Tunnel condition assessment via cloud model‐based random forests and self‐training approach-Reference-Cited by-同舟云学术

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Tunnel condition assessment via cloud model‐based random forests and self‐training approach

Published:2020-08-11 Issue:2 Volume:36 Page:164-179
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer‐Aided Civil and Infrastructure Engineering

Author:

Zhu Mengqi¹,Zhu Hehua¹,Guo Feng²,Chen Xueqin³,Ju J. Woody⁴

Affiliation:

1. Department of Geotechnical Engineering Tongji University Shanghai P. R. China

2. Department of Geosciences Virginia Tech Blacksburg VA USA

3. Department of Civil Engineering Nanjing University of Science and Technology Nanjing P. R. China

4. Department of Civil and Environmental Engineering University of California Los Angeles CA USA

Funder

China Scholarship Council

Publisher

Wiley

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12601

Reference54 articles.

1. Neural Networks in Civil Engineering: 1989–2000

2. A big data analytics strategy for scalable urban infrastructure condition assessment using semi-supervised multi-transform self-training

3. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

4. The pavement serviceability‐performance concept;Carey W.;Highway Research Board Bulletin,1960

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