Prediction of compressive strength of nano concrete used in construction under freeze-thaw cycles

In order to clarify the compressive strength performance of nano concrete under harsh conditions of freeze-thaw cycles, a study was conducted on the prediction of compressive strength of nano concrete for construction under freeze-thaw cycles. Firstly, analyse the preparation raw materials and properties of nano concrete, and prepare nano concrete specimens according to the preparation process of nano concrete. Secondly, set the conditions for freeze-thaw cycles and select the pressure machine for the experiment. Finally, taking the dosage of nano SiO2 as input and the predicted compressive strength as output, a BP neural network was used to construct a nano concrete compressive strength prediction model, and the predicted compressive strength results were obtained. The research results indicate that the relative error in predicting the compressive strength of nano concrete proposed in this article is relatively small, with a maximum relative error not exceeding 8%.

Authors

Institutions

Publication Details

Journal
International Journal of Microstructure and Materials Properties
Published
2024-12-20
DOI
https://doi.org/10.1504/ijmmp.2025.143422
Primary Topic
Nanotechnology research and applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prediction of compressive strength of nano concrete used in construction under freeze-thaw cycles

Xiaoyuan Xiong, Xuyang Li
3 links
International Journal of Microstructure and Materials Properties
Nanotechnology research and applications
article

Prediction of compressive strength of nano concrete used in construction under freeze-thaw cycles

Xiaoyuan Xiong, Xuyang Li
article en

Abstract

In order to clarify the compressive strength performance of nano concrete under harsh conditions of freeze-thaw cycles, a study was conducted on the prediction of compressive strength of nano concrete for construction under freeze-thaw cycles. Firstly, analyse the preparation raw materials and properties of nano concrete, and prepare nano concrete specimens according to the preparation process of nano concrete. Secondly, set the conditions for freeze-thaw cycles and select the pressure machine for the experiment. Finally, taking the dosage of nano SiO2 as input and the predicted compressive strength as output, a BP neural network was used to construct a nano concrete compressive strength prediction model, and the predicted compressive strength results were obtained. The research results indicate that the relative error in predicting the compressive strength of nano concrete proposed in this article is relatively small, with a maximum relative error not exceeding 8%.

International Journal of Microstructure and Materials PropertiesVol. 18(1/2)
Zhoukou City Academy of Agricultural Sciences (CN), Henan Forestry Vocational College (CN)
Sustainable cities and communities
Openalex Percentile: Top 78%
Nanotechnology research and applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Prediction of compressive strength of nano concrete used in construction under freeze-thaw cycles — Xiaoyuan Xiong, Xuyang Li · International Journal of Microstructure and Materials Properties (2024) | TGRS Research Map | TGRS