Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity-Reference-Cited by-同舟云学术

Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity

Published:2024-01-11 Issue: Volume: Page:1-24
ISSN:1058-9759
Container-title:Nondestructive Testing and Evaluation
language:en
Short-container-title:Nondestructive Testing and Evaluation

Author:

Sathiparan Navaratnarajah¹,Jeyananthan Pratheeba²

Affiliation:

1. Department of Civil Engineering, Faculty of Engineering, University of Jaffna, Kilinochchi, Sri Lanka

2. Department of Computer Engineering, Faculty of Engineering, University of Jaffna, Kilinochchi, SriLanka

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10589759.2024.2304257

Reference80 articles.

1. The effects of using agricultural waste as partial substitute for sand in cement blocks

2. Performance of sustainable cement mortar containing different types of masonry construction and demolition wastes

3. Comparison of strength and durability properties between earth-cement blocks and cement–sand blocks

4. Characterization of manufactured sand: Particle shape, surface texture and behavior in concrete

5. Comparison of natural and manufactured fine aggregates in cement mortars

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete;Construction and Building Materials;2024-08

2. Predicting compressive strength of pervious concrete with fly ash: a machine learning approach and analysis of fly ash compositional influence;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-07-25

3. Data-driven models to predict the water-to-cement ratio and initial setting time of cement grouts;Nondestructive Testing and Evaluation;2024-07-24

4. Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters;Road Materials and Pavement Design;2024-07-09

5. Predicting corrosion behaviour of steel reinforcement in eco-friendly coral aggregate concrete based on hybrid machine learning methods;Nondestructive Testing and Evaluation;2024-04-30