Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity-Reference-Cited by-同舟云学术

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

Published:2024-02 Issue: Volume:225 Page:114006
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Sathiparan Navaratnarajah^ORCID,Jeyananthan Pratheeba,Subramaniam Daniel Niruban

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

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4. Influence of aggregate gradation and compaction on compressive strength and porosity characteristics of pervious concrete;Anburuvel;Int. J. Pavement Eng.,2022

5. Experimental study on the porosity evaluation of pervious concrete by using ultrasonic wave testing on surfaces;Ridengaoqier;Constr. Build. Mater.,2021

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