Experimental assessment of the efficiency of deep learning method in predicting the mechanical properties of polymer concretes and composites-Reference-Cited by-同舟云学术

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Experimental assessment of the efficiency of deep learning method in predicting the mechanical properties of polymer concretes and composites

Published:2023-10 Issue: Volume:76 Page:107107
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Hassani Niaki Mostafa^ORCID,Pashaian Matin,Ghorbanzadeh Ahangari Morteza^ORCID

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference53 articles.

1. Polymer Concretes: Advanced Construction Materials;Niaki,2022

2. Application of Polymer in Concrete Construction;Hing,2008

3. Polymer concrete as construction materials;Yeon;Int. J. Soc. Mater. Eng. Resour.,2010

4. Reviewing some properties of polymer concrete;Bedi;Indian Concr. J.,2014

5. Polymer concrete;Taha,2019

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1. Alkali resistance prediction and degradation mechanism of basalt fiber: Integrated with artificial neural network machine learning model;Journal of Building Engineering;2024-06

2. Using deep learning method to predict dimensionless values of stress intensity factors and T‐stress of edge notch disk bend (ENDB) specimen;Fatigue & Fracture of Engineering Materials & Structures;2024-05-13

3. Effect of basalt fibers on fracture properties of nanoclay reinforced polymer concrete after exposure to elevated temperatures;Journal of Building Engineering;2023-10

4. Predicting geometry factors and normalized T‐stress of centrally cracked Brazilian disk specimens using deep learning method;Fatigue & Fracture of Engineering Materials & Structures;2023-08-09

5. A toughening mechanism of the strain rate-optimal chain length on polymer-modified calcium silicate hydrates (CSH);Physical Chemistry Chemical Physics;2023

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