Pavement Management Research Articles

A 3D reconstruction-based method using unmanned aerial vehicles for the representation and analysis of road sections

Due to the fast growth of cities worldwide, roads are increasing daily, and pavement maintenance has become very heavy and costly. Despite all efforts made under the pavement management system to keep the road surface in good shape, several road sections need to be in better condition, which presents a danger for drivers and pedestrians. This paper proposes a novel pavement 3D reconstruction and segmentation approach using the structure from motion technique, unmanned aerial vehicle, and digital camera. The method consists of the 3D modeling of the road by using images taken from different perspectives and the structure from motion technique. In this method, points cloud is sampled and cleaned using statistical outlier removal and noise filters. After that, duplicated and isolated points are eliminated to retain only significant data. The normal road plane is estimated using the principal component analysis technique and the remaining points. This plan presents a root mean square less than 0.85 cm. Finally, distances from those points to the normal plane are calculated and clustered to segment the road into distressed and non-distressed areas. The proposed approach presents a similarity rate to the survey measurement passed 95%. It has demonstrated promising results and has the potential for further improvement by optimizing various steps.

Pavement Condition Management Coupled with Skid Management at the Network Level: A Multi-Choice Goal Programming Approach

Highways promote economic growth and social development. Traditionally, pavement management considers pavement conditions for maintenance and rehabilitation (M&R) decision-making along with system constraints. One of the major tasks of highway agencies is to promote safety. Highway safety involves a complicated interaction of multiple factors. However, it is well established that there is a link between pavement surface friction and safety. Pavement surface characteristics affect the safety and comfort of passengers. Since the pavement management process considers only pavement physical conditions in M&R decision-making, there is a need to include safety indicators in the process to promote both riding comfort and safety. This study aims to integrate the pavement surface friction, that is, the skid resistance, into traditional pavement management. An optimization model has been proposed based on multi-choice goal programming by systematically integrating skid resistance into pavement M&R decision-making along with pavement conditions. Multi-choice goal programming allows setting interval aspirations rather than a fixed target. A case study has been conducted to demonstrate the usefulness of the proposed model in managing pavement conditions and pavement skids together. The case study considered five goals related to pavement conditions, skid states, and M&R costs. Interaction among the goals is discussed in different scenarios. The case study also uses friction treatment with traditional pavement management treatments. The model can capture the interaction between pavement conditions and pavement skids and determine the program that best achieves the goals. The proposed method provides a simple approach to selecting a network-level M&R program and obtaining insights into the management of conditions and skid together.

Modelling Asphalt Overlay As-Built Roughness Based on Profile Transformation-Case for Paver Using Automatic Levelling System.

The as-built roughness, or smoothness obtained during pavement construction, plays an important role in road engineering since it serves as an indicator for both the level of service provided to users and the overall standard of construction quality. Being able to predict as-built roughness is therefore important for supporting pavement design and management decision making. An as-built IRI prediction model for asphalt overlays based on profile transformation was proposed in a previous study. The model, used as basis for this work, was developed for the case of wheeled pavers without automatic screed levelling. This study presents further development of the base prediction model, including the use of an automatic screed control system through a long-distance averaging mobile reference. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver using contactless acoustic sensors set-up over a floating levelling beam attached to the paver. To calibrate the model, longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used, obtaining a mean error of 0.17 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative.

Use of Distributed Fiber Optic Sensors for the Monitoring of an Accelerated Pavement Test

The need to develop more efficient pavement management systems has driven continuous improvement in structural health monitoring (SHM) systems for road infrastructures. Factors such as low energy consumption, minimal disruption (small-sized and lightweight devices), resistance to environmental conditions, high durability, and sensitivity are some of the possible improvements worthy of exploration. Fiber optics appear to have several of these desired characteristics. In this context, this article presents strain measurements performed using fiber optic cables installed at various levels in pavement structures tested on the accelerated pavement testing facility at the Université Gustave Eiffel (or fatigue carousel). The results reveal that strains measured with this technology closely align with those obtained using traditional strain gauges. However, an advantage of fiber optics is that they enable distributed measurements over long distances with high precision (10 m with a measurement interval of 2.6 mm in this study). These measurements highlight a significant variability in pavement strains, seemingly attributable to the heterogeneity of pavement materials and variations in layer thicknesses. As a result of these findings, laboratory-scale investigations have been initiated to gain a better understanding of these sources of variability.

