Material Forecasting in PEB Fabrication Using Machine Learning Techniques

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : Ringle Raja, Hemalatha, Vincent Sam Jebadurai, Elizabeth Amudhini Stephen

10.14445/23488352/IJCE-V12I6P108

How to Cite?

Ringle Raja, Hemalatha, Vincent Sam Jebadurai, Elizabeth Amudhini Stephen, "Material Forecasting in PEB Fabrication Using Machine Learning Techniques," SSRG International Journal of Civil Engineering, vol. 12,  no. 6, pp. 83-90, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I6P108

Abstract:

This paper presents a machine learning-based predictive model to enhance material forecasting and resource optimization in Pre-Engineered Building (PEB) production. Random Forest Regression, along with GridSearchCV for hyperparameter optimization and training, is utilized in the proposed model. A dataset of 70+ actual-time project executions with careful filtering based on stringent disaggregation requirements, including fabrication completeness, critical components of PEB, and quantity limitations, is trained. Projects are probabilistically differentiated into P1 (AND) and P2 (OR) types and also differentiated into fabrication combinations (C1, C2, C3) based on structural complexity and member contribution. A subset of 47 highly used and influential consumables is chosen from 100 plus consumables commonly used to enhance model performance. The proposed predictive model identifies a profitability percentage of 80% in all the grouped datasets, thereby asserting its feasibility for real-world use. The proposed methodology facilitates proper material planning, minimizes waste in fabrication, and aids in strategic decision-making for medium-to-large-scale industrial PEB projects, thereby driving sustainability and increasing operational efficiency.

Keywords:

Artificial Intelligence (AI), AI-based training Systems, Machine Learning (ML), Material Forecasting, Pre-Engineered Buildings (PEB).

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