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Existing approaches to infrastructure management neglect the impact of infrastructure condition on safety performance, even though poorly maintained roadways can contribute to increased crash frequencies. This is perhaps because very few studies have quantified the impact of infrastructure condition on safety performance. Hence, this work develops safety performance function (SPF) models that relate pavement condition—specifically, roughness, as measured using the international roughness index (IRI)—and other roadway factors with observed crash frequencies. These SPFs are then combined with pavement roughness deterioration models to better understand how maintenance decisions can be used to improve roadway safety performance. Data from two-lane rural roadways in Pennsylvania are used for this effort. The SPF estimation results reveal that accounting for IRI in SPF development significantly improves model fit, and that IRI has a significant and positive impact on crash outcomes. The results further suggest that the IRI has a different impact on total crash frequency as compared to fatal and injury crash frequency, and rear-end crashes. This is likely due to how roughness of the roadway can impact travel speeds. Finally, the results also show that maintenance activities that reduce IRI can have significant safety benefits. This suggests that pavement management decisions should also consider the anticipated safety benefits, in addition to other factors. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMS) are considered to be crucial tools for agencies in managing their pavement networks and using accessible budgets in an appropriate technique. In this paper, a newly developed condition rating index [modified pavement condition rating (MPCR)] was used to assess the current and future pavement performance. The pavement future performance in terms of MPCR was predicted using two models: (1) a stochastic Markov chain model, and (2) an artificial neural network (ANN) model. In Markov chain modeling, the Long-Term Pavement Performance (LTPP) database was used to develop the transition probability matrix (TPM). An ANN model for future pavement performance prediction was developed based on the same LTPP data. For both models, 245 data points from the LTPP data were used for deterioration modeling. Both models were verified using LTPP data and data obtained from the General Authority of Roads, Bridges and Land Transport (GARBLT), Egypt. The Markov chain and ANN results were compared. The Markov chain model performed better than the ANN model, with a coefficient of determination, R2 , of 0.9 and RMS Error (RMSE) of 0.1268. the ANN model yielded an R2 of only 0.53 and a RMSE of 0.2205. Increasing the number data points did not lead to a significant improvement in the ANN model accuracy. A multiobjective particle swarm optimization (PSO) model is used for fund allocation to maximize the average pavement condition and minimize the maintenance cost. The developed optimization model was tested on two benchmark optimization problems to ensure its validity for real-life applications. The developed optimization model was used on a network of roads and was found to be capable of generating optimal or near-optimal solutions for the maintenance decisions to keep the pavement in good workable condition. As such, the current study presents a comprehensive development tackling different modules of a pavement management system. [ABSTRACT FROM AUTHOR]

Pavement asset management system (PAMS) is a stakeholder and data-driven approach that uses engineering tools to select strategies for rehabilitation and maintenance interventions. However, the impact of maintenance interventions on stakeholders' satisfaction is seldom considered in decision-making. For a given road network, the degree of interest, power, and influence of stakeholders on the suggested maintenance decision vary and hence causes dissatisfaction due to inefficient stakeholder management practices. Thus, the objective of this study was to develop a multi-criteria decision-making approach-based optimization model to select pavement maintenance interventions with an aim to endeavor a balance between PAMS decisions and stakeholders' satisfaction. First, engineering analysis was performed to ascertain the maintenance interventions without stakeholder participation. Second, a systematic stakeholder-centric analysis on pavement maintenance interventions was performed. Finally, an optimization tool that balances engineering analysis and stakeholder satisfaction was developed using the analytic hierarchy process and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. A set of four road sections as a case study in the State of Andhra Pradesh, India, was used to develop and test the model. It is anticipated that the optimization tool could be used to ascertain optimum maintenance intervention that equilibrates stakeholder needs and manages budgetary constraints. Also, this study is envisioned to provide evidence that the proposed methodology will be a rational approach to improve stakeholder participation in the overall PAMS process. [ABSTRACT FROM AUTHOR]

