Construction Delay Prediction Model Using a Relationship-Aware Multihead Graph Attention Network

Existing machine learning (ML) delay prediction models cannot process dependencies among the construction progress records. This study investigates graph attention networks (GAT) incorporating multihead attention mechanisms for predicting construction delays. Leveraging an attention mechanism, GAT emphasizes differential node significance in networks and demonstrates the capability to learn input configurations. The data set was configured into six networks that linked records based on contractual alignment and spatial proximity dependency criteria. Under contractual alignments, predictions for electrical and concrete tasks achieved 65% and 76%, respectively, outperforming spatial-based predictions. However, multihead GAT with spatial networks delivered 77% for insulation tasks, overtaking 67% of contractual networks, underscoring model sensitivity to task dependencies and its applicability across a range of decision making contexts. Recognizing the dependencies and shared aspects among construction records, the proposed GAT model better reflects human understanding of construction progress reports, shifting the focus from mere predictive accuracy to representative modeling of construction delay.