Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: Optimal sensor placement and identification algorithm

Ma, Xianyong; Zhang, Dong; Quan, Weiwen; Dong, Yongkang; Tan, Yiqiu

doi:10.1016/j.ymssp.2022.109930

Cited by 58 publications

(3 citation statements)

References 42 publications

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“…Validations of results have a very important role in scientific works (Liu et al., 2020; Ma et al., 2023; Wu et al., 2020; Xiong et al., 2022; Yang et al., 2021). It is very common to use the ROC curve to validate the results of studies that investigate flooding susceptibility (Costache, Pham, et al., 2020; Dano et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest‐PA‐WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest‐PA‐WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data‐scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

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Section: Methodsmentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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“…P3 is a convex optimization problem, so it can be solved by Lagrangian multiplier method as well as gradient method [73,74]. After substituting the obtained solutions θ and f into θ…”

Section: First Introduce Three Auxiliary Variables μmentioning

confidence: 99%

An Efficient Algorithm for Resource Allocation in Mobile Edge Computing Based on Convex Optimization and Karush–Kuhn–Tucker Method

Wang,

Akhtar,

Al-Zahrani

2023

Complexity

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Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications from vehicle terminals that are computation-intensive and delay-sensitive. For devices with limited resources, it uses edge resources to provide computationally heavy operations while conserving energy. This paper proposes a novel approach for computing offloading in MEC. To effectively optimize the MEC resources, this paper proposes a novel algorithm. First, the joint optimization and service cache decision subproblems were determined from continuous and discrete variables. Then, the near-optimal solution is determined from the subproblems through convex optimization and Karush–Kuhn–Tucker method. Simulation results show that the proposed algorithm has better computational offloading and resource allocation performance as compared to existing algorithms.

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“…One of the major challenges in exploring alternating patterns is that the algorithms used to perform this exploration often get a huge set of alternating patterns as an answer [ 16 , 17 ]. This issue becomes more apparent when a min-support threshold is selected.…”

Section: Introductionmentioning

confidence: 99%