The recycling of scrap tire rubber requires high levels of energy, which poses challenges to its proper valorization. The application of rubber in construction requires significant mechanical and/or chemical treatment of scrap rubber to compatiblize it with the surrounding matrix. These methods are energy-consuming and costly and may lead to environmental concerns associated with chemical leachates. Furthermore, recent methods usually call for single-size rubber particles or a narrow rubber particle size distribution; this, in turn, adds to the pre-processing cost. Here, we used microbial etching (e.g., microbial metabolism) to modify the surface of rubber particles of varying sizes. Specifically, we subjected rubber particles with diameters of 1.18 mm and 0.6 mm to incubation in flask bioreactors containing a mineral medium with thiosulfate and acetate and inoculated them with a microbial culture from waste-activated sludge. The near-stoichiometric oxidation of thiosulfate to sulfate was observed in the bioreactors. Most notably, two of the most potent rubber-degrading bacteria (Gordonia and Nocardia) were found to be significantly enriched in the medium. In the absence of added thiosulfate in the medium, sulfate production, likely from the desulfurization of the rubber, was also observed. Microbial etching increased the surface polarity of rubber particles, enhancing their interactions with bitumen. This was evidenced by an 82% reduction in rubber–bitumen separation when 1.18 mm microbially etched rubber was used. The study outcomes provide supporting evidence for a rubber recycling method that is environmentally friendly and has a low cost, promoting pavement sustainability and resource conservation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Construction project management is typically adopted in modern civil landscapes. Owing to the significant global increase in infrastructure investments, it is crucial to analyze the success and timely completion of such public projects, among which the pavement construction project is a representative example. The aim of this study is to identify the critical success factors (CSFs) that enable the success of pavement construction. Additionally, a CSF framework is developed in this study that can be used by project management teams to monitor and control the progress of pavement construction projects effectively. To the best of our knowledge, this is the first study that acquires all pavement management-related factors to develop a framework. To ensure that all pavement management-related factors were identified and considered, the authors of this study conducted an exhaustive preliminary literature review until 2022. Based on an extensive literature review, 61 multidisciplinary factors are developed and validated in a Delphi study. A questionnaire is developed, and pilot tested through interviews. A total of 27 experts were recruited to participate in two rounds of the Delphi study, which included factors related to operations, contractor expertise, logistics, human resources, communication, finances, and bureaucracy. Reliability tests, correlations, and validations are performed, and weighted averages are obtained. The reliability and accuracy of the factors are successfully defined and tested. The findings of the study suggest focusing on the following functional areas to complete a pavement construction project successfully: (1) financial factors; (2) bureaucracy- and governance-related factors; and (3) communication-related factors. According to the experts interviewed in the Delphi study, these factors contribute the most significantly to the success of the project. This study focused on addressing the research gap in analyzing pavement construction projects from the angle of CSFs. These factors were first developed using literature, validated through a Delphi study of two rounds across panelists. The findings of the Delphi enabled the researchers to identify what factors are more relevant than others when compared to general construction and enabled the research to establish a preliminary framework on a pavement project's lifecycle and stages during the entire project tenure. The findings of this study can help project managers and engineers to uniquely approach a pavement construction project and give it its due importance. From an academic perspective, this study has opened the doors to further testing of the proposed framework and the CSF that it has developed with respect to pavement construction. The outcome of this paper would help pavement construction professionals and researchers in the areas of project planning, resource allocation, risk mitigation, project execution, quality assurance, stakeholder communication, and continuous improvement. [ABSTRACT FROM AUTHOR]

The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model's effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model. [ABSTRACT FROM AUTHOR]

Concrete pavement performance models are evaluated, calibrated, and validated using field data from databases like the Long-Term Pavement Program. Conceptually, performance model predictions should match field data considering a reliability of 50%; that is, there is a 50% probability that the predictions of a certain distress indicator are higher or lower than the field data. However, modern pavements are designed for higher levels of reliability (usually 90% to 95%). Local performance model evaluation for higher levels of reliability requires a high amount of field data that traditional databases lack. Pavement management system (PMS) databases can be a useful resource for high reliability model analysis because of the large amount of data collected locally and regularly. However, when selecting and filtering field databases (of any source), the effect of censored performance data due to rehabilitation, removal from service, or modification of pavement sections is usually ignored. This paper proposes an approach for the use of PMS databases accounting for censored performance data to evaluate the accuracy of performance models' high reliability predictions. The approach is exemplified using a PMS transverse joint faulting database. Results show that by addressing the "survival issue," i.e., accounting for the censored performance data, the resulting PMS-based reliability model improves the faulting model accuracy in matching the field data for high reliability levels. [ABSTRACT FROM AUTHOR]

The prediction of strain responses under axle loadings is critical for flexible pavement design by the mechanistic-empirical approach. The mechanistic-empirical pavement design (pavement ME) method uses linear-elastic analysis to simulate the strain responses under axle loadings, and it relies on the concept of equivalent loading frequency to determine the elastic modulus of the asphalt concrete (AC) layer from the dynamic modulus master curve. The pavement ME method has a simplified procedure to calculate this frequency. The main goal of this study is to evaluate the accuracy of axle loading frequency calculated by the pavement ME method. This paper first introduces the concepts of predominant and equivalent frequencies, provides a brief explanation of the difference between them, and then proposes three predominant frequency methods for evaluation. The accuracy of the pavement ME method and the other methods of calculating the predominant frequency is evaluated in terms of frequency, modulus, and strain by comparing their results with those from dynamic viscoelastic analysis with moving loads. Results show that the time–frequency relationship for predominant frequency is closer to f=1/(2t) than f=1/t , assuming that the pulse duration t is accurate. Nevertheless, using f=1/t with the approximate pulse duration as calculated by the pavement ME method gives reasonable predictions of the maximum tensile strain. On the other hand, while it gives reasonable predictions of vertical strains with increasing depth, the pavement ME method can underestimate them near the surface by up to 55%. Overall, even though the procedure for pavement ME frequency calculation is highly simplified, its general performance appears to be acceptable. The two most typical distresses of flexible pavement are rutting and fatigue cracking, and they are related to two critical strains: vertical strains throughout the layers and horizontal strains at the bottom of the asphalt concrete later of the pavement. The commonly used design approach, the pavement ME method, utilizes the concept of equivalent loading frequency to predict critical strains within the pavement by linear-elastic analysis rather than the full dynamic viscoelastic analysis. This reduces the computational cost significantly. This paper investigates the accuracy of the equivalent loading frequency calculated by the pavement ME method by comparing the frequency values with several other methods, and, finally, comparing the critical strains predicted by the pavement ME method and the results from dynamic viscoelastic analysis, which is considered to be the truth. The results showed that the pavement ME method underestimates vertical strains near the surface of the pavement, which may lead to underestimating rutting (before calibration). However, calibration may be able to remedy the problem. On the other hand, the horizontal strain predicted by the pavement ME method is reasonable, which means the fatigue cracking can be reasonably predicted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model's applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of the maintenance management problem regarding airport pavements is addressed by developing a series of fast computational procedures whose rationales pursue both practical and economical objectives. Particularly, the study involves the design of five new heuristic algorithms replicating different management strategies that can be undertaken by the airport managers in a certain time horizon for reducing the pavement maintenance costs while considering the impact on the service level of the airport. Each algorithm works on the basis of a preliminary computational framework that has a twofold scope: (i) selecting the pavement portions to be maintained during the provided time horizon. (ii) using a K-means method based on three well-known performance indicators, which, namely, are Residual Life (RL), International Roughness Index (IRI) and Pavement Condition Index (PCI), to group the selected portions into homogenous clusters named work-zones. To evaluate the effectiveness and the efficiency of the proposed optimization algorithms in coping with the maintenance programming problem under investigation, an extended design of experiments based on international airport regulations has been arranged. The obtained numerical results revealed that no single strategy can be selected as the most performing in terms of cost and quality conditions of the pavements. To make robust the numerical results, a sensitivity analysis is conducted to evaluate the influence of the total cost of maintenance on runway length and number of sections. However, the results obtained in this study provide a series of managerial implications, further expanding the research contribution. [ABSTRACT FROM AUTHOR]

