COVADONGA PALENCIA COTO-rekin lankidetzan egindako argitalpenak (9)

2024

  1. To determine the compressive strength of self-compacting recycled aggregate concrete using artificial neural network (ANN)

    Ain Shams Engineering Journal, Vol. 15, Núm. 2

2022

  1. A Comparison of Machine Learning Tools that Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

    Materials, Vol. 15, Núm. 12

  2. A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

    Materials, Vol. 15, Núm. 15

  3. Impact of sulfate activation of rice husk ash on the performance of high strength steel fiber reinforced recycled aggregate concrete

    Journal of Building Engineering, Vol. 54

  4. Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

    Mathematics, Vol. 10, Núm. 13

  5. Satisfaction Level of Engineering Students in Face-to-Face and Online Modalities under COVID-19—Case: School of Engineering of the University of León, Spain

    Sustainability, Vol. 14, Núm. 10, pp. 6269

  6. To determine the performance of metakaolin-based fiber-reinforced geopolymer concrete with recycled aggregates

    Archives of Civil and Mechanical Engineering, Vol. 22, Núm. 3

  7. To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models

    Case Studies in Construction Materials, Vol. 16, pp. e01046

2021

  1. Impact of design parameters on the ratio of compressive to split tensile strength of self‐compacting concrete with recycled aggregate

    Materials, Vol. 14, Núm. 13