Vol. 17 No. 01 (2027): Journal of Materials & Construction
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JOURNAL OF MATERIALS & CONSTRUCTION

ISSN: 2734-9438

Website: www.jomc.vn

Graph neural network-based optimization of embodied CO2 emission in residential building layouts through concrete and reinforcement steel design

Do Tuong Dat Dinh , Le Thi Van Anh

Keywords:

Graph neural networks
Embodied CO2 emissions
Structural optimization
Reinforced concrete
Residential building layout
Sustainable design
Embodied carbon
Deep learning in structural engineering

Abstract

The reinforced concrete (RC) construction of residential buildings contributes significantly to embodied carbon emissions, while conventional structural design may lead to material overuse due to reliance on experience-based sizing and simplified design practices. This study develops a Graph Neural Network (GNN)-based framework for optimizing RC member dimensions and reinforcement ratios to minimize embodied CO2 while satisfying structural safety constraints. Residential layouts are represented as graphs, with columns as nodes and beams, slabs, and load paths as edges. A GraphSAGE model is trained using a synthetic dataset generated from closed-form RC design equations and serves as a surrogate model for predicting embodied CO2 and structural safety margins. The surrogate is integrated into a constrained optimization scheme to identify material-efficient structural designs. Numerical applications to single- and two-story residential buildings demonstrate that the proposed framework can reduce embodied CO2 relative to baseline designs while maintaining positive safety margins for all structural components. The results highlight the potential of GNN-based surrogate optimization as an efficient approach for low-carbon RC structural design.

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How to Cite

Do Tuong Dat Dinh, & Le Thi Van Anh. (2026). Graph neural network-based optimization of embodied CO2 emission in residential building layouts through concrete and reinforcement steel design. JOURNAL OF MATERIALS & CONSTRUCTION, 17(01). https://doi.org/10.54772/jomc.v17i01.1482

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