A novel technique for multi-objective sustainable decisions for pavement maintenance and rehabilitation

To maintain pavement in good condition while considering financial costs and sustainability, it is necessary to develop a comprehensive pavement management plan. Pavement Maintenance and Rehabilitation (M&R) consists of two essential components: firstly, predicting the pavement condition within a specified timeframe, and secondly, employing an appropriate optimization algorithm. This study utilized three ensemble learning techniques including extreme gradient boosting, categorical boosting, and light gradient boosting machine to develop accurate predictions about the pavement condition. Subsequently, the most accurate prediction technique, which was extreme gradient boosting, was combined with non-dominated sorting genetic algorithm III which is a multi-objective metaheuristic optimization algorithm, resulting in a hybrid technique that offers highly accurate multi-objective maintenance and rehabilitation planning. Although previous studies neglected important criteria such as road closure in the optimization process, this study takes into account four objective functions including greenhouse gas emission, M&R cost, pavement condition, and road closure to be minimized over a 5-year program. This process generated 52 non-dominated optimal solutions known as the Pareto front. To compare and rank various optimal maintenance and rehabilitation plans, grey relational analysis was employed. The results suggested that there is a direct correlation between M&R costs and GHG emissions. Minimizing only pavement conditions in the planning can significantly increase GHG emissions, M&R costs, and road closure. Implementing preventive M&R actions can reduce M&R costs and overall road closure while light and medium rehabilitation actions are recommended to optimize the condition of pavements.

The impact of autonomous vehicles on the performance of steel bridge deck pavement

The widespread application of autonomous driving technology has become a prominent trend in the transportation industry. The purpose of this study was to investigate the impact of the lateral distribution pattern of autonomous vehicles (AVs) on steel bridge deck pavements (SBDP), with the aim of providing reliable theoretical support for future bridge deck pavement management and operation. A mixed traffic flow lateral distribution model was established and the Nanjing Yangtze Fourth Bridge was used as a case study. A corresponding finite-element model (FE) was constructed and Abaqus FE software was used to analyse rutting depths under different scenarios. The simulation results demonstrated that, under the zero distribution mode, a rational layout of AVs can significantly alleviate rut formation. Conversely, an unreasonable layout will reduce the service life of SBDP. Comparing the lateral distribution patterns of different vehicles, both the normal distribution and uniform distribution patterns exhibited positive effects on the SBDP. However, there was no significant difference in the magnitude of this positive impact between the two modes. This research not only fills the gap in the study of AV impact on SBDP but also proposes management methods for AVs.

Physics-informed neural networks to advance pavement engineering and management

Physics-informed neural networks (PINN) are machine learning (ML) algorithms that can bridge the gap between our understanding of physical phenomena and the corresponding empirical observations. This paper discusses applications of physics-integrated ML to advance pavement engineering. To demonstrate an example, a PINN model was pretrained to approximate the simulation of vehicles’ suspension responses to longitudinal road profiles. The parameters of the outer layers were finetuned to adapt model output to the standard International Roughness Index (IRI), while keeping the pretrained inner layers to preserve the embedded physical knowledge of the suspension behaviour. The PINN model showed low bias and standard error in predicting IRI values on training, test, and an independent dataset from an autonomous vehicle study by the Ford Motor Company. This approach to reconcile and supplement infrequent survey data with spatiotemporally continuous data (from connected vehicles) can enhance data-driven practices for pavement design, maintenance and asset management.

