Construction cost prediction using a fused loss–enhanced hybrid GCN–GAT–MLP approach

Purpose Accurate prediction of construction costs is vital for effective project management and risk mitigation in the construction industry. This study aims to enhance the precision of construction cost forecasts by developing a hybrid model that integrates graph convolutional networks (GCN), graph attention networks (GAT) and multi-layer perceptrons (MLP). Design/methodology/approach A hybrid GCN-GAT-MLP approach is introduced to effectively capture the complex relationships and dependencies within construction cost data. GCN is utilized to extract local topological information, GAT employs an attention mechanism to adaptively weigh the influence of neighboring nodes and MLP captures global non-linear patterns. We also employed a gate network to dynamically allocate branch weights for feature fusion and integrated multiple loss functions, thereby enhancing the model’s generalization ability. The model’s performance is assessed by comparing it to traditional machine learning models and standalone graph neural networks (GNNs), using metrics such as root mean square error (RMSE), R², MAE and MAPE. Findings The proposed GCN-GAT-MLP hybrid model with a fused loss function exhibits outstanding predictive performance, achieving an average RMSE of 0.4490, R² of 0.9891, mean absolute error of 0.2640 and mean absolute percentage error of 0.0159 across ten independent runs using different random seeds. These metrics demonstrate substantial improvements over baseline machine learning models and standalone GNNs and highlight the superior performance of the fused loss function compared to standard loss functions. Results visualizations like density scatter plots and violin plots further confirm the model’s predictions closely align with actual values, with minimal errors, making it a robust tool for practical cost estimation. Research limitations/implications The proposed hybrid model enhances early-stage construction cost forecasting by addressing accuracy issues and incorporating structural characteristics. It supports project managers with reliable data for better decision-making, optimizes budgets and mitigates risks, while enabling stakeholders like investors and contractors to improve project outcomes through informed cost control. The model’s high accuracy and efficient training also boost resource allocation and productivity, promoting sustainable and intelligent construction practices. While effective for moderate sample sizes, the proposed hybrid model faces limitations including increased complexity with larger datasets, reduced interpretability of its predictions and challenges in efficiently processing real-time data updates. Originality/value This paper introduces a pioneering hybrid model that integrates GCN, GAT and MLP for construction cost prediction, filling a gap in the application of advanced GNNs within this field. This study innovatively employs a gate network to dynamically allocate branch weights for feature fusion and incorporates multi-loss integration, further enhancing prediction accuracy and improving the model’s generalization capability. This paper establishes a new standard for predictive modeling in construction cost estimation, with significant potential to influence future research and industry practices.