Pavement Maintenance Research Articles

Predictive modeling of transverse cracking in continuously reinforced concrete pavement: a machine learning approach

Abstract Accurate prediction of transverse cracking in Continuously Reinforced Concrete Pavement (CRCP) is critical for improving infrastructure management procedures and preserving the road network's long-term durability and safety. This paper conducts a thorough analysis into predicting transverse cracking in CRCP using machine learning approaches. The research involved meticulous data preparation, feature selection, and evaluation of various machine learning models to identify the most effective predictor. Key variables such as pavement age, total thickness, temperature, freeze index, traffic volume, precipitation, and initial International Roughness Index (IRI) were analyzed for their impact on transverse cracking occurrences. Sensitivity analysis was conducted to assess the influence of individual input variables on model predictions. Results indicated that the cubic Support Vector Machine (SVM) model outperformed other models, demonstrating exceptional predictive accuracy. Furthermore, sensitivity analysis revealed significant correlations between input variables and transverse cracking occurrences, emphasizing the importance of considering a holistic range of factors in pavement engineering and maintenance strategies. Our findings, which provide insights into the intricate interactions between input factors and pavement distress, help to create tailored treatments and methods for minimizing transverse cracking and enhancing CRCP performance.

INVESTIGATION INTO THE PERFORMANCE OF RIGID PAVEMENT JOINTS AND SEALANTS

This study, conducted by the Georgia Department of Transportation, examines the effectiveness of various joint sealants in rigid pavements over a five-year period. Focusing on a test area along I-20 east of Atlanta, the research involved the construction of 32 test sections with different joint spacings and angles, utilizing eight types of sealants including polysulfide and polyurethane polymers, rubberized asphalt, neoprene, and polyvinylchloride (PVC). The study meticulously monitored the installation process to ensure optimal sealant performance. Key findings suggest significant differences in performance among the sealants, with specific recommendations against using polysulfide and polyurethane for contraction joints unless shape factors are modified. Moreover, the study emphasizes the importance of watertight seals to prevent pavement deterioration due to water ingress and subsequent base material loss, which can lead to slab cracking and reduced ride quality. Recommendations also include the potential use of low modulus silicone for sealing shoulder joints due to its durable and flexible properties, particularly in colder conditions. The study highlights the critical role of proper joint sealing in extending the lifespan and functionality of concrete roadways, providing essential insights for pavement maintenance and construction standards. (Abstract generated by AI tool ChatGPT 4)

Quantification of lifecycle costs for porous asphalt life-extension maintenance methods under managerial uncertainties

ABSTRACT The objective of this research was to evaluate the lifecycle costs associated with emerging pavement maintenance technologies, namely, in-situ rejuvenation and very open emulsion asphalt concrete (ZOEAB+), and scrutinise their suitability over corrective resurfacing maintenance using a stochastic approach. A rational lifecycle inventory was developed by conducting interviews and questionnaire surveys with experts and referring to standard guidelines and international databases. The net present value (NPV) was found sensitive to 12 different inputs with traffic growth rate and discount rate causing the highest uncertainty followed by gasoline and diesel prices. Monte Carlo simulations suggested that the median uncertainty in NPV by using in-situ rejuvenation and ZOEAB+ was 13% and 4% lower than resurfacing. It is envisioned that the research outcomes will assist decision-makers in understanding the uncertainties and costs associated with different maintenance alternatives in the early stages of the project to foster procurement of sustainable and circular pavement maintenance strategies.