Decision making in pavement management relies on current road condition and the condition forecast. In this study, it is shown that both the condition forecast as well as the condition measurements are affected by uncertainties as demonstrated in a literature review and in preliminary studies of road condition data. If these uncertainties are to be considered in forecast models, the need for a probabilistic approach is evident. In this study a methodology based on an Extended Kalman filter (EKF) was developed and tested, which allows combining both empirical models and collected condition data for the development of section-based pavement forecast models. The model has been validated to predict the condition state effectively for all selected condition indicators. All relevant steps for the condition forecast have been implemented into a prototype to evaluate the applicability of the methodology using collected data on road networks from Germany, Austria, and Switzerland. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks in pavement management and rehabilitation is to detect and classify different distress types from images collected during field surveys. In this paper, a multilabel convolutional neural network (CNN) model for classifying asphalt distress is proposed. Unlike typical CNN models that classify a single object per image, the proposed model can detect and classify multiple distress types per image, without prior knowledge of the distress location. The model can classify the distress types into four categories: alligator cracking, block cracking, longitudinal/transverse cracking, and pothole. The proposed model was trained and tested on a real data set comprising 42,520 images using different pretrained architectures with various hyperparameter combinations. The results demonstrate the robustness of the proposed model and its potential for crack detection and localization using weakly supervised machine learning methods that can cope with partially labeled data sets. [ABSTRACT FROM AUTHOR]

Top-down cracking (TDC) is recognized as a significant form of distress in asphalt pavements. While the NCHRP project 1-52 introduced a mechanistic-empirical (ME) predictive model for TDC, this model was found to be less accurate in certain traffic conditions—overpredicting crack initiation in sections with medium to low traffic and underpredicting in high-traffic sections. To enhance the precision of TDC predictions, a new fracture-mechanics-based framework has been developed. This improved framework employs a Hot Mix Asphalt (HMA) fracture mechanics methodology, resulting in a marked enhancement in the accuracy of predicting crack initiation times when compared to the original sub-model from NCHRP 1-52. The new framework also incorporates a novel aging model for the asphalt mixture, ensuring more precise inputs for the crack propagation model. The propagation prediction sub-model has been refined by implementing a J-integral-based Paris' law developed in the NCHRP 1-52 project. The efficacy of this updated framework was rigorously tested on eighteen diverse pavement sections across various geographic locations, with well-documented performance history and material properties. The outcomes indicate that the predicted initiation and propagation of TDC closely align with actual field observations, demonstrating the robustness and improved reliability of the new model. [ABSTRACT FROM AUTHOR]

Probabilistic pavement models, with Markov chains as the most widely used type, are considered to capture an accurate representation of the in situ pavement performance. Homogeneous Markov chain models present the same transition probability matrix (TPM) for all the transitions of the period and require data from multiple duty cycles of one or two years. The aim of this paper is to explore the feasibility of developing homogeneous Markov chain models with variations of the duty cycle (in increments of either one or two years). Without considering maintenance and rehabilitation works, this research found that TPMs for a one-year duty cycle can be calculated from the two-year duty cycle, without a noticeable effect on accuracy using International Roughness Index (IRI) values from the Spanish State Road Network. However, for developing coherent TPMs, two primary assumptions were made: (1) heavy vehicle traffic volumes determine the traffic category (TC), and (2) only roads from the same climatic region were modelled. The satisfactory results verified the validity of the methodology and overcame the disadvantages of homogeneous Markov models. Furthermore, the results suggest that pavement sections are adequately designed in Spain for each TC because of the similar deterioration patterns. [ABSTRACT FROM AUTHOR]