PROBABILISTIC MANAGEMENT OF PAVEMENT DEFECTS WITH IMAGE PROCESSING TECHNIQUES

Pavement management has traditionally relied on human-based decisions. In many countries, however, the pavement stock has recently increased, while the number of management experts has declined, posing the challenge of how to efficiently manage the larger stock with fewer resources. Compared to efficient computer-based techniques, human-based methods are more prone to errors that compromise analysis and decisions. This research built a robust probabilistic pavement management model with a safety metric output using inputs from image processing tested against the judgment of experts. The developed model optimized road pavement safety. The study explored image processing techniques considering the trade-off between processing cost and output accuracy, with annotation precision and intersection over union (IoU) set objectively. The empirical applicability of the model is shown for selected roads in Japan.

Positioning and detection of rigid pavement cracks using GNSS data and image processing

Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. Manual inspection is performed by trained inspectors to assess pavement damages conventionally. This can be cost-intensive, time-consuming, and a source of risk for inspectors. An image-based inspection using a smartphone is adopted to overcome such problems. This paper proposes an automatic crack detection and mapping program for rigid pavement, which can automate the visual inspection process. The program uses Global Navigation Satellite System (GNSS) data recorded by smartphones and various image processing techniques to detect crack lengths and areas in images. The performance of the program was evaluated by a field study. A crack quantification process was performed to compare the manually measured values and crack lengths obtained from the program. The results show that the program can detect other types of distress, such as pop-outs and punch-outs. This method can achieve satisfactory performance compared to the effort and costs spent.

A Bayesian decision support system for optimizing pavement management programs

Over time, the pavement deteriorates due to traffic and the environment, resulting in poor riding quality and structural inadequacies. Evaluating pavement condition over time is thus a critical component of any pavement management system (PMS) to extend the service life of pavements. However, the uncertainty associated with the pavement deterioration process due to the heterogeneous nature of the pavement degradation factors makes the process difficult. The current work addresses this challenge of pavement management by developing an expert system framework based on Bayesian Belief Networks (BBN). This framework integrates data on existing road deterioration factors with knowledge gained from pavement experts to produce optimal decisions. The advantages of the BBN techniques lie in their ability to capture uncertainty, and probabilistically infer the values of variables in the domain, especially in the case of incomplete information where we only have data about some and not all variables. This has motivated the adoption of BBN in this study to optimize pavement maintenance decisions, on the basis of inferred road deterioration interpretations drawn from partial knowledge about road distress variables. This study presents the adoption of Bayesian methods to assist pavement maintenance engineers in determining the most successful and efficient maintenance and repair (M&R) tactics and the best time to apply them by means of a decision-support system. Data collected from 32 road sections in the United Arab Emirates in relation to road distress parameters (rutting, deflection, cracking, and international roughness index), as well as road characteristics, traffic, and environment data, has been used to demonstrate the applicability of the proposed decision-support tool.

Multi-Objective Optimization for Sustainable Pavement Maintenance Decision Making by Integrating Pavement Image Segmentation and TOPSIS Methods

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.

U-Net-based integrated framework for pavement crack detection and zone-based scoring

ABSTRACT Pavement scoring systems are cost-effective means of integrating the pavement management to prevent the progression of failure. The current research developed a pavement scoring system in which the presence and severity of longitudinal and transverse cracks in asphalt pavements are detected through digital images. The pavement cracks were identified using U-Net-based network architecture optimised by the grasshopper optimisation algorithm. The quantum geographic information system was used to reveal the cracks on the study zones. These zones then were evaluated, compared and scored according to their mean crack length, mean crack width and the number of cracks. The analytic hierarchy process based on expert opinion was employed to determine the importance and weight of these factors. Each area received a score based on the condition of the pavement that enabled comparison and identification of the critical areas. The proposed method can allow transportation agencies to identify and evaluate critical areas and determine the proper maintenance and repair priorities.

