A review on empirical methods of pavement performance modeling

Hu, Aihui; Bai, Qiang; Chen, Lin; Meng, Siyuan; Li, Qihui; Xu, Zhiman

doi:10.1016/j.conbuildmat.2022.127968

Cited by 27 publications

(9 citation statements)

References 150 publications

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“…Pavement surface continues to deteriorate over time unless suitable rehabilitation is provided due to traffic loading, daily and seasonal climate fluctuation, and other factors. Therefore, in order to preserve the pavement, periodic monitoring of the status of the pavement is required, and this monitoring must be followed by rapid treatment [8]. Several transportation bodies have created rules and policies to preserve the road network.…”

Section: Literature Reviewmentioning

confidence: 99%

Pavement Condition Assessment Using Pavement Condition Index and Multi-Criteria Decision-Making Model

Elmansouri¹,

Alossta²,

Badi³

2022

MITS

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Road maintenance is essential to the growth of the transportation infrastructure and, thereby, has a big impact on a nation's overall economic stability and prosperity. It is impossible to simultaneously monitor and maintain the entire network. As a result, transportation authorities are eager to develop scientific foundations for assessing the importance of maintenance tasks within the network of roads. Hence, pavement assessment methods are needed to establish the priorities and achieving the most convenient level of service. In this study, a road stretch was assessed using the sixteen criteria in the Distress Identification Manual for pavement defects, using pavement condition index (PCI) and multi-criteria decision-making models (MCDM). The two methods were compared to determine the possibility of using MCDM. The study came to the conclusion that MCDM is reliable in assessing pavement performance because both methods indicated that the road pavement is deteriorating.

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Section: Literature Reviewmentioning

confidence: 99%

Pavement Condition Assessment Using Pavement Condition Index and Multi-Criteria Decision-Making Model

Elmansouri¹,

Alossta²,

Badi³

2022

MITS

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“…Generally, pavement performance deterioration models can be divided into three basic types: mechanistic models, empirical models, and mechanistic-empirical models [1]. Taking the Taxiway of Beijing Capital International Airport as an example, Ma et al [2] designed the sensor layout to obtain the mechanical response of the pavement, and the multilayer system theory was selected as the mechanistic model of the airport asphalt pavement structure, and as a result, the real-time deterministic calculation of pavement damage at any time in the short term was realized.…”

Section: Introductionmentioning

confidence: 99%

“…Empirical methods can rapidly evaluate pavement performance and develop management plans. Empirical models consist of deterministic models, probabilistic models, and machine learning models [1]. Based on the deterministic system theory, Pérez Acebo et al [8] developed an IRI performance model for two lane road with flexible pavement, and the test showed that the multiple linear regression model had the best performance.…”

Section: Introductionmentioning

confidence: 99%

Pavement performance deterioration model based on deep neural networks

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The development of transportation has shifted from construction to the state of maintenance, and reasonable and efficient pavement performance prediction models play an important role in developing reasonable pavement maintenance strategies, ensuring the efficient operation of pavement and extending the service life. At present, the research on pavement performance prediction mostly focuses on a non-comprehensive pavement performance evaluation indicator, and when the influence factors increase, the predictive ability will also be affected. Therefore, based on deep neural networks, OPI is selected as the pavement performance evaluation index, and the pavement service time, traffic load, pavement asphalt rutting condition and pavement asphalt cracking condition are selected as the influence factors. Next, the pavement performance deterioration model is established, and the established model is compared with the traditional regression model. After comparison, it is found that the prediction accuracy of the deterioration model reaches 83.25%, which is 17.7% higher than the traditional regression model. Besides, the prediction accuracy of the vast majority of test sets and train sets exceeds 80%. According to the research in this paper, the pavement performance deterioration model based on deep neural networks can better simulate the pavement performance deterioration process, and can still predict pavement performance well under multiple influence factors.

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“…Various performance prediction models have been reported in the literature; the majority of them fall into two categories: deterministic models and probabilistic models (George et al , 1989). Different approaches were used over the years for evaluating pavement performance, including regression analysis (Pan et al , 2011; Kim and Kim, 2006; Mills et al , 2012), artificial neural networks (ANN) (Yang et al , 2003; Roberts and Attoh-Okine, 1998; Vyas et al , 2022), Bayesian belief networks (BBN) (Pantuso et al , 2021; Xiao et al , 2022), Markov chain models (Kobayashi et al , 2010; Kobayashi et al , 2012; Salman and Gursoy, 2022), decision trees (Piryonesi and El-Diraby, 2020) and others (Hu et al , 2022). According to Osorio-Lird et al (2018), the advantages of probabilistic models, specifically Bayesian approaches and Markov chain models over regression and ANN methods in predicting the pavement performance are their capability to capture uncertainty and employment of a transition probability matrix (TPM) for predicting future behavior based on the current state.…”

Section: Introductionmentioning

confidence: 99%

Time-series forecasting of road distress parameters using dynamic Bayesian belief networks

Philip,

AlJassmi

2023

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Purpose To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters. Design/methodology/approach While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors. Findings The four developed performance prediction models achieved an overall precision and reliability rate of over 80%. Originality/value The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

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A review on empirical methods of pavement performance modeling

Cited by 27 publications

References 150 publications

Pavement Condition Assessment Using Pavement Condition Index and Multi-Criteria Decision-Making Model

Pavement Condition Assessment Using Pavement Condition Index and Multi-Criteria Decision-Making Model

Pavement performance deterioration model based on deep neural networks

Time-series forecasting of road distress parameters using dynamic Bayesian belief networks

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