Developing machine learning frameworks to predict mechanical properties of ultra-high performance concrete mixed with various industrial byproducts.

Journal: Scientific reports
Published Date: Jul 9, 2025

Abstract

This research investigates the predictive modeling of ultra-high-performance concrete (UHPC) incorporating industrial byproducts, focusing on compressive strength (Fc), flexural strength (Ff), workability (Slump), and porosity. Various machine learning models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Table (DT), were evaluated to determine the most accurate predictions for each property. The findings indicate that Kstar outperformed other models across all categories, demonstrating the highest accuracy and lowest errors in predicting Fc, Ff, Slump, and Porosity. Sensitivity analyses using SHAP and Hoffman & Gardener's methods identified the most influential parameters affecting each UHPC property, providing insights into the key factors driving concrete performance. The research highlights the sustainable impact of incorporating industrial byproducts, reducing environmental footprints while maintaining superior mechanical and durability properties. The developed models offer practical guidance for optimizing UHPC formulations, promoting sustainability in the construction industry by enabling efficient material utilization and reducing reliance on conventional cement-based components.

Authors

  • Kennedy C Onyelowe
    Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, 440109, Nigeria. kennedychibuzor@kiu.ac.ug.
  • Shadi Hanandeh
    Department of Civil Engineering, al-Balqa Applied University, As-Salt, Jordan.
  • Nestor Ulloa
    Facultad de Mécanica, Escuela Superior Politécnica de Chimborazo (ESPOCH), 060155, Riobamba, Ecuador. nestor.ulloa@espoch.edu.ec.
  • Ruth Barba-Vera
    Facultad de Informática y Electrónica, Escuela Superior Politécnica de Chimborazo (ESPOCH), 060155, Riobamba, Ecuador.
  • Arif Ali Baig Moghal
    Department of Civil Engineering, National Institute of Technology Warangal, Warangal, 506004, India.
  • Ahmed M Ebid
    Department of Civil Engineering, Faculty of Engineering, Future University in Egypt, New Cairo, Egypt.
  • Krishna Prakash Arunachalam
    Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, Chile.
  • Ateekh Ur Rehman
    Department of Industrial Engineering, College of Engineering, King Saud University, 11421, Riyadh, Saudi Arabia.

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