A new model for predicting IRI using TabNet with hunter-prey optimization

ABSTRACT To address the prediction problem of the international roughness index (IRI), which is influenced by multiple factors, we propose a hunter-prey optimization (H-PO) TabNet prediction model. First, we combined multiple data sources, including traffic flow, climate data, pavement basic, and pavement condition data, aligned with annual detection data for highways at a spatial resolution of 100 m. Second, we conducted feature importance analysis on the integrated dataset to identify the most significant factors influencing pavement surface smoothness, which serve as inputs for the prediction model. We employed multiple machine-learning prediction models to predict the IRI using the compiled multisource dataset. After comparing the predictive performance of different models, we selected the TabNet model as the base model and optimised its parameter tuning process using the H-PO algorithm. Finally, ablation experiments were conducted to validate the proposed model. The results demonstrate that the H-PO-TabNet model achieved the best performance, with an R 2 increase of 7.92% compared with a model with default parameter settings. The H-PO-TabNet model also attained the highest overall accuracy, with an R 2 value of 0.8763. This model can improve the accuracy of IRI prediction and provide certain data support for pavement maintenance activities.

Multi-objective optimisation for urban road network maintenance based on spatial design network analysis: trade-offs in performance, environmental impacts and costs

ABSTRACT Pavement maintenance decision-making needs to account for multiple conflicting strategic objectives and the complex interdependencies among heterogeneous road sections within road networks. Using spatial design network analysis which allows to capture the complex interdependencies among interconnected road sections, a multi-objective optimisation model is developed for urban road network maintenance. This model integrates multi-dimensional objectives: maximising road network condition, minimising environmental impacts, and minimising agency costs. The model, which uses the non-dominated sorting genetic algorithm II to approximate the Pareto front, is implemented in a real-world urban road network in Shanghai, China. The results show that the trade-offs among the three objectives are more intricate than those found in prior research using traditional decision-making models, which often overlooked the complex interdependencies among heterogeneous road sections. Sensitivity analyses indicate prominently non-linear relationships among objectives: marginal changes in road network condition and environmental impacts fluctuate with different agency costs. As an exploratory effort to develop a network-level multi-objective optimisation model for urban road maintenance through modelling complex interconnections among heterogeneous road sections, this study contributes to a deepened understanding of the complex non-linear associations among performance-environment-cost objectives in pavement maintenance decision-making and offers a network-based approach to perform trade-offs in these conflicting objectives.

Life cycle cost analysis of ultra-thin white topping of geopolymer concrete overlay on bituminous concrete pavement

The social and economic growth of nations greatly depends on effective pavement maintenance methods that adhere to sustainable principles, ensuring long-term durability and performance. Conventional methods for maintaining bituminous concrete pavements, such as using a bituminous concrete overlay, are not always the most efficient. In contrast, concrete overlays have consistently demonstrated superior performance globally. This paper focuses on the design, analysis of life cycle costs, and assessment of emissions of greenhouse gases associated with bituminous concrete pavement from a life cycle perspective. It evaluates two maintenance strategies: a bituminous concrete overlay and an ultra-thin geopolymer concrete overlay (often called "white topping"). The findings suggest that utilizing sustainable materials in an ultra- thin white topping (UTWT) geopolymer concrete overlay is a more favorable option when compared to a bituminous concrete overlay. The life cycle cost study, utilizing the net present value approach, demonstrates its superior economic viability in the long run. The study suggests that using a very thin layer of white topping geopolymer concrete as an overlay on bituminous concrete pavement improves its long term performance and provides economic and environmental advantages.

Providing a Robust Optimization Model for the Preventive Maintenance of Asphalt Road Pavements

Road maintenance and transportation administration must plan the maintenance of the road asphalt network and manage the annual budget. In addition, the primary purpose of doing preventive maintenance (PM) activities is to improve the condition of the asphalt. The purpose of this paper is to present a model for pavement maintenance planning. The goal of this research is to reduce the total International Roughness Index of asphalt roads by taking into account the weight of each part of the road. This research focuses on pavement conditions, addressing the surface layer and its direct impacts on ride quality and durability. A budget has also been set aside annually for road asphalt maintenance. Additionally, sensitivity analysis is performed on the presented model to observe the impact of parameters on the objective function. As a result of changing the budget values, it is established that the objective function improves when that parameter is increased, since it is more flexible. Additionally, the cost of the PM activity was changed, but the objective function remained unchanged. By providing a proper schedule for PM activities and accurate budget management, this model can assist in improving the condition of asphalt. Moreover, this model has been implemented for the roads of North Khorasan province in Iran and its results have been presented. Road maintenance and transportation administrations could use this model to manage their budgets and carry out their PM activities.

