Predicting the Twenty-Eight Day Compressive Strength of OPC- and PPC-Prepared Concrete through Hybrid GA-XGB Model-Reference-Cited by-同舟云学术

Predicting the Twenty-Eight Day Compressive Strength of OPC- and PPC-Prepared Concrete through Hybrid GA-XGB Model

Published:2023-08 Issue:3 Volume:28 Page:
ISSN:1084-0680
Container-title:Practice Periodical on Structural Design and Construction
language:en
Short-container-title:Pract. Period. Struct. Des. Constr.

Author:

Nayak Dheeresh Kumar¹^ORCID,Verma Gaurav²^ORCID,Dimri Akshat³^ORCID,Kumar Rajesh⁴,Kumar Veerendra⁴

Affiliation:

1. Ph.D. Candidate, Dept. of Civil Engineering, Indian Institute of Technology (Banaras Hindu Univ.), Varanasi, Uttar Pradesh 221005, India. ORCID: .

2. Ph.D. Candidate, Dept. of Civil Engineering, Indian Institute of Technology (Banaras Hindu Univ.), Varanasi, Uttar Pradesh 221005, India (corresponding author). ORCID: .

3. Ph.D. Candidate, Dept. of Civil Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, West Bengal 721302, India. ORCID: .

4. Professor, Dept. of Civil Engineering, Indian Institute of Technology (Banaras Hindu Univ.), Varanasi, Uttar Pradesh 221005, India.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Arts and Humanities (miscellaneous),Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/PPSCFX.SCENG-1315

Reference48 articles.

1. Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing

2. Modeling the compressive strength of high-strength concrete: An extreme learning approach

3. Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model

4. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

5. Prediction of the strength of mineral-addition concrete using regression analysis

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