Optimal planning of Maintenance and Rehabilitation (M&R) treatments due to limited resources and funding is vital. A pavement performance function formula is needed to model the M&R optimisation problem. By considering important features, a pavement deterioration function was developed with an R2 of 0.897. The essence of pavement deterioration is uncertain. Hence, disregarding the uncertainty may result in sub-optimal solutions. Therefore, two approaches were employed to optimise M&R treatments in a large-scale pavement network, including deterministic and risk-based. Both approaches aimed to minimise the required budget in the planning period constrained to acceptable pavement condition. A novel and robust metaheuristic algorithm was adjusted to solve the M&R planning problem deterministically. In the risk-based method, appropriate probability distribution functions were fitted to historical data of uncertain parameters. Monte Carlo simulation was performed to generate probability distributions of the required budget and condition of pavement sections. By using a risk-based strategy instead of the deterministic one, the required budget was reduced by 30%. Fault tree analysis was performed on the solutions obtained from the risk-based method to increase the pavement network's reliability. The results of the mentioned analysis represented that only by spending 2.5% more budget network's reliability could be significantly increased. [ABSTRACT FROM AUTHOR]

Pavement maintenance is a prominent issue in road traffic systems and plays a fundamental role in ensuring the safety, mobility and comfort of road networks worldwide. This paper introduces an innovative approach for scheduling network-level pavement maintenance based on mathematical planning, guided by the principle of lifecycle cost analysis (LCCA). The primary objective is to optimise pavement performance while minimising maintenance costs, ultimately achieving cost-effectiveness. In particular, the method aims to solve the '2W1H' problem (i.e. 'Which', 'When', and 'How'), which implies identifying the specific road sections, determining an appropriate maintenance timeline, and selecting the most effective maintenance treatment for long-term pavement maintenance planning. The proposed method comprises a bi-level optimisation model. The upper layer is a multi-objective optimisation model which concurrently maximising the lifecycle pavement performance while minimising the lifecycle costs; the lower layer evaluates the impact of pavement maintenance operations on travellers' route choice behaviour by employing the user equilibrium problem to determine the change in user cost attributable to a specific maintenance scheduling scheme. A meta-heuristic algorithm is utilised to solve the model and acquire a Pareto front for multi-objective optimisation. Furthermore, we discuss the practical engineering applications under budgetary constraints and pavement performance requirements, followed by the consideration of parameter uncertainties in pavement performance prediction models. The numerical experiments demonstrate that our approach can deliver pavement maintenance scheduling outcomes across various scales of road networks, and highlight the promising potential of our approach for future pavement management endeavours. [ABSTRACT FROM AUTHOR]

The economy of the nation is significantly influenced by pavement management systems. The use of cutting-edge techniques and technology in various pavement management system applications, as well as research into these topics, are particularly important today. To reduce human errors in data collection, advances in technology and automated surveillance should be used. The most recent methods used in modern equipment for the automatic evaluation of pavement deterioration are reviewed in this paper. To identify trending tools, research gaps, emerging technologies, challenges and limitations of using computer vision for pavement distress and condition assessment, papers collected using UAV (Unmanned Ariel Vehicle) data collection and the application of machine learning methods are investigated. The review comes to the conclusion that the application of machine learning techniques is the general trend in evaluating the condition of pavements, despite some limitations not only in the detection of a small number of pavement distresses with complex patterns but also in the indication of the severity and density of distresses, opening up possibilities for further study. [ABSTRACT FROM AUTHOR]

Developing an accurate pavement prediction model plays a dominant role in pavement M&R optimization. Despite employing different robust machine learning techniques to predict pavement conditions, these methods have some weaknesses in synchronising with exact optimization algorithms. The main contribution of this study is to propose a novel method for optimizing the pavement M&R plan with high accuracy. Contrary to conventional approaches, a robust prediction algorithm, Random Forest Regression (RFR), is applied to predict the pavement International Roughness Index (IRI). In addition, Multiple Linear Regression (MLR) is employed to assess the performance of the proposed technique in terms of IRI prediction accuracy. Whale Optimization Algorithm (WOA), as a powerful metaheuristic optimization algorithm, is utilised to obtain the optimal solution to the pavement M&R optimization problem. RFR is run as an internal part of the WOA in the introduced method. Furthermore, Genetic Algorithm (GA) is used to examine the performance of the proposed approach in finding the optimal solution. The RFR results conclude a more accurate prediction of IRI than MLR based on all machine learning performance indicators. Furthermore, the newly developed hybrid model significantly outperforms GA in finding the optimal and cost-effective solution to the M&R optimization problem. [ABSTRACT FROM AUTHOR]

