Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm

Naseri, Hamed; Golroo, Amir; Shokoohi, Mohammad; Gandomi, Amir H.

doi:10.1080/15732479.2022.2095407

Cited by 14 publications

(5 citation statements)

References 52 publications

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“…However, these pavements deteriorate over time, and their condition worsens. Therefore, maintenance should be implemented to keep pavements in an appropriate condition ( 46 ). To this end, optimization techniques are generally applied to optimize the pavement maintenance actions in the given planning horizons.…”

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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“…Then, an optimization framework was developed to maximize the prediction accuracy. The hyperparameters of COA were tuned according to the details provided by Naseri et al ( 34 ) to tune the hyperparameters of metaheuristic algorithms. Further, the XGBoost library in Python was used to perform XGBoost, and COA was coded in Python according to Pierezan and Dos Santos Coelho ( 35 ).…”

Section: Methodsmentioning

confidence: 99%

Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

Naseri

Waygood

Wang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver’s license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual’s Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.

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“…The fitting of a 3D quadratic function was adopted to precisely position and measure the feature points obtained in step (1). The pixel gradient amplitude and pixel gradient direction could be calculated as Equations ( 11) and ( 12):…”

Section: The Road Images Splicing Methodsmentioning

confidence: 99%

“…Road maintenance is critical to extend the life of roads and to increase the sustainability of road facilities [1]. A scientific and reasonable road maintenance strategy cannot be achieved without pavement damage detection and assessment.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing

et al. 2023

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In order to investigate the use of unmanned aerial vehicles (UAVs) for future application in road damage detection and to provide a theoretical and technical basis for UAV road damage detection, this paper determined the recommended flight and camera parameters based on the needs of continuous road image capture and pavement disease recognition. Furthermore, to realize automatic route planning and control, continuous photography control, and image stitching and smoothing tasks, a UAV control framework for road damage detection, based on the Dijkstra algorithm, the speeded-up robust features (SURF) algorithm, the random sampling consistency (RANSAC) algorithm, and the gradual in and out weight fusion method, was also proposed in this paper. With the Canny operator, it was verified that the road stitched long image obtained by the UAV control method proposed in this paper is applicable to machine learning pavement disease identification.

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Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm

Cited by 14 publications

References 52 publications

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing

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