Intelligent Conceptual Design of Railway Bridge Based on Graph Neural Networks

Abstract

In the conceptual design stage of railway bridge, the beam type of the bridge at the main control point must be modified repeatedly to satisfy varying requirements. Thus, the demand for design efficiency is high. However, railway bridge design relies heavily on professional knowledge and experience and is typically completed manually by senior designers, thereby requiring considerable time. An intelligent beam type recommendation algorithm named AutoDis Graph Ontology Attention Matching (AGOAM) is proposed to rapidly generate bridge design plans for railway route main control points. This method acquires the node embeddings of the main control point and beam type attribute graphs through graph neural networks (GNNs) and predicts the score of each beam type through graph matching technology. The beam type with the highest prediction score is recommended. In addition, the accuracy of the recommendation results is improved through ontology-enhanced attribute interaction and attention mechanism-based graph pooling. The efficiency of the proposed method is demonstrated with a real-world railway bridge design dataset, and ablation study is conducted to evaluate the effectiveness of the ontology-enhanced attribute interaction and attention mechanism-based graph pooling.