For better optimization of pavement design and management, accelerated full-scale pavement tests (APT) represent very efficient tools to study pavement behaviour from initial to damaged condition. This paper presents an experimental study where three asphalt pavements with different interface conditions between the asphalt layers were tested in an APT experiment. Asphalt layer strains, FWD deflections, and surface cracking were monitored during the experiment. First, strain signals and pavement surface deflections measured at the beginning of the experiment, were used to characterize the viscoelastic properties of the asphalt layers. Then the measurements were used to analyze the fatigue behaviour of the experimental pavements, and the evolution of their level of damage to traffic. The analysis showed that in such asphalt pavements, asphalt strains, which varied significantly with the increase of load cycles, were not suitable for estimating an 'average' asphalt layer damage. In order to monitor the general pavement behaviour, a new damage ratio was introduced, based on FWD back-calculated moduli of the asphalt layers, that allowed to observe a stiffness increase of the asphalt layers at the beginning of the loading due to aging and post-compaction, and then fatigue damage evolution which had a very good correspondence with surface cracking. [ABSTRACT FROM AUTHOR]

Traditional contact and water-based testing devices exhibit some limitations in evaluating pavement skid resistance. This study demonstrates the viability of deep learning (DL) based non-contact pavement skid resistance evaluation using 3D laser imaging technology. Pavement 3D images and friction numbers were collected from 28 field sites in 10 states in the U.S. to cover pavement sections with various texture and friction characteristics. A total number of 28,000 pairs of 3D images and friction numbers were prepared as the database to train and validate the friction prediction model, named as FrictionNet-V, using the convolutional neural network. The input of FrictionNet-V is the 3D pavement image from the left wheel path, and the output is the corresponding friction number ranging from 0.2 to 0.9. Based on the experimental results on 5,600 testing images, FrictionNet-V achieved better performance than other DL models in terms of speed and accuracy. The result demonstrates the potential of highway speed non-contact pavement skid resistance evaluation using high resolution 3D texture images and DL methods at the network level in the future. [ABSTRACT FROM AUTHOR]

AASHTO's Pavement Mechanistic-Empirical Design (PME) inserts the computed structural responses in the calibrated transfer functions to predict pavement distresses. However, the asphalt layer modulus is not adjusted based on the accumulated fatigue damage over the design period. This limitation negatively impacts the mechanistic part of the design procedure, making the predicted distresses more dependent on the reliability of transfer functions in the empirical part. Besides, computing actual pavement responses assists in forecasting structural deficiencies over the analysis period and planning the optimal treatment scenario through a multi-year prioritisation at the network level. Hence, a standalone application called Enhanced-PME (EPME) was developed in this study which accounts for the deterioration in asphalt layer modulus due to the accumulated fatigue damage and computes the representative pavement responses. EPME extracts traffic data, climate conditions, and material characteristics from the PME while modifying the asphalt layer modulus using a user-defined damage function. Also, a structural deterioration model for predicting effective structural number is developed in this study to demonstrate the application of EPME. The SN prediction model is validated and then implemented in two case studies, highlighting the benefits of utilising structural deterioration models in the Pavement Management System (PMS). [ABSTRACT FROM AUTHOR]

Road agencies are looking for efficient pavement management tools to maintain adequate service levels and preserve the asset value. The traffic speed deflectometer (TSD) allows to continuously assess the structural condition of the pavement while moving at traffic speeds, thus overcoming the typical drawbacks of traditional non-destructive testing equipment. However, managing the huge amount of data deriving from TSD surveys is challenging, and there is no established method to use those data for identifying the remaining service life (RSL) of the pavement network. This study aimed at developing a new practical and reliable methodology to assess the RSL of motorway pavements at the network level from TSD measurements. The methodology is based on the use of the surface curvature index SCI300 and includes the temperature adjustment, the definition of a representative SCI300 value for each pavement section and the estimation of the residual fatigue resistance (considering bottom-up cracking as the critical distress). The methodology was implemented using the deflection data deriving from a TSD survey carried out on about 400 km of motorway pavement. The obtained RSL predictions can be used to identify the structural ranking of different pavement sections and guide budget forecasting, according to the actual maintenance and rehabilitation priorities. [ABSTRACT FROM AUTHOR]

Resilient modulus ($M_R$ M R ) plays the most critical role in the evaluation and design of flexible pavement foundations. $M_R$ M R is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine $M_R$ M R , cyclic triaxial compressive experiments under different confining pressures and deviatoric stresses are needed. However, such experiments are costly and time-consuming. In the present study, an extreme gradient boosting-based ($XGB$ X G B) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation ($PSO$ P S O), social spider optimisation ($SSO$ S S O), sine cosine algorithm ($SCA$ S C A), and multi-verse optimisation ($MVO$ M V O)) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the $M_R$ M R of flexible pavement foundation, but the $PSO-XGB$ P S O − X G B models have the best prediction accuracy considering both training and testing datasets. [ABSTRACT FROM AUTHOR]

The process of pavement deterioration involves uncertainties, and neural networks have been widely used in pavement performance prediction due to their high accuracy. However, the overwhelming majority of current performance prediction models based on neural networks are deterministic. Therefore, this study combined Bayesian theory and neural networks to establish a Bayesian neural network (BNN)-based probabilistic model for predicting pavement deterioration. The proposed model was built on the pavement data in Shanxi Province, China. This study first refined data using the K-Nearest Neighbour and empirical methods, and then selected input features based on correlation coefficient methods. Using the refined data, the deterministic neural network model was established to obtain the prior probability distribution of weights, and then the BNN-based probabilistic model was developed. Compared with the sole neural network model, the BNN-based model not only retains comparable prediction accuracy to the neural network model, but also incorporates uncertainties. The BNN-based model is also theoretically superior to the Markov-based probabilistic model because the former can incorporate all factors and does not need to classify performance values into states. The BNN-based model shall provide more reliable prediction results of pavement deterioration and help engineers make more reasonable maintenance decisions. [ABSTRACT FROM AUTHOR]

