Pavement Deterioration Process Research Articles

STRUCTURAL ASSESSMENT OF COMPOSITE HIGHWAY BRIDGES CONSIDERING VEHICLE‐BRIDGE DYNAMIC INTERACTION

AbstractOver time, as the bridge deck pavement deterioration process increases, the calculated vertical translational displacements and stresses are higher. In this context, the chances of mechanical failure occurrence increase and may cause limitation in the service life of steel and composite (steel‐concrete) bridges. Thus, the current work presents a methodology for fatigue analysis developed by the authors through the years. The methodology is devoted specifically for the assessment of composite bridges employing the hot‐spot local S‐N approach and a coupled vehicle‐pavement‐bridge system considering progressive pavement deterioration with stochastically generated roughness profiles.

ASENN: attention-based selective embedding neural networks for road distress prediction

This study proposes an innovative neural network framework, ASENN (Attention-based Selective Embedding Neural Network), for the prediction of pavement deterioration. Considering the complexity and uncertainty associated with the pavement deterioration process, two fundamental frameworks, SEL (Selective Embedding Layer) and MDAL (Multi-Dropout Attention Layer), are combined to enhance feature abstraction and prediction accuracy. This approach is significant while analyzing the pavement deterioration process due to the high variability of the contributing deterioration factors. These factors, represented as tabular data, undergo filtering, embedding, and fusion stages in the SEL, to extract crucial features for an effective representation of pavement deterioration. Further, multiple attention-weighted combinations of raw data are obtained through the MDAL. Several SELs and MDALs were combined as basic cells and layered to form an ASENN. The experimental results demonstrate that the proposed model outperforms existing tabular models on four road distress parameter datasets corresponding to cracking, deflection, international roughness index, and rutting. The optimal number of cells was determined using different ablation settings. The results also show that the feature learning capabilities of the ASENN model improved as the number of cells increased; however, owing to the limited combination space of feature fields, extreme depths were not preferred. Furthermore, the ablation investigation demonstrated that MDAL can improve performance, particularly on the cracking dataset. Notably, compared with mainstream transformer models, ASENN requires significantly less storage and achieves faster execution speed.

Deep Learning–Based Pavement Performance Modeling Using Multiple Distress Indicators and Road Work History

The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can help maximize the use of available resources for pavement management agencies through better coordinated preservation and maintenance activities. This paper uses deep neural networks such as the convolutional neural network (CNN) and the long short-term memory (LSTM) to model the pavement deterioration process. In this paper, pavement condition data and maintenance and rehabilitation history collected by the Texas Department of Transportation over the past 18 years were used. Twenty-one flexible pavement condition indicators, including cracking, rutting, raveling, and roughness, collected from more than 100,000 pavement sections were included in the proposed models. Promising preliminary results were obtained. Case study results show that the proposed CNN model outperforms standard machine learning models in predicting pavement condition values.

The feasibility of recycled micro polyethylene terephthalate (PET) replacing natural sand in hot-mix asphalt

Microplastic pollution is contaminating the human food chain worldwide. At the same time, construction excessively exploits natural sand, requiring the incorporation of alternative materials to achieve sustainable development. From this scenario, the study evaluates the potential of recycled micro-PET replacing natural sand on hot-mix asphalt (HMA) mechanical behavior by weight and volume. The materials’ characterization occurred via XDR, XRF, TGA/DTG, FTIR, SEM, and AFM tests. The Superpave mix design, X-ray micro-tomography (μ-CT), indirect tensile strength (ITS), resilient modulus (RM), and moisture damage tests were performed in conventional and PET-incorporated mixtures, considering 2% (in wt.), 4% (in wt.), and 8% (in wt. and vol.) substitutions. The 8% PET replacing sand by volume showed no significant change in the mixture’s optimum asphalt content (OAC) due to the binder absorption equivalence between the materials’ surfaces. For the same binder content, the increase in PET content in weight decreased the binder film thickness and increased the volume of voids, visualized as non-permeable micropores in μ-CT. Although adding and incrementing PET reduced the mixtures’ ITS and RM values, its most significant contribution was enhancing the resistance to moisture damage, especially for regions where the pavement deterioration process is associated with an intensive rainfall regime, thus demonstrating this application’s practical feasibility in concrete asphalt paving.