A quantitative methodology for measuring the social sustainability of pavement deterioration

The social pillar of pavement sustainability is understudied compared to economic and environmental pillars, making it difficult to integrate into life-cycle methodologies. While methods such as social life cycle assessment (S-LCA) exist, they usually focus on social governance rather than quantifying the impact of pavement investment decisions on communities. This study introduces a methodology to quantify the impact of road condition on vulnerable communities, specifically Environmental Justice (EJ) communities. The methodology calculates the impact of road condition on residents and analyzes fuel consumption (as an example impact) for road users during recurrent home-work trips as a function of pavement condition for EJ and non-EJ communities. It was found that EJ communities in Massachusetts are twice as likely to live near poor condition roads and consume twice as much excessive fuel during recurrent home-work trips. The proposed method is designed to integrate into existing life-cycle methods and represents a significant step towards integrating equity into pavement management decisions.

Automatic Classification of Pavement Type and Service Age Benchmarked with Standard Texture Databases Using the Machine Learning Method: A Pilot Study

Pavement intelligent management systems have attracted considerable interest from researchers. However, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ii) texture service ages, and (iii) a combination of (i) and (ii), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively. In conclusion, the CNN demonstrates a high capability in expressing the pavement texture features and achieves satisfactory identification results for pavement surface types, but is inferior for texture service age. It is promising that the presented results could serve as a foundational exploration in the automatic identification of texture service conditions benchmarked with standard texture databases to facilitate pavement management systems.

Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters

The importance of skid resistance performance assessment in pavement engineering and management is crucial due to its direct influence on road safety features. This paper provides a new approach to skid resistance predictive model definition based on experimentally obtained texture roughness parameters. The originally developed methodology is based on a photogrammetry technique for pavement surface data acquisition and analysis, named the Close-Range Orthogonal Photogrammetry (CROP) method. Texture roughness features were analyzed on pavement surface profiles extracted from surface 3D models, obtained by the CROP method. Selected non-standard roughness parameters were used as predictors in the skid resistance model. The predictive model was developed by the partial least squares (PLS) method as a feature engineering procedure in the regression analysis framework. The proposed model was compared to the simple linear regression model with a traditional texture parameter Mean Profile Depth as the predictor, showing better predictive strength when multiple non-standard texture parameters were used.

The Conceptual of Pavement Management System Based on IoT, Big Data, and Data Mining in Indonesia

The country’s economic development and people’s lives require reliable transportation and logistics services. Therefore, road maintenance policies and techniques must be more comprehensive to ensure transportation infrastructure operations’ efficiency. The inefficiency of road maintenance, among others, is due to the failure to identify and predict the level of damage that can cause fundamental data anomalies. Therefore, the current scientific development of road maintenance must be able to take advantage of the convenience of technology. Widespread technology implementation on the Internet of Think (IoT), big data, and data mining (DM) can be a solution to developing a better pavement management system. However, implementing these technologies is challenging due to the weak conception and availability of currently available resources. This paper offers a pavement system management implementation concept that can be developed in Indonesia to improve the existing system. The study results show that IoT, big data, and DM are believed to support a more intelligent and comprehensive pavement management system. The concept offered consists of three main things: identification of damage and 3D pavement modeling, data analysis, decision support systems, and collaboration of intelligent solutions in implementing maintenance in the field. This paper is equipped with an illustration of the concept of road maintenance based on IoT, big data, and DM, which can be implemented intelligently and simply in Indonesia.

Pavement Sections’ Reliability Based on Deterioration Model Using Artificial Neural Network (ANN)

Pavement distresses, such as cracks and ruts, reduce pavements’ effectiveness and serviceability and can lead to failure. This underlines the importance of predicting pavements’ deterioration in pavement management systems (PMSs) for effective maintenance and rehabilitation (M&R) strategies. Consequently, it is essential to understand the concept of service life, which represents how long a pavement will remain in service based on how reliable it is. This study introduces a pavement deterioration model using data from the Long-Term Pavement Performance program for the international roughness index (IRI) and other factors. Different machine learning methods were utilized in developing the model to incorporate eight factors that significantly affect pavement roughness; these methods are: linear regression, regression tree, Gaussian Process Regression (GPR), Support Vector Machine (SVM), Ensemble Trees, and Artificial Neural Network (ANN). For comparison, the models’ performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R squared (R2). The weights and biases of the best model and the Federal Highway Administration (FHWA) recommended IRI ranges were utilized to create the limit state function. A reliability analysis using Monte Carlo Simulation (MCS) was determined to calculate the sections’ probability of failure. This study concluded that pavement sections in the US and Canada are reliable and that the mean yearly Kilo Equivalent Single Axle Load (KESAL) significantly contributes to pavement failure. Keywords: Pavement deterioration, IRI, Machine learning, Neural network, Pavement reliability, Probability of failure.