Groove deterioration and accumulation of surface contaminants have the negative effect of reducing groove depth and width. Reduced depths and/or widths of grooves in a runway pavement would adversely affect its safety functions. Currently, no mechanistic solutions are available to quantitatively evaluate the effect of groove depth and width on the safety effectiveness of a grooved pavement against skidding and hydroplaning. This study developed a finite-element procedure to provide a mechanistic solution to the dynamic tire-pavement skid resistance problem, and calculate the skid resistance and hydroplaning speed of grooved pavement. Based on the standard FAA square grooves, analyses were presented in this paper to analyze the impacts of groove deterioration on the skid resistance and hydroplaning speed for a B777 aircraft. The analyses offered insights into the variations of skid resistance and hydroplaning speed with groove dimensions, and provided supporting results to the minimum 3 mm groove depth and width recommendation by FAA for runway pavements. The solution offers a useful tool to quantify the reduced safety margins of deteriorated grooves with shallow depths and widths. It can be applied to analyze the safety impacts of changes in the dimensions of any runway pavement groove configuration. [ABSTRACT FROM AUTHOR]

Highways play an important role in economic and social development at both national and regional levels. As a major component of highway infrastructure, pavements require significant funding to be maintained at an acceptable level of service. Allocating the budget for pavement maintenance and rehabilitation (M&R) is a complex decision-making process for a highway agency. This allocation of funding across M&R activities and across districts becomes a challenging process with the presence of multiple stakeholders, constrained budgets, customer satisfaction, agencies' strategic goals, and deteriorating pavement conditions. This paper proposes an extended goal-programming framework for allocating the available pavement M&R budget across multiple management districts of a highway agency over its planning horizon. A case study of applying the proposed model is conducted using data from the Texas Department of Transportation (TxDOT), where five objectives are considered: maximise the proportion of pavement sections with best conditions, minimise the proportion of pavement sections with worst conditions, minimise crash severities through pavement M&R, minimise energy consumption due to M&R activities, and maximise the total lane mile of pavement sections receiving M&R treatments. A sensitivity analysis is also conducted to examine the effect of different parameter values on the results of budget allocation. [ABSTRACT FROM AUTHOR]

Assessing roadway assets condition is the prerequisite of an efficient road management system. It requires participation from the top management, equipment, trained human resources, and dedicated funding. Newfoundland and Labrador have 13,500 lane kilometers of roads, of which almost 7,700 kilometers belong to the local jurisdictions. Local agencies typically consult the Transportation Association of Canada's pavement management guidelines for managing the road networks. But, municipality roads require more specified guidelines considering issues like lack of human resources, equipment, inadequate funding, environmental factors, and public expectations. To better maintain these roads, evaluation of road conditions is the first step. However, a proper evaluation system needs considerable funding, a trained workforce, and necessary equipment. Hence, the idea of using road users' feedback is introduced in this paper. Citizens from 108 municipalities of the province participated in a feedback survey where they were asked questions about roadway assets condition. The survey resulted in a significant amount of data. First, an exploratory analysis of the road users' feedback data was conducted. Then, a simple distress-based pavement performance model was developed. This model can be adopted by the local agencies as a simple decision-making tool. To make the model practical, a smartphone application called MUNPave is also introduced in this paper. [ABSTRACT FROM AUTHOR]

The available skid resistance, or friction, in a pavement surface is a vital parameter for functional evaluations of roads due to its relation with crashes. Therefore, highway administrations must collect friction data on their road network to provide safe roads to users. Additionally, a prediction model that can forecast the available skid resistance in each road segment is necessary for an efficient pavement management system (PMS). The aim of this paper is to develop a skid resistance prediction model for the bituminous pavements of the motorway network of federal state of Bavaria, in Germany, with information that is typically available in a PMS: the Annual Average Daily Traffic , the Annual Average Daily Heavy Traffic , and the number of lanes in each segment. Despite its simplicity, with 6410 road segments of 2 and 3 lanes of the Bavarian motorway network, the model achieves a determination coefficient (R2) of 0.405. If information about the surface layer material is added, R2 increases to 0.480. Consequently, apart from predicting the minimum available friction in each lane in a motorway, the study underlines the necessity that a PMS should contain the recommended elements and additional surface layer material, because the quality of the prediction improves. [ABSTRACT FROM AUTHOR]

This study aims to clarify the specific impact of the void distribution characteristics of the matrix skeleton on the mechanical properties of semi-flexible pavement (SFP) and form a targeted control method for the mechanical properties of SFP based on the gradation design of the matrix skeleton. Therefore, based on industrial CT and 3D reconstruction methods, this study comprehensively analyzed the matrix skeleton's two-dimensional and three-dimensional void distribution characteristics and conducted correlation analysis with the mechanical properties such as compression characteristics, tensile characteristics, dynamic modulus characteristics, and cracking characteristics of SFP. Research has found that the strength and rigidity of SFP are mainly positively correlated with the porosity and void's aperture of the matrix skeleton. The toughness of SFP is mainly positively correlated with the uniformity of void distribution and the three-dimensional void relative surface area index. The three-dimensional void relative surface area index is the most critical indicator affecting the mechanical properties of SFP, followed by the uniformity of void distribution, porosity, and the void's aperture. [ABSTRACT FROM AUTHOR]

Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it. [ABSTRACT FROM AUTHOR]

The advent of connected vehicles (CVs) starts a new era in the field of pavement evaluation. With CVs on roads, the wide fleet of vehicles will constantly transmit pavement data to central servers and also share the necessary information with one another. Such a system presents opportunities for continuous, crowdsourced, and cost-effective monitoring of pavement assets and the development of pavement management databanks. This study reviewed the published studies on CV-based pavement evaluation, summarised the findings, and identified research gaps. In the published resources, 9 different applications have been proposed for CVs in the field of pavement monitoring (some applications are discussed in more than 1 resource): roughness measurement (proposed in 37.5% of resources), distress detection (31%), gathering road weather data (22%), slippery pavement recognition (19%), distress classification (12.5%), road geometry/type evaluation (9%), road amenities detection (9%), skid resistance measurement (6%), and distress intensity measurement (4.5%). The performed case studies were also scrutinised based on utilised sensors, data types, analysis methods, and connectivity messages and networks. In the end nine research subjects with no attention in the previous studies and five research subjects with insufficient attention in the published articles were identified. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet efficient approach for the reliability-based design of flexible pavements, based on the concept of quantile-values. The reliability-based approaches currently adopted in the pavement design guides are simplified techniques, based on an overall reliability factor. On the other hand, simulation-based probabilistic techniques, widely discussed in literature, can be computationally expensive and tedious to implement. The 'Simplified Effective Random Dimension Quantile Value Method' (Simplified ERD-QVM), adopted in this study, presents an elegant technique to bridge the gap between the precision of simulation-based probabilistic techniques and the efficiency of design guide approaches. The accuracy of this technique is verified through Monte Carlo Simulations for different pavement design scenarios. The main advantages of the method are (i) the significant savings in computational times for reliability-based design of flexible pavements without the use of surrogate models (ii) the use of simple techniques already familiar to practitioners who adopt the Mechanistic-Empirical Pavement Design Guide and (iii) it is a comprehensive reliability analysis approach that addresses the drawbacks of the currently used approaches, while capturing the effect of both design parameter and model uncertainty. The methodology for integrating the Simplified ERD-QVM with the existing flexible pavement design codes is also presented. [ABSTRACT FROM AUTHOR]

Automated pavement survey (APS) systems (e.g. Laser Crack Measurement System) are increasingly adopted by transportation planners as a data-enriching and economical alternative to the traditional windshield pavement survey (WPS) systems. Application arises where transportation planners wish to use APS measurements to predict WPS distress scores, as such prediction will enable them to continue using the WPS-based pavement asset management system while using the newly collected APS data. In this paper, we develop a machine learning model to predict the WPS pavement distress scores using APS variables, by employing decision tree classifier based ensemble learning model. The novelty is threefold. First, instead of using the mean we use the distributions of APS variables in the prediction, by examining multiple statistics for every APS variable. Second, these statistics are then used to develop decision tree classifiers as base learners in predicting WPS distress scores. Third, base learners are ensembled using various weight assignment strategies. Our computational test uses the WPS and APS data for two wheel path crack distress from the Kentucky roadway systems, and suggests that conditional probability-based weight assignment provides the best prediction with approximately 79% accuracy. [ABSTRACT FROM AUTHOR]

Decision makers have different attitudes towards risks and opportunities of maintenance and rehabilitation (M&R) strategies. However, most existing pavement management studies simply assumed the neutral attitudes of decision makers. The available risk-based network-level M&R optimisation research equated risk with uncertainty which is actually different. Hence, this study aims to develop a method to quantitatively incorporate decision makers' attitudes towards risk and opportunity into network-level pavement maintenance planning. Quantitative criteria were developed and incorporated into the maintenance optimisation model. A multi-objective optimisation (MOO) model was established to explore the trade-offs between expected returns, risks, and opportunities. The proposed methods were applied to a real-world highway network as a demonstration. The results show that budget increases can simultaneously reduce expected total costs and downside risks and increase upside potential by up to 0.41%, 5.26%, and 0.92%, respectively, for each 1% increase in current year's budget, but their marginal effects are diminishing. Risk reduction requires compromising the expected performance and upside potential of the M&R strategy. The solutions derived from the mean-semivariance model dominate those from the mean-variance model. The outcomes of this study provide decision-makers with ways to incorporate their attitudes into maintenance optimisation, thereby reducing risk exposure and exploiting potential opportunities. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a generic pavement condition assessment scale, called the unified pavement health index (UPHI), that is capable of quantifying asphalt pavement surface quality, which could be altered based on region-specific conditions. The database, consisting of 541 road sections encompassing 1,345,397 data points in the State of Andhra Pradesh, India, was used to formulate the metric. Five severity-based distress indexes were established to estimate the UPHI on a scale of 0 to 100. The metric was categorized into five groups to help inform maintenance interventions. A deep neural network was developed to automatically compute the UPHI for distresses data, which had a coefficient of determination of 96.05%. It is envisioned that the unified metric developed in this study will help practitioners in assessing the pavement functional condition based on 18 distresses and suggest maintenance strategies for network-level management in a rapid and rational manner. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is one of the most used devices for road structural damages detection. However, due to the different roadbed conditions and various disturbances in the nearby environment during detection, there are great difficulties in interpreting detection images, which also hinders automatic detection based on deep learning. In this work, we design a GPR image denoising method based on Cyclegan. We select the most suitable generator and add different attention mechanisms. After denoising the natural GPR road detection image, using the Yolo (You Only Look Once) to test the accuracy of the original image and the denoised image after adding different attention mechanisms. The detection accuracy is improved by 30%. The results of the detection network and the evaluation of the denoised images by GPR image interpreters indicate that the method has the following advantages: lower requirements for training data sets, a wide range of data sources, low cost, good denoising effect, and automatic detection of GPR images. It is of great help to the automatic detection of GPR images. [ABSTRACT FROM AUTHOR]