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration

Various studies have been recently conducted to predict pavement condition, but most of them were developed in a certain region where climate conditions were kept constant and/or the research focused on specific road distresses using single parameters. Thus, this research aimed at determining the influence of pavement structure, traffic demand, and climate factors on urban flexible pavement condition over time. To do this, the Structural Number was used as an indicator of the pavement capacity, various traffic and climate variables were defined, and the Pavement Condition Index was used as a surrogate measure of pavement condition. The analysis was focused on the calibration of regression models by using the K-Fold Cross Validation technique. As a result, for a given pavement age, pavement condition worsens as the Equivalent Single Axle Load and the Annual Average Height of Snow increased. Likewise, a cold Annual Average Temperature (5–15 °C) and a large Annual Average Range of Temperature (20–30 °C) encourage a more aggressive pavement deterioration process. By contrast, warm climates with low temperature variations, which are associated with low precipitation, lead to a longer pavement service life. Additionally, a new classification of climate zones was proposed on the basis of the weather influence on pavement deterioration.

Effect of Speed Bumps on Pavement Condition

Speed bumps are efficient traffic calming devices that allow transport authorities to control vehicle speeds and improve safety in specific locations. Though frequently used in residential areas, they have become increasing prevalent in other higher volume roadways (particularly in developing countries), because of their effectiveness, low-cost of implementation, and easy installation process. However, in these countries there is no proper technical guidance for speed bump design and implementation, which is why one can often find these devices placed on an arterial highway or on roads where the proportion of trucks is high. The most important consequence of placing a speed bump on a road with large numbers of trucks is that the pavement deterioration process will accelerate as a result of the induced vibrations and bouncing of a truck’s suspension. In this paper the relationship between the bump geometry—length and height—and the dynamic load transmitted to the pavement is studied. A full truck model has been used to simulate the vehicle dynamics while passing over the bump, to estimate the demand imposed over the pavement. Damage was calculated for each simulation scenario using a combination of linear-elastic analysis and empirical damage functions. The geometry that leads to less damage is the circular, with a minimum length of two meters and a maximum height of ten centimeters.

Development of network-level pavement deterioration curves using the linear empirical Bayes approach

ABSTRACT Modelling the pavement deterioration process is essential for a successful pavement management system (PMS). The pavement deterioration process is highly influenced by uncertainties related to data acquisition and condition assessment. This paper presents a novel approach for predicting a pavement deterioration index. The model builds on a negative binomial (NB) regression used to predict pavement deterioration as a function of the pavement age. Network-level pavement condition models were developed for interstate, primary, and secondary pavement road families and were compared with traditional non-linear regression models. The linear empirical Bayesian (LEB) approach was then used to improve the predictions by combining the deterioration estimated by the fitted model and the observed/measured condition recorded in the PMS. The proposed approach can improve the mean square error prediction of the next-year pavement condition by 33%, 36% and 41% for Interstate, Primary, and Secondary roads, respectively, compared with the measured pavement condition without further modelling of the pavement deterioration.

Distress-based evidential reasoning method for pavement infrastructure condition assessment and rating

ABSTRACT Pavement condition rating is an essential step in pavement management, as it provides the basis for maintenance decisions and budget allocations. The pavement deterioration process is stochastic in nature because several types of distress usually affect performance of a pavement structure. Hence, the deterministic number-based condition indices used to rate pavement condition are not sufficient. This paper introduces the evidential reasoning (ER) theory to the area of pavement condition assessment and discusses a distress-based condition assessment method using an evidential reasoning approach. The proposed pavement assessment method utilises a condition matrix to assess the basic probability of pavement condition based on extent and severity of different distresses identified in a pavement structure. The Dempster–Shafer theory of evidence is used to aggregate evidence into an overall ‘belief value’ reflecting the probable condition assessment of the pavement structure. The proposed ER method holds specific advantages over other available condition assessment methods and has potential to enhance the condition rating process. A case study is used to illustrate the proposed method and to validate its results. Based on the obtained results, the proposed method facilitates eliminating inconsistency in subjective judgment processes and produces a robust pavement condition rating.