Surrogate modelling of surface roughness for asphalt pavements using artificial neural networks: a mechanistic-empirical approach

ABSTRACT Pavement surface smoothness (or roughness) is crucial for traffic safety, driving comfort, and fuel efficiency. As a widely applied roughness indicator, an accurate forecasting of the International Roughness Index (IRI) and its deterioration is essential for the design, maintenance, and management of asphalt pavements. Previous studies have used field measurement data or AASHTOWare Pavement ME Design simulations for the development of machine learning (ML) models to streamline the IRI modelling. However, these models frequently lack the accuracy and robustness of the measurement data or high-fidelity computational simulations they are intended to surrogate. To address this issue, we employed a new adaptive sampling technique to generate an informative yet efficient pavement damage database from Pavement ME simulations. Utilising Artificial Neural Networks (ANNs), we engineered two types of surrogate ML models: (a) Model I, an ANN-based IRI predictive model, and (b) Model II, a hybrid model combining ANN-based predictions of rutting, fatigue damage, and thermal cracking with closed-form relationships between these indicators and IRI. Our findings show that Model II outperforms Model I in IRI modelling accuracy both globally and locally. Moreover, Model II matches IRI simulations of Pavement ME while providing enhanced efficiency and adaptability to a broader spectrum of design considerations.

Sustainable pavement maintenance and rehabilitation planning using the quantum cognitive trust network

Selecting appropriate pavement maintenance strategies can enhance resource efficiency, reduce environmental pollution, prolong road lifespan, and improve societal sustainability. Currently, most models, which selecting pavement maintenance options, exhibit high subjectivity and low reliability. This study employs an innovative probabilistic linguistic multi-attribute decision-making model basing on the quantum cognitive theory. Therefore, the model focuses on expert decision-making groups for pavement maintenance strategies. It considers the trust networks within the decision-making process, as well as the sequential and interference effects arising from experts' belief biases and behavioral interactions. By doing so, it facilitates a more scientific selection of optimal strategies among multiple maintenance options. The proposed method has been validated through practical case studies of road segments, and its scientific validity and effectiveness have been further corroborated through sensitivity analysis.

Corrigendum to “The effects of curing conditions on the performance and carbon dioxide emissions of fly ash-magnesium phosphate cement repair materials for pavement maintenance” [J. CO2 Util. 90 (2024) 102998

Corrigendum to “The effects of curing conditions on the performance and carbon dioxide emissions of fly ash-magnesium phosphate cement repair materials for pavement maintenance” [J. CO2 Util. 90 (2024) 102998

The effects of curing conditions on the performance and carbon dioxide emissions of fly ash-magnesium phosphate cement repair materials for pavement maintenance

The effects of curing conditions on the performance and carbon dioxide emissions of fly ash-magnesium phosphate cement repair materials for pavement maintenance

Self-powered asphalt-based sensors for smart roads

Monitoring traffic conditions and pavement structures is essential for intelligent transportation systems. However, conventional sensors have limitations, including poor compatibility with pavement and high maintenance costs. Here, we present the concept of transforming asphalt from a pavement structural component to a sensing component and demonstrate its application in smart road systems. The functional asphalt was customized by adding piezoelectric materials into the asphalt matrix. We optimized its piezoelectric properties by improving the fabrication process and measured the electrical performance of the asphalt-based sensors. In the traffic monitoring experiment, we developed a system incorporating data acquisition, signal processing, and wireless transmission functions to capture tire-ground contact information. The details, such as speed and wheelbase, are decoded by a feature extraction algorithm and input into a support vector machine (SVM) classification model for training and testing. The model reaches a test accuracy of 97 % in training a small-sample dataset. In addition, the self-powered asphalt-based sensor showed its feasibility and potential in the verification experiments of acoustic source localization (error = 2.4 %) and energy harvesting. Our work proposes a low-cost, environmentally friendly, multifunctional material suitable for road sensors, potentially facilitating the large-scale implementation of smart road networks.

