Operational Resilience Strategies for Geopolymer Concrete Production under Raw Material Supply Variability

Authors

  • Anber Abraheem Shlash Mohammad

    Digital Marketing Department, Faculty of Administrative and Financial Sciences, University of Petra, Amman 11196, Jordan
  • Suleiman Ibrahim Mohammad

    Business Administration Department, Business School, Al al-Bayt University, Mafraq 25113, Jordan

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
  • Asokan Vasudevan

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia

    Faculty of Management, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok 12160, Thailand

    Business Administration and Management Department, Wekerle Business School, 1083 Budapest, Hungary
  • Shaman Raj Sagai Rajan

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
  • Shiney John

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
  • Naomi Yang

    Career Services, INTI International College Subang, Subang Jaya 47500, Malaysia
  • Mahirah Saidah Marzuki

    Academic Support Unit (ASU), INTI International University, Nilai 71800, Malaysia

DOI:

https://doi.org/10.30564/jbms.v8i1.12662
Received: 3 November 2025 | Revised: 16 December 2025 | Accepted: 6 January 2026 | Published Online: 13 February 2026

Abstract

The advent of low-carbon construction has made geopolymer concrete (GPC) a sustainable material for construction. However, the supply uncertainty of the raw materials needed for GPC production makes this a challenge. This research aims to develop and design an integrated digital twin-reinforcement learning framework for optimizing geopolymer concrete production processes. The problem statement concerns the uncertainty involved when producing geopolymer concrete. This paper focuses on building a digital twin structure for optimizing the geopolymer concrete process. The authors also designed a reinforcement learning framework for optimizing the geopolymer concrete production process. The objective is achieved since the digital twin is a computer representation of a production environment. The computer simulation will utilize reinforcement learning. This will ensure that the production is done at a lower cost. Additionally, the digital twin can predict the supply uncertainty. The computer simulation will determine the supply uncertainty level. Performance was evaluated for three supply conditions: stable, with a moderate and severe level of variability, based on a set of indicators: throughput, downtime, energy consumption, CO2 emission, and quality variability. In all cases, it has been shown that the Digital Twin–Reinforcement Learning (DT–RL) approach results in a considerable improvement of production resilience and sustainability performance by as much as 22% relative to downtime performance, as well as saving 13% of energy and a decrease of CO2 emission by as much as 15% relative to static planning. Additionally, a strongly negative correlation between resilience and quality variability of manufactured products was shown to exist. This research shows that applying digital intelligence to green material production leads to an improvement in efficiency and green performance.

Keywords:

Geopolymer Concrete; Digital Twin; Reinforcement Learning; Operational Resilience; Supply Variability; Sustainability

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How to Cite

Mohammad, A. A. S., Mohammad, S. I., Vasudevan, A., Rajan, S. R. S., John, S., Yang, N., & Marzuki, M. S. (2026). Operational Resilience Strategies for Geopolymer Concrete Production under Raw Material Supply Variability. Journal of Building Material Science, 8(1), 85–103. https://doi.org/10.30564/jbms.v8i1.12662

Issue

Vol. 8 , Iss. 1 (March 2026): In Progress

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Article

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Copyright © 2026 Anber Abraheem Shlash Mohammad, Suleiman Ibrahim Mohammad, Asokan Vasudevan, Shaman Raj Sagai Rajan, Shiney John, Naomi Yang, Mahirah Saidah Marzuki

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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