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Production and Analysis of Sporosarcina pasteurii Biocement Bricks Using Custom 3D-Printed Molds for Unconfined Compression Tests
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Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.

Muhammad Nasir Amin1, Kaffayatullah Khan1, Muhammad Faisal Javed2

  • 1Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf, Al-Ahsa 31982, Saudi Arabia.

Materials (Basel, Switzerland)
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

This study models concrete compressive strength using rice husk ash (RHA) as a cement substitute. Key factors influencing strength are concrete age, cement, and aggregate content, promoting sustainable construction.

Keywords:
compressive strengthexternal validationmachine learningrice husk ashwaste material

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Environmental Science

Background:

  • Rice husk ash (RHA) is an agricultural byproduct posing environmental challenges.
  • Recycling RHA in construction materials offers a sustainable solution.
  • Developing predictive models for RHA concrete is crucial for its wider adoption.

Purpose of the Study:

  • To develop an empirical model for predicting the compressive strength of concrete incorporating RHA (CRHA).
  • To identify key factors influencing the mechanical properties of CRHA.
  • To promote the utilization of RHA in green concrete production.

Main Methods:

  • Utilized multi expression programming (MEP) to build a predictive model.
  • Compiled a database of 192 experimental results from literature.
  • Input variables included specimen age, RHA percentage, cement, superplasticizer, aggregates, and water content.

Main Results:

  • The MEP model accurately forecasts CRHA compressive strength.
  • Specimen age, cement content, and aggregate amount were identified as the most significant factors.
  • Sensitivity and SHAP analyses validated model performance and factor importance.

Conclusions:

  • The developed model facilitates the use of RHA in concrete, reducing environmental pollution.
  • Increased RHA utilization in green concrete offers environmental and economic benefits.
  • This research supports sustainable construction practices and waste valorization.