Optimal machine learning-based method for gauging compressive strength of nanosilica-reinforced concrete
Author:
Funder
Deanship of Scientific Research, King Khalid University
King Khalid University
Publisher
Elsevier BV
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Reference56 articles.
1. Several machine learning models to estimate the effect of an acid environment on the effective fracture toughness of normal and reinforced concrete;Albaijan;Theor Appl Fract Mech,2023
2. Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods;Ashrafian;Constr Build Mater,2018
3. Cotransport of heavy metals and SiO2 particles at different temperatures by seepage;Bai;J Hydrol,2021
4. Behaviors of eccentrically loaded ECC-encased CFST columns after fire exposure;Cai;Engng Struct,2023
5. Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques;Chou;J Comput Civ Engng,2011
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