Interpreting Impact Echo Data to Predict Condition Rating of Concrete Bridge Decks: A Machine-Learning Approach-Reference-Cited by-同舟云学术

Interpreting Impact Echo Data to Predict Condition Rating of Concrete Bridge Decks: A Machine-Learning Approach

Published:2021-08 Issue:8 Volume:26 Page:04021044
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Sengupta Agnimitra¹^ORCID,Ilgin Guler S.²^ORCID,Shokouhi Parisa³

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Pennsylvania State Univ., University Park, PA 16802 (corresponding author). ORCID: .

2. Assistant Professor, Dept. of Civil and Environmental Engineering, Pennsylvania State Univ., University Park, PA 16802. ORCID: .

3. Associate Professor, Dept. of Engineering Science and Mechanics, Pennsylvania State Univ., University Park, PA 16802.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29BE.1943-5592.0001744

Reference62 articles.

1. AASHTO. 2017. AASHTO LRFD bridge design specifications, 8th ed. Washington, DC: AASHTO.

2. Aggarwal C. C. A. Hinneburg and D. A. Keim. 2001. “On the surprising behavior of distance metrics in high dimensional spaces.” In Vol. 1973 of Lecture Notes in Computer Science 420–434. Berlin Germany: Springer.

3. Improved concrete bridge deck evaluation using GPR by accounting for signal depth–amplitude effects

4. Building Materials Durability: Semi‐Markov Approach

5. A semi-Markov approach for modelling asset deterioration

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