Development of a multicriteria matrix for decision making in the management of flexible pavements in an urban road network. Case study the city of Itagüí – Colombia

Decision-making for interventions in urban road networks is often carried out reactively, addressing roads in the worst condition first, which reduces the length of intervention in the entire urban road network under an assigned budget. The objective of this work was to establish a methodology to prioritize the intervention of asphalt pavements on urban roads through a multi-criteria matrix that allows optimizing the management of pavements in the network. The criteria were previously adjusted and validated by road infrastructure experts using the Likert scale. Subsequently a large group of experts completed the multi-criteria matrix to qualify and weigh each of the criteria and intervention alternatives considered to address each of the established problems; the goal is to preserve the urban road network in good condition. With the support of Geographic Information Systems (GIS) tools, each criterion was represented by a vector shapefile format, each of them was transformed into raster format and reclassified to assign a specific score. The attributes of the raster format used information from the road network of the city of Itagüí (Colombia). The final result was the weighted overlay of maps that allowed establishing the prioritization of the roads to intervene according to the score obtained in each road segment, which allows the administrator to make efficient decisions in an urban network.

Research and comparison of pavement performance prediction based on neural networks and fusion transformer architecture

<abstract> <p>The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior optimization of the L-ANN model. Subsequently, by incorporating structures such as self-attention mechanism, a novel predictive approach based on the Transformer architecture was proposed. The objective is to devise a more accurate predictive methodology for pavement performance indices, with the goal of guiding pavement maintenance and management efforts. Experimental results indicate that, through comparative analysis of three quantitative evaluation metrics (root mean square error, mean absolute error, coefficient of determination), along with visual scatter plots, the predictive model employing the fused Transformer architecture demonstrates higher robustness and accuracy within the domain of pavement performance prediction when compared to the L-ANN model. This outcome substantiates the efficacy and superiority of the model in terms of predictive performance, establishing it as a reliable tool for accurately reflecting the evolution of asphalt pavement performance. Furthermore, it furnishes a theoretical reference for determining optimal preventive maintenance timing for pavements.</p> </abstract>

ADVANCING INFRASTRUCTURE IN DEVELOPING NATIONS: A SYNTHESIS OF AI INTEGRATION STRATEGIES FOR SMART PAVEMENT ENGINEERING

This research focuses on the transformational potential of artificial intelligence (AI) towards the field of pavement engineering, particularly considering underdeveloped nations. This exploratory study aims to shed light on the complex effects AI has on pavement engineering and management processes, as well as various strategic, policy, and ethical perspectives underpinning its implementation. By using a systematic process, the study builds on information from academic papers, case studies, and industry research to create more enriching knowledge on this topic. Through a robust study, it is shown that AI helps improve the composition of pavement materials building and maintenance routines. Such a study shows that AI can improve the level of economic benefits in the pavement infrastructure of underdeveloped countries. These key findings emphasize how AI reduces the costs of design, predictive maintenance models, and sustainable materials use. In addition, the study traverses through the difficult routes of policy and institutional frameworks, stating that the policies ought to be responsive while collaboration is required in promoting AI uptake. The discussion integrates ethics and the environment very carefully, as required by sustainability and equity. Lastly, the research presents an excellent argument for AI as an agent for constructing new and eco-friendly transportation infrastructure in poor countries. These countries’ recommended policies should be flexible, as they promote capacity building and ethics in AI with regard to infrastructure development as part of their developmental paths, which are simultaneously technical and social.
 Keywords: Artificial Intelligence, Pavement Engineering, Developing Nations, Sustainable Infrastructure, Ethical AI, Transportation