Metoda wykrywania niestabilności i przewidywania zużycia w procesach toczenia promieniowego w oparciu o dane statystyczne

Pavement maintenance management poses a significant challenge for highway agencies in terms of pavement deterioration over time and limited financial resources to keep the road condition at an acceptable level. In this paper two probabilistic maintenance models are proposed and compared for pavement deterioration and maintenance processes to evaluate different maintenance strategies. Firstly, the states of pavement condition are defined using the features of different pavement maintenance works, instead of using the traditional method of cumulative service index rating. Secondly, a Markovian model is presented to describe the pavement deterioration and maintenance process with some constraints on the number of interventions, the effect of interventions and etc. But for the complex scenarios, such as non-Markovian deterioration, dependencies between the different types of interventions and the usage of emergency maintenance for roads when the required budget for maintenance is unavailable, a simulation-based Petri-net model is built up to investigate the whole life-cycle evolution. Two examples are used to illustrate and compare the proposed models to demonstrate the merits and disadvantages of each model and its applicable conditions.

Optimization of Pavement Inspection Schedule with Traffic Demand Prediction

In order to improve the current pavement inspection systems, many studies have attempted to produce a flexible inspection schedule by minimizing the lifecycle cost of pavement management system. However, these methods do not incorporate the risk of untimely inspection nor adopt the state-of-art intelligent transportation resources, such as traffic data from various infrastructures, flow prediction models with high accuracy, and empirical-mechanistic models for pavement deterioration process. Therefore, this paper proposes a framework to optimize a flexible inspection schedule within a risk boundary defined by pavement state prediction with traffic flow data and a more mechanistic deterioration model. The results validate the outperformance of the optimized inspection over two conventional inspection schemes – 1-year and 2-year regular inspection. Also the optimized inspection is comparably more robust than the regular inspections with different traffic scenarios due to the uncertainty risk taken by regular inspections.

Stochastic Forecasting of Life Expectancies Considering Multi-Maintenance Criteria and Localized Uncertainties in the Pavement-Deterioration Process

Abstract Understanding the deterioration characteristics of highway pavements is an essential factor for developing better maintenance strategies, as well as estimating mid- to long-term budget demands. In general, estimation results regarding life expectancy have often been represented with a constant in deterministic manners. However, in reality, these results do not correspond with real life spans because of the severe uncertainties in the pavement-deterioration process and various maintenance criteria. For these reasons, in this paper, the real life expectancy was assessed in a stochastic manner with the Bayesian Markov hazard model, which is suitable for estimating localized uncertainties in the progress of deterioration based on various deterioration indices. On the basis of this model, we have developed a new methodology that can determine the joint distribution of total maintenance demands by aggregating multiple deterioration processes using the concept of “safety-hazard zones.” In addition, we developed a method for disaggregating the joint distribution by maintenance types to mitigate the conventional problems associated with the probabilistic budget estimation approach. For our empirical study, we used data from the Korean national highways from 2007 to 2011 concerning pavement conditions, including crack, rut depth, and international roughness index (IRI). The results of this study are meaningful in that they establish the ideal conditions required for the Bayesian Markov hazard model to forecast the deterioration of pavement, allowing the incorporation of a practical viewpoint into an academic approach under the concept of asset management.

