A methodology for scheduling within‐day roadway work zones using deep neural networks and active learning-Reference-Cited by-同舟云学术

A methodology for scheduling within‐day roadway work zones using deep neural networks and active learning

Published:2022-09-20 Issue: Volume: Page:
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer aided Civil Eng

Author:

Saneii Mostafa¹,Kazemeini Ali²,Seilabi Sania Esmaeilzadeh³,Miralinaghi Mohammad⁴,Labi Samuel³

Affiliation:

1. Department of Civil Engineering Sharif University of Technology Tehran Iran

2. Department of Civil Engineering University of British Columbia Vancouver Canada

3. Lyles School of Civil Engineering Purdue University West Lafayette Indiana USA

4. Department of Civil, Architectural, and Environmental Engineering Illinois Institute of Technology Chicago Illinois USA

Funder

U.S. Department of Transportation

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12921

Reference102 articles.

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4. Construction Scheduling, Cost Optimization and Management

5. Cross‐comparison of convergence algorithms to solve trip‐based dynamic traffic assignment problems;Ameli M.;Comput. Civ. Infrastruct. Eng.,2020

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