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Advanced hybrid machine learning models for estimating chloride penetration resistance of concrete structures for durability assessment: optimization and hyperparameter tuning Cover

Advanced hybrid machine learning models for estimating chloride penetration resistance of concrete structures for durability assessment: optimization and hyperparameter tuning

Open Access
|Dec 2025

Authors

Irfan Ullah

19pwciv5205@uetpeshawar.edu.pk

College of Civil and Transportation Engineering, Hohai University, Nanjing, China

Muhammad Faisal Javed

arbabfaisal@giki.edu.pk

Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Swabi, Pakistan

Deema Mohammed Alsekait

Dmalsekait@pnu.edu.sa

Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Mohammed Jameel

jamoali@kku.edu.sa

Department of Civil Engineering, College of Engineering, King Khalid University, Asir, Abha, Saudi Arabia

Hisham Alabduljabbar

h.alabduljabbar@psau.edu.sa

Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin 14 Abdulaziz University, Al-Kharj, Saudi Arabia

Khawaja Atif Naseem

khawaja-atif.naseem1@louisiana.edu

Arkansas Department of Transportation,, Little Rock, USA

Diaa Salama AbdElminaam

diaa.salama@miuegypt.edu.eg

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
Jadara Research Center, Jadara University, Irbid, Jordan
Language: English
Submitted on: Apr 9, 2025
Accepted on: Nov 18, 2025
Published on: Dec 17, 2025
Published by: Sciendo
In partnership with: Paradigm Publishing Services

© 2025 Irfan Ullah, Muhammad Faisal Javed, Deema Mohammed Alsekait, Mohammed Jameel, Hisham Alabduljabbar, Khawaja Atif Naseem, Diaa Salama AbdElminaam, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.