Corroboration of Modelling Input data for AC Pavements

The earliest study of asphaltic concrete pavement performance indicated that the deterioration process is a non-linear function of distresses that affect the AC fabric.Since AC pavements are non-homogeneous composites which are designed and often constructed to a number of different standards and requirements, the modelling of their progressive deterioration and future performance is a rather complex issue.There are a great number of pavement performance models used by road managing authorities, but do these models meet their expectations? To answer this question, a study was recently conducted into the maintenance practices adopted by local road authorities.In this study, prime consideration was given to the types of models that were adopted by these authorities and the quality of the output data generated by these models. Consequently, it was important to find out the quality of input data adopted for the older pavements which had some maintenance treatments applied and whether the model outcomes were satisfactory?The outcome of this study instigates the fact that the pavement modelling system does not perform satisfactorily on all levels of modelling and particularly on pavements that have very limited data in terms of structural capacities. Evaluation of pavement condition is carried out at certain intervals, usually between three to five years, and often the predicted model output does not match the actual road pavement conditions. In order to synchronise such a match, management uses some artificial ranking as they have no other means of measuring pavement structural strength and capacity. The available non-destructive testing methods are time consuming and often financially unaffordable for the whole road network. Destructive testing is not recommended and as it is also time consuming and costly. Therefore, they greatly rely on the old methods of pavement surface evaluations based on a visual assessment of the surface conditions. However, this type of evaluation is very restrictive in terms of accuracy and defects classification as well as establishing meaningful performance values that could be confidently used as modelling input data. In addition, this method does not provide any specific comparison between different pavements and this ultimately makes modelling limited.The aim of this publication is to establish a feasible evaluation method of a pavement deterioration process that would provide realistic numerical values relating to pavement strength and capacity. Consequently, this could be used as modelling input data.The ultimate solution to the existing problem would be the creation of a simple, easily accessible and inexpensive engineering instrument that would enable the measurement of pavement strength in-situ conditions.

Modelo de previsão do índice de condição dos pavimentos flexíveis

Resumo Este trabalho apresenta o desenvolvimento de um modelo de previsão do índice de condição dos pavimentos flexíveis para a malha rodoviária, em concreto asfáltico usinado a quente, do Estado da Bahia. Teve como ponto de partida um planejamento fatorial, elaborado com base em informações de um banco de dados de rodovias, fornecido pelo Departamento de Infraestrutura de Transportes da Bahia. Para a verificação da adequação do modelo utilizou-se a reta de ajuste dos dados, o coeficiente de correlação (r), a análise de resíduos e o gráfico de probabilidade normal dos resíduos. O modelo correlaciona o desempenho do índice de condição dos pavimentos flexíveis às variáveis idade, tráfego e pluviometria, fatores que tem influência no processo de deterioração dos pavimentos. O modelo desenvolvido apresentou ajuste aos dados observados e r = 0,64, mostrando indícios da validade da sua aplicação, para as características da malha rodoviária do Estado da Bahia. Não existem modelos de desempenho universais e modelos que foram desenvolvidos a partir de dados observados em estudos realizados em locais específicos não são necessariamente transferíveis, e possuem algumas limitações inerentes.

Use of exponential hidden Markov models for modelling pavement deterioration

In this paper, the potential of using an exponential hidden Markov model to model an indicator of pavement condition as a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the values of the pavement condition indices can be adequately described using discrete condition states and modelled as a Markov process. It is also assumed that the values of the indices can be measured over time and represented continuously using exponential distributions. The potential advantage of using such a model is illustrated using a real-world example.

Development of Maintenance and Rehabilitation Program

Over the past decade, there has been a movement in North America toward a performance-based contract (PBC) model for maintaining and managing road networks. In traditional method-based contracts, the owner agency specifies techniques, materials, methods, and quantities, along with the time period for the contract. By contrast, in a PBC, the client agency specifies certain clearly defined minimum performance measures to be met or exceeded during the contract period. PBC is a type of contract in which payments are explicitly linked to the contractor's successfully meeting or exceeding certain clearly defined minimum performance indicators. Therefore, the selection of a PBC model for maintenance and rehabilitation differs significantly from that of a traditional asset management contract. Also, a PBC is more complex because of the pavement deterioration process and probability of failure to achieve the specified level of service for various performance measures along the contract period. A novel framework was developed for the selection of maintenance and rehabilitation activities with a model for pavement performance prediction and linear optimization. A case study based on data from the second generation pavement management system of the Ministry of Transportation Ontario is used to illustrate the framework.

