Innovative method for recognizing subgrade defects based on a convolutional neural network-Reference-Cited by-同舟云学术

Innovative method for recognizing subgrade defects based on a convolutional neural network

Published:2018-04 Issue: Volume:169 Page:69-82
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Tong Zheng^ORCID,Gao Jie,Zhang Haitao

Funder

Key Laboratory of Roads at Chang’an University

Northeast Forestry University

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference40 articles.

1. Numerical modelling of anti-frost heave measures of high-speed railway subgrade in cold regions;Tai;Cold Regions Sci. Technol.,2017

2. Ahmed Mancy Mosa, Amer Hasan Taher, Layth A. Al-Jaberi. Improvement of poor subgrade soils using cement kiln dust, Case Stud. Constr. Mater. (Available online 23 June 2017).

3. Wallace Wai-Lok Lai, Xavier Dérobert, Peter Annan. A review of ground penetrating radar application in civil engineering: a 30-year journey from locating and testing to imaging and diagnosis. NDT & E Int. (Available online 23 May 2017).

4. Development of regularization methods on simulated ground-penetrating radar signals to predict thin asphalt overlay thickness;Zhao;Signal Process.,2017

5. A semi-automatic processing and visualisation tool for ground-penetrating radar pavement thickness data;Varela-González;Autom. Constr.,2014

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