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    An Improved Two-Stage Bottom-Up Optimization Approach for Pavement Maintenance and Rehabilitation Decision Making 

    Source: Journal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003:;page 04024038-1
    Author(s): Qingwei Zeng; Feng Xiao; Xiaorui Sun; Shunxin Yang; Qixuan Cui
    Publisher: American Society of Civil Engineers
    Abstract: Existing two-stage bottom-up optimization (TSBUO) approaches have a simple combination of optimization paths at the first stage, leading to the possibility of missing the optimal plan at the second stage with complex network ...
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    Optimization-Based Decision-Making Approach for Incorporating the Length Constraint of Preventive Maintenance into Pavement Maintenance and Rehabilitation Planning 

    Source: Journal of Transportation Engineering, Part B: Pavements:;2022:;Volume ( 148 ):;issue: 004:;page 04022050
    Author(s): Feng Xiao; Shunxin Yang; Jianchuan Cheng
    Publisher: ASCE
    Abstract: Most existing pavement maintenance and rehabilitation (M&R) decision-making approaches ignore the length constraint of preventive maintenance (PM). This study proposed a practical optimization-based decision-making ...
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    Data Cleaning Framework for Pavement Maintenance and Rehabilitation Decision-Making in Pavement Management System Based on Artificial Neural Networks 

    Source: Journal of Infrastructure Systems:;2024:;Volume ( 030 ):;issue: 003:;page 04024017-1
    Author(s): Qingwei Zeng; Feng Xiao; Hui Zhang; Shunxin Yang; Qixuan Cui
    Publisher: American Society of Civil Engineers
    Abstract: The quality of data in a pavement management system (PMS) has a direct impact on pavement maintenance and rehabilitation (M&R) decisions and management. However, pavement performance data and M&R action data often ...
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    Informer–SVR: Traffic Volume Prediction Hybrid Model Considering Residual Autoregression Correction 

    Source: Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 004:;page 04025011-1
    Author(s): Chang Xu; Yichen Chen; Qingwei Zeng; Shunxin Yang; Wenbo Zhang; Haoyang Li
    Publisher: American Society of Civil Engineers
    Abstract: Predicting daily traffic volumes is essential for planning freeway infrastructure. Daily traffic volumes on freeways have complex time dependencies and submodal variations. Transformer, a deep learning model based on the ...
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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