This study aims to clarify the specific impact of the void distribution characteristics of the matrix skeleton on the mechanical properties of semi-flexible pavement (SFP) and form a targeted control method for the mechanical properties of SFP based on the gradation design of the matrix skeleton. Therefore, based on industrial CT and 3D reconstruction methods, this study comprehensively analyzed the matrix skeleton's two-dimensional and three-dimensional void distribution characteristics and conducted correlation analysis with the mechanical properties such as compression characteristics, tensile characteristics, dynamic modulus characteristics, and cracking characteristics of SFP. Research has found that the strength and rigidity of SFP are mainly positively correlated with the porosity and void's aperture of the matrix skeleton. The toughness of SFP is mainly positively correlated with the uniformity of void distribution and the three-dimensional void relative surface area index. The three-dimensional void relative surface area index is the most critical indicator affecting the mechanical properties of SFP, followed by the uniformity of void distribution, porosity, and the void's aperture. [ABSTRACT FROM AUTHOR]

Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it. [ABSTRACT FROM AUTHOR]

The advent of connected vehicles (CVs) starts a new era in the field of pavement evaluation. With CVs on roads, the wide fleet of vehicles will constantly transmit pavement data to central servers and also share the necessary information with one another. Such a system presents opportunities for continuous, crowdsourced, and cost-effective monitoring of pavement assets and the development of pavement management databanks. This study reviewed the published studies on CV-based pavement evaluation, summarised the findings, and identified research gaps. In the published resources, 9 different applications have been proposed for CVs in the field of pavement monitoring (some applications are discussed in more than 1 resource): roughness measurement (proposed in 37.5% of resources), distress detection (31%), gathering road weather data (22%), slippery pavement recognition (19%), distress classification (12.5%), road geometry/type evaluation (9%), road amenities detection (9%), skid resistance measurement (6%), and distress intensity measurement (4.5%). The performed case studies were also scrutinised based on utilised sensors, data types, analysis methods, and connectivity messages and networks. In the end nine research subjects with no attention in the previous studies and five research subjects with insufficient attention in the published articles were identified. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet efficient approach for the reliability-based design of flexible pavements, based on the concept of quantile-values. The reliability-based approaches currently adopted in the pavement design guides are simplified techniques, based on an overall reliability factor. On the other hand, simulation-based probabilistic techniques, widely discussed in literature, can be computationally expensive and tedious to implement. The 'Simplified Effective Random Dimension Quantile Value Method' (Simplified ERD-QVM), adopted in this study, presents an elegant technique to bridge the gap between the precision of simulation-based probabilistic techniques and the efficiency of design guide approaches. The accuracy of this technique is verified through Monte Carlo Simulations for different pavement design scenarios. The main advantages of the method are (i) the significant savings in computational times for reliability-based design of flexible pavements without the use of surrogate models (ii) the use of simple techniques already familiar to practitioners who adopt the Mechanistic-Empirical Pavement Design Guide and (iii) it is a comprehensive reliability analysis approach that addresses the drawbacks of the currently used approaches, while capturing the effect of both design parameter and model uncertainty. The methodology for integrating the Simplified ERD-QVM with the existing flexible pavement design codes is also presented. [ABSTRACT FROM AUTHOR]

Automated pavement survey (APS) systems (e.g. Laser Crack Measurement System) are increasingly adopted by transportation planners as a data-enriching and economical alternative to the traditional windshield pavement survey (WPS) systems. Application arises where transportation planners wish to use APS measurements to predict WPS distress scores, as such prediction will enable them to continue using the WPS-based pavement asset management system while using the newly collected APS data. In this paper, we develop a machine learning model to predict the WPS pavement distress scores using APS variables, by employing decision tree classifier based ensemble learning model. The novelty is threefold. First, instead of using the mean we use the distributions of APS variables in the prediction, by examining multiple statistics for every APS variable. Second, these statistics are then used to develop decision tree classifiers as base learners in predicting WPS distress scores. Third, base learners are ensembled using various weight assignment strategies. Our computational test uses the WPS and APS data for two wheel path crack distress from the Kentucky roadway systems, and suggests that conditional probability-based weight assignment provides the best prediction with approximately 79% accuracy. [ABSTRACT FROM AUTHOR]