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Predicting the flowability of UHPC and identifying its significant influencing factors using an accurate ANN model
Predicción de la trabajabilidad del UHPC e identificación de sus factores de influencia significativos utilizando un modelo ANN preciso
DOI:
https://doi.org/10.15446/dyna.v91n231.109579Palabras clave:
ANN; slump flow; k-fold validation; connection weight approach; supplementary cementitious materials (en)ANN, flujo estático; validación cruzada tipo k-fold; moldeo de conexión de pesos; materiales cementantes suplementarios (es)
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In this research, a one-hidden layer artificial neural network paradigm (ANN) was created to forecast the slump flow of ultra-high-performance concrete (UHPC). To achieve this goal, 3,200 ANNs were evaluated to estimate the fresh UHPC’s slump flow utilizing 793 observations. The performance metrics measured on training and test data subsets were in the same order of magnitude, thereby pointing out the proper work of the k-fold validation procedure. The results of the connection weight approach analysis (CWA) indicated that water dosage had the highest positive importance in slump flow, preceding the superplasticizer volume ratio. Other factors that positively influenced slump flow were the water-to-powder ratio, the dosage of high-alkali glass powder, the water-to-binder ratio, and limestone concentration. The most negative influences on rheology were the high-alumina FC3R and metakaolin. The ANN accurately predicted the slump flow of UHPC, while the results of the CWA analysis were well-correlated with previous research.
En esta investigación, se desarrolló un modelo de red neuronal artificial de una capa oculta para pronosticar el flujo estático del concreto de ultra alto rendimiento (UHPC). Se evaluaron 3200 redes neuronales artificiales para estimar el flujo estático del UHPC fresco utilizando 793 observaciones. Las métricas de rendimiento medidas en los subconjuntos de datos de entrenamiento y de testeo estuvieron en el mismo orden de magnitud, lo que indica el trabajo adecuado del procedimiento de validación cruzada k-fold. Los resultados del análisis de enfoque de peso de conexión (CWA) indicaron que el contenido de agua tuvo la mayor importancia positiva en el flujo estático, precediendo a la relación de volumen del superplastificante. Otros factores que influyeron positivamente en el flujo estático fueron la relación agua-polvos-totales, la dosificación de polvo de vidrio con alto contenido de álcali, la relación agua-aglutinante y la dosificación del carbonato cálcico. La influencia más negativa en la reología fueron el FC3R alto en alúmina y el metacaolín. La ANN predijo con precisión el flujo de asentamiento de UHPC, mientras que los resultados del análisis CWA se correlacionaron bien con investigaciones previas.
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2. Tianshuai Yao, Yuli Wang, Xilin Wang, Shuqiong Luo. (2025). Effect of ultrafine coal gasification slag on the properties of ultra-high performance concrete (UHPC). Construction and Building Materials, 492, p.142946. https://doi.org/10.1016/j.conbuildmat.2025.142946.
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