method has some inherent weaknesses of overfitting or underfitting of the data at different damage levels. This study proposes a new method where a total curve is divided into two parts that fit the data in two different equations and then sum them up. The first part comprises of the data up to cumulative damage corresponding to the maximum effective stress, and the second part consists of accumulated damage in the material till it reaches the maximum strain (30 %). This method is called the bifurcation method in this paper. LAS test data of 27 long-term aged bitumen samples having different physical properties is used to determine the number of fatigue life cycles (Nf) by these two methods, and results are compared in terms of mean absolute percentage error (MAPE), root mean square error (RMSE), and residual sum of squares (RSS). It is shown that there is a significant reduction in the errors when the data fitting was done using the proposed bifurcation method. Further, the ranking was allotted to the samples based on the number of fatigue life cycles (Nf) at three different strain levels (2.5 %, 5 %, and 10 %) at intermediate-temperature conditions estimated using two methods. The shift indicator parameter was used to analyze the variability in the ranking of the samples due to changes in the strain levels. The study concludes that the proposed bifurcation method fits the predicted values more accurately than the prescribed power law.