Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning

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

doi:10.1080/10298436.2021.1969019

Cited by 22 publications

(7 citation statements)

References 52 publications

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“…In this regard, non-dominated sorting genetic algorithm III (NSGA-III), a multi-objective metaheuristic algorithm, was used as the optimization tool. Genetic algorithms have been widely used to optimize several engineering problems [ 70 , 71 , 72 ]. NSGA-III was used for the optimization process since it is a multi-objective metaheuristic optimization technique, and metaheuristic techniques can sync with machine learning techniques [ 73 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children’s TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children’s TMC and detect its determinants since it can present the relative influence of variables on children’s TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of “hyperparameters”. To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and “Hyperopt”. Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children’s TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children’s TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, “walkability” and “bikeability” of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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“…Naseri et al [48] evaluated several metaheuristic algorithms to solve large-scale pavement network m&r scheduling based on the iri deterioration. A case study with 109 pavement sections indicated that the Water Cycle Algorithm (wca) has a better performance than genetic algorithms (ga), particle swarm optimization (pso), and differential evolutionary (de) methods.…”

Section: Optimization With Hybrid Methodsmentioning

confidence: 99%

Applied Metaheuristic Optimization in Asphalt Pavement Management

Vásquez-Varela

García-Orozco

2021

Cien.Ing.Neogranadina

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Pavement engineering is a crossroads between geotechnical and transportation engineering with a sound base on construction materials. There are multiple applications of optimization algorithms in pavement engineering, emphasizing pavement management for its socioeconomic implications and back-calculation of layer properties for its complexity. A detailed literature review shows that optimization has been a permanent concern in pavement engineering. However, only in the last two decades, the increase in computational power allowed the implementation of metaheuristic optimization techniques with promising results in research and practice. Pavement management requires powerful optimization tools for multi-objective problems such as minimizing costs and maximizing the pavement state from network to project level with constrained budgets. A substantial amount of research focuses on genetic algorithms (GA), but new developments include particle intelligence (PSO, ACO, and ABC). The study must go beyond small-sized networks to improve the management of existing road infrastructure (pavement, bridges) based on mechanistic and reliability criteria.

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“…Naseri et al [77] selected the strongest evolutional and metaheuristic algorithms to solve the M&R planning optimization problem, such as WCA, AOA, DE, ACO, GA, and PSO. After comparing the algorithms, WCA and AOA demonstrated the highest performance.…”

Section: Flexible Pavement Maintenancementioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

Self Cite

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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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Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning

Cited by 22 publications

References 52 publications

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Applied Metaheuristic Optimization in Asphalt Pavement Management

Artificial Neural Networks for Flexible Pavement

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