An executive model for network-level pavement maintenance and rehabilitation planning based on linear integer programming

Ameri, Mahmoud; Jarrahi, Armin

doi:10.3934/jimo.2018179

Cited by 4 publications

(2 citation statements)

References 23 publications

(35 reference statements)

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“…This algorithm could effectively resolve the maintenance and rehabilitation issues associated with flexible pavements. Ameri and Jarrahi [21] used condition indicators in the form of normalized values and developed technical constraints in a linear integer programming model to improve network-level pavement maintenance and rehabilitation planning. Chen et al [22] proposed a network-level pavement maintenance and rehabilitation optimization model considering the costs of user travel time and vehicle fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

Pavement Maintenance Decision Making Based on Optimization Models

et al. 2021

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Pavement maintenance prioritization considering both quality and cost is an important decision-making problem. In this paper, the actual pavement condition index of city roads was calculated using municipal patrol data. A linear optimization model that maximized maintenance quality with limited maintenance costs and a multi-objective optimization model that maximized maintenance quality while minimizing maintenance costs were developed based on the pavement condition index. These models were subsequently employed in making decisions for actual pavement maintenance using sequential quadratic programming and a genetic algorithm. The results showed that the proposed decision-making models could effectively address actual pavement maintenance issues. Additionally, the results of the single-objective linear optimization model verified that the multiobjective optimization model was accurate. Thus, they could provide optimal pavement maintenance schemes for roads according to actual pavement conditions. The reliability of the models was investigated by analyzing their assumptions and validating their optimization results. Furthermore, their applicability in pavement operation-related decision making and preventive maintenance for roads of different grades was confirmed.

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Section: Introductionmentioning

confidence: 99%

Pavement Maintenance Decision Making Based on Optimization Models

et al. 2021

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“…The importance of maintenance planning work is to choose the appropriate time and method to reduce the rate of deterioration of pavement sections according to the available budget. To reach this goal, it is required to develop models that help improve the development process and take into account the factors affecting the maintenance decision making [5].…”

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confidence: 99%

Evaluation of Pavement Condition Deterioration Using Artificial Intelligence Models

Elshamy

Tiraturyan

Uglova

et al. 2022

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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Introduction. One of the most significant tasks facing road experts is to maintain the transport network in good condition. The process of selecting an appropriate approach to providing such condition is quite complex since it requires considering many parameters, such as the existing condition of the pavement, road category, weather conditions, traffic volume, etc. Recently, the rising trend of digitization in the industry has contributed to the use of artificial intelligence to address problems in several fields, including the bodies in charge of operational control over the status of roadways. Within the context of any control system, the main task of the control system is to carry out reliable forecasting of the operational state of the road in the medium and long term.Materials and Methods. This study investigated the possibility of using artificial neural networks to assess existing pavement characteristics and their potential application in developing road maintenance strategies. A back-propagation neural network was implemented, trained using data from 1,614 investigated sections of the M4 «DON» highway in the road network of the Russian Federation in the period from 2014 to 2018. Several models were developed and trained using the MATLAB application, each with a different number of neurons in the hidden layers.Results. The results of the models showed a convergence between the inferred paving state values and the actual values, as the multiple correlation coefficient (R2) values exceeded 92 % for most of the models during all learning stages.Discussion and Conclusions. The findings suggest that public road authorities may utilize the established models to choose the best road maintenance strategy and assign the most efficient steps to restore road bearing capacity and operation.

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