A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty

Fani, Amirhossein; Naseri, Hamed; Golroo, Amir; MirHassani, S. A.; Gandomi, Amir H.

doi:10.1080/10298436.2020.1859506

Cited by 17 publications

(3 citation statements)

References 48 publications

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“…This process can be related to the exponential trend of pavement deterioration. That is, the pavement deterioration follows an exponential trend, and IRI is increased exponentially ( 44 ). Further, pavement age, ESAL, and SN are the most influential parameters on IRI, which indicates the importance of traffic and structural conditions on pavement roughness.…”

Section: Resultsmentioning

confidence: 99%

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Naseri

Shokoohi

Jahanbakhsh

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road infrastructures are fundamental parts of peoples’ lives, allowing them to access various destinations and activities. Accordingly, infrastructure should be in an appropriate condition. A pavement maintenance plan should be optimized, and pavement condition should be predicted accurately to obtain optimal pavement maintenance solutions. Therefore, the prediction of pavement conditions with high accuracy has been an immense concern. This study aims to introduce a new approach to accurately predict pavement international roughness index (IRI) over the long term. To this end, all the vital parameters, including initial IRI, pavement age, lane width, traffic loadings, structural characteristics, climatic features, and pavement distresses, are considered. With all the vital parameters, the prediction problem includes 58 variables. Thus, the application of a proper feature-selection technique is vital. To this end, a novel hybrid feature-selection method is introduced by a combination of arithmetic optimization algorithm and stochastic gradient descent regression (AOA-SGDR). Moreover, the performance of the proposed feature-selection method is compared with Lasso and all features. Five machine-learning algorithms, including random forest regression (RFR), support vector machine, multi-layer perceptron, decision-tree regression, and multiple linear regression, are employed for the prediction process. By employing AOA-SGDR, the average testing-data mean absolute error (MAE) reduces by at least 7.92%. Meanwhile, RFR provides the highest accuracy, with average testing-data MAE of 0.095 m/km. Moreover, analyzing the parameters indicates that initial IRI, pavement age, equivalent single axle load (ESAL), and structural number (SN) have the most significant relative influence on IRI.

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Section: Resultsmentioning

confidence: 99%

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Naseri

Shokoohi

Jahanbakhsh

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Fani et al [98] proposed a stochastic model, considering annual budget and pavement deterioration rate as uncertain pavement factors. They found that the complexity of the model increased as the number of network sections and scenarios increased.…”

Section: Flexible Pavement Costmentioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

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Transportation agencies are primarily responsible for building new roads and maintaining current roads. The main focuses of these agencies are to prioritize maintenance and make significant rehabilitation decisions to handle serious problems facing road authorities. Considerable efforts and an abundance of studies have been performed to determine the nature, mechanisms, test methods, and measurement of pavements for preservation and improvements of roadways. The presented study reports a state-of-the-art review on recent advances in the application of artificial intelligence in various steps of flexible pavement, including pavement construction, performance, cost, and maintenance. Herein, the challenges of gathering large amounts of data, parameter optimization, portability, and low-cost data annotating are discussed. According to the findings, it is suggested that greater attention should be paid to integrating multidisciplinary roadway engineering techniques to address existing challenges and opportunities in the future.

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“…Li & Zhang, 2021) (Ta et al, 2020). Ignoring this uncertainty can lead to suboptimal decisions or unreliable solutions (de Jonge et al, 2015) (Fani et al, 2022) (de la Barra et al, 2020). To address this challenge, the concept of distributionally robust optimization (DRO) has emerged as a powerful framework that explicitly considers a range of possible distributions or  Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty (Hengki Tamando Sihotang, et al) uncertainty sets(S. Lu et al, 2020) (Siqin et al, 2022) (Gu et al, 2021)(C. Shang & You, 2018) (Ning & You, 2019) (Miao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty

Sihotang,

Marsoit,

Marsoit

2023

emod

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Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty is a crucial research area that addresses the challenge of making optimal decisions in the presence of distributional ambiguity. This research explores the development of methodologies and algorithms to handle uncertainty in variational inequalities, incorporating a distributionally robust framework that considers a range of possible distributions or uncertainty sets. By minimizing the worst-case expected performance across these distributions, the proposed approaches ensure robustness and optimality in decision-making under uncertainty. The research encompasses theoretical analysis, algorithm development, and empirical evaluations to demonstrate the effectiveness of the proposed methodologies in various domains, such as portfolio optimization and supply chain management. The outcomes of this research contribute to the advancement of robust optimization techniques, enabling decision-makers to make reliable and robust decisions in complex real-world systems

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A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty

Cited by 17 publications

References 48 publications

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Artificial Neural Networks for Flexible Pavement

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty

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