Barreto-Sandoval, Gersson Fernando
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Barreto-Sandoval
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Gersson Fernando
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gersson.barreto@ucn.cl
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Publicación Clogging reduction and maintenance of pervious concrete pavements(Elsevier, 2025-02-07) Barreto-Sandoval, Gersson Fernando; Possan, Edna; Campos-de-Moura, André; Pieralisi, RicardoPervious concrete (PC), known for its high hydraulic capacity, is widely employed in paving and sidewalks, subject to environmental exposure such as rainfall and soil erosion. The hydraulic functionality of PC is intricately linked to clogging, influenced by material exposure and the potential for adjacent soil erosion. Sediments, transported as runoff, can partially or fully obstruct the material's pores, resulting in significant permeability reductions, often exceeding 90% of the initial permeability. Addressing this issue, hydraulic maintenance becomes crucial for PC, involving techniques to remove sediments and restore permeability. The choice of maintenance technique depends on the sediment type, a critical consideration in the mix design process. Implementing effective maintenance not only facilitates permeability recovery but also extends the material's hydraulic useful life. The maintenance periodicity will be project-specific, based on individual hydraulic requirements, ensuring the material's sustained hydraulic serviceability.Publicación Permeability measurement and prediction of pervious concrete pavements(Elsevier, 2025) Pieralisi, Ricardo; Cunha-Lofrano, Fábio; Jansen-Mikami, Rafael; Barreto-Sandoval, Gersson FernandoA robust methodology for assessing and predicting permeability in pervious concrete is indispensable for the design of pervious concrete pavements, as well as for quality control and performance evaluation during construction and throughout the service life of the pavement. This chapter provides a comprehensive analysis of flow through porous media, permeability testing, and prediction models specific to pervious concrete. Literature findings reveal variations in hydraulic conductivity corresponding to changes in the hydraulic gradient, highlighting inaccuracies in applying Darcy's law. This study investigates discrepancies between permeability measurements conducted in the laboratory and those observed in the field, emphasizing the limitations of Darcy’s law and reflecting on alternative sources of potential inconsistency. Additionally, there is a gap in investigating nonlinear aspects of analytical, empirical, and numerical models for permeability prediction. In conclusion, this study underscores the potential of machine learning models to predict permeability and optimize pervious concrete mixture designs.