A Machine Learning Prediction of Mechanical Properties in Reinforcement Bars: A Data-Driven Approach

Taimoor Hussain Qureshi; Muhammad Ali Siddiqui; Syed Dabeer Mehdi Naqvi; Muhammad Zeeshan; Minhal Waseem; Zain Noreen

Authors

Taimoor Hussain Qureshi Celeritas Digital, Karachi, Pakistan
Muhammad Ali Siddiqui Computational and Experimental Materials Innovation Group (CEMIG), Department of Metallurgical Engineering, NED University of Engineering and Technology, Karachi, Pakistan
Syed Dabeer Mehdi Naqvi IRD, Pakistan
Muhammad Zeeshan Department of Mathematics, NED University of Engineering and Technology, Karachi, Pakistan
Minhal Waseem Computational and Experimental Materials Innovation Group (CEMIG), Department of Metallurgical Engineering, NED University of Engineering and Technology, Karachi, Pakistan
Zain Noreen FAST National University of Computer and Emerging Sciences, Karachi Pakistan

Keywords:

Machine Learning, Reinforcement Bars (Rebars), Data-Driven Approach, Chemical Composition, Quality Control

Abstract

Introduction/Importance of Study: This study addresses the pressing need for precise prediction of mechanical properties in steel reinforcement bars (rebars) through a data-driven approach utilizing machine learning techniques.

Novelty statement: Our research provides a solution to the challenge of predicting mechanical properties in rebars using advanced machine learning algorithms, filling a critical gap in existing methodologies.

Material and Method: Our study utilized a meticulously curated dataset comprising over 10,300 samples of diverse rebar types manufactured through industrial methods. We leveraged the latest PyCaret model to integrate machine learning algorithms, with a focus on training and rigorously testing linear regression models. Data preprocessing involved thorough cleaning using Python libraries such as Pandas and NumPy, supplemented by cross-validation techniques to ensure robust model generalization.

Result and Discussion: The core findings of our study revolve around the linear regression model algorithm trained within the machine learning framework, enabling precise determination of key mechanical properties including Yield Strength (YS) and ultimate Tensile Strength (UTS). Additionally, we explored the Ratio of UTS to YS (UTS/YS) as a critical mechanical property, incorporating essential input features such as weight percent of carbon (C), manganese (Mn), silicon (Si), carbon equivalent (Ceq), quenching parameters (Q), and diameter (d).

Concluding Remarks: Our research offers valuable insights into the application of machine learning for the precise prediction of mechanical properties in reinforcement bars, contributing to enhanced quality control and optimization in the steel manufacturing industry.

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