통계기법을 활용한 포장파손자료의 잠재오차 보정

성공적인 자산관리시스템의 도입은 풍부하고도 신뢰할 수 있는 데이터와 함께 시작된다. 그러나 현실에서는 알려지지 않은 다양한 원인에서 비롯되는 관측 오차들은 항상 존재하기 마련이다. 이는 관리자의 유지보수활동을 방해하며 파손과정이나 생애주기비용 예측결과의 신뢰성에도 악영향을 미친다. 무엇보다도 관리자가 자신의 손으로 직접 조사한 자료를 믿지 못하는 모순된 결과를 낳는다. 이런 현상은 도로포장관리분야에서 특히나 심각하다. 도로관리자들은 이러한 사실에 대해 충분히 인식하고 있으나, 현실적으로 오차 없는 조사를 수행한다는 것은 하나의 큰 도전이라고도 할 수 있다. 이러한 점들을 고려하여 본 연구에서는 포장표면 조사자료에 포함된 오류 및 잠재오차를 보정하기 위한 통계적 방법론을 제시하고자 하였다. 방법론으로는 오차의 구조 및 신뢰도를 고려할 수 있도록 확률분포이론에 근거한 두 가지 기법(샘플기준법과 분포기준법)을 제안하였다. 제안된 방법론은 국도에서 수집된 포장상태자료들을 활용하여 적용 가능성을 살펴보았으며, 그 결과 눈에 보이는 오류들만을 제거하는 일반적인 방법론으로는 포장자료에 포함된 불확실성을 충분히 고려할 수 없음을 확인하였다. 제안된 방법론은 도로자산관리에 필요한 다양한 분석의 신뢰성을 개선하는데 유용하게 활용될 수 있을 것이다. Successful implementation of infrastructure asset management system can be started with rich and reliable data. However, measurement errors in the data have always existed in the real world caused for many unknown reasons. It disturbs maintenance activities of agencies, and makes negative effects to reliability of research results on forecasting deterioration process and life cycle cost. Above all, it makes a contradiction that road agencies cannot believe their inspection data surveyed by their hands. It is particularly serious in the road pavement management field. Although road agencies are well recognized the fact, inspecting without measurement error would be a great challenge. Considering the facts, this paper aimed to suggest statistical error processing methods to correct latent error included in pavement surface inspection data. As alternatives, this paper suggested two methods based on probability distribution to consider structure of error and reliability of the data. The suggested methods were empirically tested by using pavement inspection data from Korean National Highway. As the result, this paper confirmed that conventional error processing that just removes only visible errors is not enough to cover uncertainty in pavement deterioration process. The suggested methods would be useful for improving reliability of analysis results required for road infrastructure asset management.

Modeling the Road Degradation Process: Non-linear Mixed Effects Models for Correlation and Heteroscedasticity of Pavement Longitudinal Data

Pavement deterioration models are important inputs for the pavement management systems. These models are based on the study of performance data, and they provide the evolution law of pavement deterioration.In order to characterize the pavement deterioration process, several statistical methods have been developed at the French institute of science and technology for transport, development and networks (Ifsttar). This paper presents a nonlinear mixed-effects model accounting for the correlation between repeated observations on the same pavement section. Based on this nonlinear mixed-effects modeling, we investigate and test climatic factor that could explain differences in the parameters between pavement sections.

Forecasting pavement remaining service life with limited causal data

Assessment of pavement remaining service life assists decision making on pavement maintenance and rehabilitation (M&R) such that proper M&R actions can be selected and scheduled to optimize the use of resources over the life cycles of pavements. In this paper, a pavement remaining service life model was developed dealing with the limited causal data present in the pavement management system databases. The model achieves this by including the current pavement condition rating in the model specification and considering the boundary conditions of the pavement deterioration process. Empirical results of model estimation and verification are presented in the context of the Florida pavement condition data sets.

Nonlinear Mixed-Effects Model for the Evaluation and Prediction of Pavement Deterioration

Pavement deterioration models are important inputs for pavement management systems (PMS). These models are based on the study of performance data, and they provide the evolution law of pavement deterioration. Performance data consist of observations of pavement section conditions, and are collected through several follow-up campaigns on road networks. To characterize the pavement deterioration process, several statistical methods have been developed at the Laboratoire Central des Ponts et Chaussees (LCPC). However, these methods are suboptimal for modeling the evolution of pavement deterioration, as they ignore unit-specific random effects and potential correlation among repeated measurements. This paper presents a nonlinear mixed-effects model enabling accounting for the correlation between observations on the same pavement section. On the basis of this nonlinear mixed-effects modeling, we investigate and identify structural and climatic factors that explain differences in the parameters between pavement...