Decarbonizing precast concrete building components: Cradle-to-site carbon modeling and optimization, explainable machine learning, and a transportation efficiency index

  • Peyman Naghipour orcid

    Department of Architecture, Ta.C., Islamic Azad University, Tabriz, Iran
  • Afshin Naghipour orcid

    Department of Civil Engineering - Civil, Ta.C., Islamic Azad University, Tabriz, Iran
  • Tarana Bakirova orcid

    Graphic and Media Design Department, Design Faculty, Azerbaijan University of Architecture and Construction, Baku, Azerbaijan
  • Hussein Ghiyasi orcid

    Department of Architecture, Ta.C., Islamic Azad University, Tabriz, Iran
  • Faraneh Soltani Gerd Faramarzi orcid

    Department of Architecture, CT.C., Islamic Azad University, Tehran, Iran
  • Farazin Soltani Gerd Faramarzi orcid

    Department of Architecture, CT.C., Islamic Azad University, Tehran, Iran
Article ID: 4056
DOI: https://doi.org/10.59400/be4056
Keywords: prefabricated buildings, embodied carbon, cradle-to-site emissions, transportation stage, explainable machine learning, transportation efficiency index (TEI)

Abstract

Reducing carbon in prefabricated buildings demands component-scale evidence, yet most assessments remain confined to factory production and provide limited, non-transparent guidance on how transportation and on-site installation decisions reshape emissions. This study delivers a consistent framework for quantifying and predicting emissions from the production, transportation, and installation of precast concrete components. It explores the concept that integrating coordinated design standards with logistical planning leads to considerable reductions in cradle-to-site emissions. The framework contributes: (i) a tri-stage system boundary; (ii) a machine-learning plus explainable-AI (XAI) model for transport coupled with a new Transportation Efficiency Index (TEI), defined as delivered component volume-distance per unit CO2e; and (iii) joint optimization of design standardization and logistics parameters. Empirical data were obtained from a prefabrication plant in Tehran, Iran (156,000 m2 footprint; 300,000 m3·yr−1 capacity), including 411 daily energy/resource records, bills of materials and mold-use logs, 408 manufactured components, and matched delivery/installation activities. Gradient-boosted trees yield high predictive accuracy (coefficient of determination R2 = 0.99 for production and R2 = 0.97 for transportation; mean absolute percentage error MAPE < 6%), while XAI identifies component volume, design standardization, route distance, and truck utilization as dominant drivers; materials account for ~91–98% of production emissions and mold amortization falls from ~9% to <3% when standardization exceeds 0.90 and reuse surpasses ~60 cycles. Scenario optimization improves TEI by ~25% and reduces combined production-to-installation emissions by ~20–30%, providing actionable guidance for manufacturers, contractors, and policymakers seeking low-carbon prefabrication supply chains.

Published
2026-04-16
How to Cite
Naghipour, P., Naghipour, A., Bakirova, T., Ghiyasi, H., Faramarzi, F. S. G., & Faramarzi, F. S. G. (2026). Decarbonizing precast concrete building components: Cradle-to-site carbon modeling and optimization, explainable machine learning, and a transportation efficiency index. Building Engineering, 4(2). https://doi.org/10.59400/be4056
Issue
Vol. 4 No. 2 (2026)
Section
Article

Copyright (c) 2026 Peyman Naghipour, Afshin Naghipour, Tarana Bakirova, Hussein Ghiyasi, Faraneh Soltani Gerd Faramarzi, Farazin Soltani Gerd Faramarzi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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