Investigation of Machine Learning Methods for Structural Safety Assessment under Variability in Data: Comparative Studies and New Approaches-Reference-Cited by-同舟云学术

Investigation of Machine Learning Methods for Structural Safety Assessment under Variability in Data: Comparative Studies and New Approaches

Published:2021-12 Issue:6 Volume:35 Page:04021090
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Sarmadi Hassan¹^ORCID

Affiliation:

1. Researcher, Dept. of Civil Engineering, Faculty of Engineering, Ferdowsi Univ. of Mashhad, P.O. Box: 91779-48974, Mashhad, Iran; Head of Research and Development, Ideh Pardazan Etebar Sazeh Fanavar Pooya (IPESFP) Company, 29th Reza St., Reza Blvd., P.O. Box: 91767-68540, Mashhad, Iran. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CF.1943-5509.0001664

Reference37 articles.

1. Comparison of Visual Inspection and Structural-Health Monitoring As Bridge Condition Assessment Methods

2. Aggarwal, C. C., and C. K. Reddy. 2016. Data clustering: Algorithms and applications. Boca Raton, FL: CRC Press.

3. Self-Organizing Maps for Structural Damage Detection: A Novel Unsupervised Vibration-Based Algorithm

4. Model-Based Damage Identification in a Continuous Bridge Using Vibration Data

5. Applications of neural network models for structural health monitoring based on derived modal properties

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