Application of Random Forest and Multi-layer Perceptron ANNS in Estimating the Axial Compression Capacity of Concrete-Filled Steel Tubes-Reference-Cited by-同舟云学术

Application of Random Forest and Multi-layer Perceptron ANNS in Estimating the Axial Compression Capacity of Concrete-Filled Steel Tubes

Published:2022-06-23 Issue:6 Volume:46 Page:4111-4130
ISSN:2228-6160
Container-title:Iranian Journal of Science and Technology, Transactions of Civil Engineering
language:en
Short-container-title:Iran J Sci Technol Trans Civ Eng

Author:

Arokiaprakash A.^ORCID,Selvan S. Senthil

Publisher

Springer Science and Business Media LLC

Subject

Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s40996-022-00893-y.pdf

Reference46 articles.

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2. ANSI (2010) Committee A—Specification for structural steel buildings (ANSI/AISC 360–10). American Institute of Steel Construction, Chicago-Illinois

3. Avci-Karatas C (2019) Prediction of ultimate load capacity of concrete-filled steel tube columns using multivariate adaptive regression splines (MARS). Steel Compos Struct 33(4):583–594

4. Bahrami A, Kouhi AM (2010) Compressive behaviour of circular, square, and rectangular concrete-filled steel tube stub columns. Civil Eng Archit 8(5):1119–1126

5. Botchkarev A (2019) A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdiscip J Inf Knowl Manag 14:045–076

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