Evaluating the dynamic response and phase angle behavior of SBS-modified asphalt mixtures for enhanced pavement performance

Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs. To address these issues, this study examines the dynamic modulus and phase angle behavior of Styrene Butadiene Styrene (SBS) modified and unmodified asphalt mixtures. SBS was incorporated in various proportions, ranging from 2 to 7% by the weight of bitumen. The asphalt mixture performance tester (AMPT) was utilized to measure the dynamic modulus at temperatures of 4.4, 21.1, 37.8, and 54.4 °C, and frequencies of 0.1, 0.5, 1, 5, 10, and 25 Hz. The study found significant correlations between dynamic modulus, temperature, loading frequency, and SBS content. Additionally, Multi Expression Programming (MEPX) and regression modeling were employed to estimate the dynamic modulus of SBS-modified HMA. Results indicated that increasing SBS content up to 7% decreased penetration and ductility values by up to 46% and 56%, respectively, while raising the softening point by 63% due to increased stiffness. The blend with 6% SBS by weight of bitumen exhibited superior performance compared to other mixtures. Phase angle initially increased with rising temperature, peaking at 37.8 °C at lower frequencies, and continued to increase at higher frequencies. Isothermal and isochronal plots showed that the 0% SBS mix had a higher phase angle due to increased bitumen content. Overall, the HMA mix with 6% SBS provided the best outcomes.

Classification of Asphalt Pavement Defects for Sustainable Road Development Using a Novel Hybrid Technology Based on Clustering Deep Features

In the rapid development of urbanization, the sustained and healthy development of transportation infrastructure has become a widely discussed topic. The inspection and maintenance of asphalt pavements not only concern road safety and efficiency but also directly impact the rational allocation of resources and environmental sustainability. To address the challenges of modern transportation infrastructure management, this study innovatively proposes a hybrid learning model that integrates deep convolutional neural networks (DCNNs) and support vector machines (SVMs). Specifically, the model initially employs a ShuffleNet architecture to autonomously extract abstract features from various defect categories. Subsequently, the Maximum Relevance Minimum Redundancy (MRMR) method is utilized to select the top 25% of features with the highest relevance and minimal redundancy. After that, SVMs equipped with diverse kernel functions are deployed to perform training and prediction based on the selected features. The experimental results reveal that the model attains a high classification accuracy of 94.62% on a self-constructed asphalt pavement image dataset. This technology not only significantly improves the accuracy and efficiency of pavement inspection but also effectively reduces traffic congestion and incremental carbon emissions caused by pavement distress, thereby alleviating environmental burdens. It is of great significance for enhancing pavement maintenance efficiency, conserving resource consumption, mitigating environmental pollution, and promoting sustainable socio-economic development.

SUGARCANE BAGASSE AS AN ALTERNATIVE MATERIAL FOR THE REUSE OF SCARIFIED PAVEMENT MATERIAL

The increasing amount of sugarcane fibrous waste presents environmental problems. The reconstruction of existing highways due to modern globalization results in a larger volume of scarified pavement materials that are often discarded as waste. This study attempts to evaluate the geotechnical characteristics of reclaimed pavement materials that can be stabilized with sugarcane bagasse ash (SCBA). Finding an economical and sustainable way of reusing reclaimed pavement soils instead of using verging laterite soil for road construction could potentially unlock a new dimension. The preliminary results show that sugarcane bagasse ash can potentially improve the reclaimed soil’s strength characteristics, with an increased proportion of sugarcane bagasse ash, Unconfined Compressive Strength (UCS), and California Bearing Ratio (CBR) was found to increase by more than 350% and 100% respectively. The overall geotechnical characteristics are significantly improved by increasing the amount of sugarcane bagasse ash, which make reclaimed soil a potential suitor in relaying and repair maintenance of pavements.

