Predicting the performance of pervious concrete pavements using artificial intelligence

Abdulkader El-Mir; Dana Nasr; Hilal El-Hassan

doi:10.1016/B978-0-443-21704-3.00016-4

Predicting the performance of pervious concrete pavements using artificial intelligence

Abdulkader El-Mir, Dana Nasr, Hilal El-Hassan

Department of Civil and Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Citation (Scopus)

Abstract

This chapter provides a comprehensive and up-to-date analysis of advanced machine learning (ML) methods applied to predict the key properties of pervious concrete (PC). Initially, a thorough review and evaluation of various ML techniques are conducted. Then, the application of these methods in predicting fundamental properties of PC, such as compressive strength, permeability, and porosity, is elucidated. The review identifies research gaps in the current understanding of these prediction models and outlines potential avenues for future research in this field.

Original language	English
Title of host publication	Pervious Concrete Pavements
Subtitle of host publication	Design, Performance, and Applications
Publisher	Elsevier
Pages	319-343
Number of pages	25
ISBN (Electronic)	9780443217043
ISBN (Print)	9780443217050
DOIs	https://doi.org/10.1016/B978-0-443-21704-3.00016-4
Publication status	Published - Jan 1 2025

Keywords

artificial intelligence
machine learning
mechanical properties
permeability
Pervious concrete

ASJC Scopus subject areas

General Engineering
General Materials Science

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10.1016/B978-0-443-21704-3.00016-4

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Predicting the performance of pervious concrete pavements using artificial intelligence

Abstract

Keywords

ASJC Scopus subject areas

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