Study and Neural Network Analysis on Durability of Basalt Fibre Concrete

Author:

Shao Shanqing1,Wang Ran1,Gong Aimin1,Li Ruijun2,Xu Jing3,Wang Fulai1,Liu Feipeng34ORCID

Affiliation:

1. College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China

2. Hubei Academy of Water Resources and Hydropower Sciences, No. 286 Luoshi South Road, Hongshan District, Wuhan 430070, China

3. Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China

4. Southwest Survey and Planning Institute of National Forestry and Grassland Administration, Kunming 650031, China

Abstract

In order to investigate the law of basalt fibre to enhance the durability of concrete, this paper selects basalt fibre length as the main factor, supplemented by novel research methods such as neural networks, to study the rule of concrete resistance to multiple types of salt erosion. Tests have shown that large doses of mineral admixtures and basalt fibres can prolong the time that concrete is eroded by salt solutions; the age of maintenance has a small effect on the mechanical and durability of the concrete; the increase in length of basalt fibres enhances the mechanical properties of the concrete, but weakens the durability. This is exacerbated by the mixing of fibres, but the increase is not significant; the effect of length on concrete resistance to mass loss, corrosion resistance factor of compressive strength, and resistance to chloride ion attack is ranked as follows: 6 mm > 12 mm > 18 mm > 6 mm + 12 mm > 6 mm + 12 mm + 18 mm. The opposite is true for effective porosity; the highest compressive strength corrosion resistance coefficient was found in the length of 6 mm, with an average increase of 6.2% compared to 18 mm, and the mixed group was generally smaller than the single mixed group. The average increase in chloride content was 25.1% for length 18 mm compared to 6 mm; the triple-doped L6-12-18 group was the largest, with an average increase of 33.9% in effective porosity over the minimum 6 mm group. Based on the data from the above indoor trials, artificial neural network models and grey cluster analysis were used to predict and analyse the data, and the prediction and categorisation results were accurate and reliable, providing a reference for subsequent studies.

Funder

Scientific Research Fund project of Yunnan Education Department

Yunnan University Professional Degree Graduate Student Practical Innovation Fund project

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

Reference35 articles.

1. Detection and evaluation of offshore concrete structure based on durability life prediction;Tu;Water Transp. Eng.,2008

2. Research on seismic performance of combined rubber concrete and conventional concrete piles;Gao;J. Northeast. Univ. (Nat. Sci. Ed.),2021

3. Advances in the study of coupled chloride and sulphate erosion in reinforced concrete;Zhang;Mater. Guide,2022

4. The effect of sulphate attack on the shear resistance of concrete;Zhang;J. Civ. Eng.,2020

5. Progress in the study of the similarity of concrete resistance to chloride ion attack in the marine environment;Bao;J. Silic.,2020

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