The technological innovation pathway for green, low-carbon, and durable pavement construction and maintenance

The technological innovation pathway for green, low-carbon, and durable pavement construction and maintenance

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.

Two-stage algorithm for automatic repair of pavement cracks

PurposeTo develop an automated system for identifying and repairing cracks in asphalt pavements, addressing the urgent need for efficient pavement maintenance solutions amidst increasing workloads and decreasing budgets.Design/methodology/approachThe research was conducted in two main stages: Crack identification: Utilizing the U-Net deep learning model for pixel-level segmentation to identify pavement cracks, followed by morphological operations such as thinning and spur removal to refine the crack trajectories. Automated crack repair path planning: Developing an enhanced hybrid ant colony greedy algorithm (EAC-GA), which integrates the ant colony (AC) algorithm, greedy algorithm (GA) and three local enhancement strategies – PointsExchange, Cracks2OPT and Nearby Cracks 2OPT – to plan the most efficient repair paths with minimal redundant distance.FindingsThe EAC-GA demonstrated significant advantages in solution quality compared to the GA, the traditional AC and the AC-GA. Experimental validation on repair areas with varying numbers of cracks (16, 26 and 36) confirmed the effectiveness and scalability of the proposed method.Originality/valueThe originality of this research lies in the application of advanced deep learning and optimization algorithms to the specific problem of pavement crack repair. The value is twofold: Technological innovation in the field of pavement maintenance, offering a more efficient and automated approach to a common and costly issue. The potential for significant economic and operational benefits, particularly in the context of reduced maintenance budgets and increasing maintenance demands.

Navigating Cityscapes: Innovations in Urban Mobility and Infrastructure

Abstract The nature and broadening of road transportation infrastructure influence a nation’s ability to grow sustainably. Due to the combined impacts of poor maintenance and structural weaknesses to meet the demands of an increase in traffic intensity and loads, India’s enormous network of existing roads, which were built at considerable expense, is starting to exhibit signs of distress failure before the design period. Maintenance directly affects a pavement’s longevity. The Pavement Maintenance Management System (PMMS) is a scientific strategy for assessing the current state of pavement in a specific area. To provide a more flexible approach that allows tasks to be accomplished more successfully, this project intends to provide a Pavement Maintenance Management System that provides an organized approach for the maintenance, improvement, and operation of city pavements and tools. As part of this study, an initial survey is carried out from Thanakankulam to Thiruparankundram. The World Bank created HDM-4, a software program that may be used internationally to identify the best repair itinerary for highway pavements. To identify the Pavement Quality Index and associated techniques, the HDM-4 tool will conduct further investigations.

Pavement crack detection based on depth-supervision FRRN model

Timely detection of early pavement distress is crucial for effective pavement maintenance. A promising approach involves the segmenting the road surface cracks area from its images. However, the previously employed full-resolution residual network (FRRN), when employing a large number of full-resolution residual units (FRRUs), is susceptible to challenges such as exploding and vanishing gradients. Addressing these issues, this paper has introduced a depth supervision mechanism into FRRN, leading to the development of the Depth Supervision FRRN (DSFRRN) model, designed specifically for accurate segmentation of cracks in road images. This model enhances nonlinear expressiveness while retaining essential features, achieving high precision in pavement crack segmentation. Through experimental validation using a dataset comprising 1565 images of damaged pavements, the result demonstrate that DSFRRN outperforms the original FRRN in terms of both F1 score and Jaccard index, accurately delineating cracks. Consequently, the automated pavement damage detection method presented herein facilitates cost-effective and efficient pavement inspection, contributing to the safeguarding of roadway surfaces and transportation infrastructure.