.png){kind=icon}
.png){kind=icon}
Return
Sulfate Attack-Induced C-S-H Gel Degradation Mechanism and Machine Learning-Based Strength Prediction of Coal Gangue Aggregate Concrete
.png){kind=icon}
OA
AI
S
R
J
Y
L
Y
DOI：10.3390/gels12080712.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Coal gangue concrete (CGC) is an effective green building material that can promote the resource utilization of solid waste. To study its durability performance and degradation mechanism under sulfate attack with dry-wet cycles, and to realize the intelligent prediction of mechanical properties, this study prepared CGC specimens with a water-to-binder ratio of 0.4, a fine aggregate replacement rate of 20%, and coarse aggregate replacement rates of 0%, 20%, 50%, 80%, and 100%. The specimens were tested under 30, 60, 90, and 120 dry-wet cycles in 10% MgSO4 solution. Mass loss, relative dynamic elastic modulus, and compressive and flexural strength corrosion resistance coefficients were used as evaluation indices, and SEM and XRD were adopted to analyze microstructural deterioration. A database compiled from literature data was established, and six machine learning models-random forest (RF), artificial neural network (ANN), decision tree (DT), support vector machine (SVM), particle swarm optimization-artificial neural network (PSO-ANN), and particle swarm optimization-support vector machine (PSO-SVM) were constructed to predict the strength corrosion resistance coefficients. Test results indicate that all macroscopic indices first increased and then decreased with the number of dry-wet cycles. Early ettringite and gypsum products filled internal pores, while prolonged sulfate attack caused decalcification and structural degradation of C-S-H gel, resulting in obvious performance loss. The PSO-SVM model showed the best prediction accuracy, with R2 values of 0.912 and 0.981 for compressive and flexural strength corrosion resistance coefficients, respectively. Feature importance analysis shows that dry-wet cycles had the most significant negative impact, followed by the coal gangue fine aggregate replacement rate. This study provides support for the durability evaluation and intelligent prediction of coal gangue concrete in sulfate environments.
Keywords:
coal gangue aggregate concrete
sulfate attack
C-S-H gel degradation
mechanical properties
corrosion resistance coefficient
machine learning
prediction model
Journal
IF:
5.3
Papers:
4.9K
Citations:
1.4W
Organization
Cited Papers