Damage and Deterioration Model of Basalt Fiber/Magnesium Oxychloride Composites Based on GM(1, 1)-Markov in the Salt Spray Corrosion Environment

Abstract

This study was designed to solve the problem of magnesium hazards due to potash extraction in the salt lake region. Using basalt fiber (BF) as the reinforcement material and magnesium oxychloride cement (MOC) as the gelling material, a BF/MOC composite material was prepared. Firstly, the effect of BF addition content on the basic mechanical properties of the composites was investigated. Then, through the salt spray corrosion test, the durability damage deterioration evaluation analysis was carried out from both macroscopic and microscopic aspects using mass change, relative dynamic modulus of elasticity (RDME) change, SEM analysis and FT-IR analysis. Finally, a GM(1, 1)-Markov model was established to predict the durability life of composite materials by using durability evaluation indicators. The results show that: when the BF content is 0.10% (by volumetric content), the composites have the best mechanical properties and resistance to salt spray corrosion. However, when the volume of BF content exceeds 0.10%, a large number of magnesium salt crystallization products are observed from the microscopic point of view, and the corrosion of the main strength phase of MOC is more serious. The prediction results of the GM(1, 1)-Markov model are highly identical with the raw data. In addition, using the change of RDME as a predictor, RDME is more sensitive to environmental factor compared to the change of mass. Predictions using the change of RDME as a threshold indicate that MOC-BF0.10 has the longest durability life, which is 836 days. The model is important to promote the application of MOC composites in the salt lake region and to promote the healthy development of green building materials.

Durability and life prediction analysis of recycled aggregate concrete with ceramic waste powder under freeze-thaw conditions based on impact-echo method and Grey-Markov model

“…Those interested can find the details in the literature (Yang and Liu, 2008;Jiang et al, 2017;Mao et al, 2020). Referring to the author's previous research (Yu et al, 2022), the Markov chain based on the division of ε (0) ( t) into k states can be expressed as Eq. 19:…”