Extreme fine-tuning and explainable AI model for non-destructive prediction of concrete compressive strength, the case of ConcreteXAI dataset-Reference-Cited by-同舟云学术

Extreme fine-tuning and explainable AI model for non-destructive prediction of concrete compressive strength, the case of ConcreteXAI dataset

Published:2024-06 Issue: Volume:192 Page:103630
ISSN:0965-9978
Container-title:Advances in Engineering Software
language:en
Short-container-title:Advances in Engineering Software

Author:

Guzmán-Torres J.A.^ORCID,Domínguez-Mota F.J.,Tinoco-Guerrero G.,Tinoco-Ruíz J.G.,Alonso-Guzmán E.M.

Publisher

Elsevier BV

Reference67 articles.

1. Effective medium crack classification on laboratory concrete specimens via competitive machine learning;Guzmán-Torres,2022

2. A multi-layer approach to classify the risk of corrosion in concrete specimens that contain different additives;Guzmán-Torres;Case Stud Constr Mater,2021

3. Explainable computational intelligence method to evaluate the damage on concrete surfaces compared to traditional visual inspection techniques;Guzmán-Torres,2024

4. Explainable machine learning using real, synthetic and augmented fire tests to predict fire resistance and spalling of RC columns;Naser;Eng Struct,2022

5. Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete;Tien Bui;Eng Comput,2021

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1. Visualization analysis of concrete crack detection in civil engineering infrastructure based on knowledge graph;Case Studies in Construction Materials;2024-12

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