Prediction model for compressive strength of alkali-activated multi-source solid-waste grouting materials based on generative adversarial networks and machine learning.
- DOI
- 10.1371/journal.pone.0357051
- Published
- 2026
- Container
- PloS one
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0357051,
title = {Prediction model for compressive strength of alkali-activated multi-source solid-waste grouting materials based on generative adversarial networks and machine learning.},
author = {Yue Q and Yu G and Yao Y and Yang Z and Li Y and Bai L and Xiong G},
year = {2026},
journal = {PloS one},
doi = {10.1371/journal.pone.0357051},
url = {https://doi.org/10.1371/journal.pone.0357051}
}RIS
TY - JOUR TI - Prediction model for compressive strength of alkali-activated multi-source solid-waste grouting materials based on generative adversarial networks and machine learning. AU - Yue Q AU - Yu G AU - Yao Y AU - Yang Z AU - Li Y AU - Bai L AU - Xiong G PY - 2026 JO - PloS one DO - 10.1371/journal.pone.0357051 UR - https://doi.org/10.1371/journal.pone.0357051 ER -
APA
Q, Y., G, Y., Y, Y., Z, Y., Y, L., L, B., & G, X. (2026). Prediction model for compressive strength of alkali-activated multi-source solid-waste grouting materials based on generative adversarial networks and machine learning.. PloS one. https://doi.org/10.1371/journal.pone.0357051
Source records
- pubmed · retrieved 2026-11-10T14:39:50.086Z