An investigation of machine learning algorithms for estimating fracture toughness of asphalt mixtures-Reference-Cited by-同舟云学术

An investigation of machine learning algorithms for estimating fracture toughness of asphalt mixtures

Published:2024-07 Issue: Volume:435 Page:136783
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Talebi Hossein^ORCID,Bahrami Bahador^ORCID,Ahmadian Hossein,Nejati Morteza,Ayatollahi Majid R.

Publisher

Elsevier BV

Reference84 articles.

1. An analytical study for debonding in single-lap shear test by considering the residual strength;Mirzaei;Procedia Struct. Integr.,2021

2. Phase-Field Approach for Fracture Prediction of Brittle Cracked Components;Ahmadian,2024

3. Analyzing quasi-static fracture of notched magnesium ZK60 using notch fracture toughness and support vector machine;Alshaiji;Theor. Appl. Fract. Mech.,2022

4. Data-Driven Prediction of Fracture Size Effect;Mehraban,2024

5. Estimating the effective fracture toughness of a variety of materials using several machine learning models;Mahmoodzadeh;Eng. Fract. Mech.,2023

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1. Optimizing porous asphalt mix design for permeability and air voids using response surface methodology and artificial neural networks;Construction and Building Materials;2024-09

2. Fiber Showdown: A Comparative Analysis of Glass vs. Polypropylene Fibers in Hot-Mix Asphalt Fracture Resistance;Buildings;2024-08-31

3. A Deep Neural Network Approach towards Performance Prediction of Bituminous Mixtures Produced Using Secondary Raw Materials;Coatings;2024-07-23