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Relationship extraction is a fundamental task in natural language processing, with applications ranging from knowledge graph construction to information retrieval. Existing entity-relationship joint extraction models have made significant strides in this field. However, they still face limitations in effectively utilizing interaction information between subjects and objects, as well as capturing the spatial location relationships between entities. In this paper, we propose a novel relationship extraction model that addresses these limitations. Our model introduces innovative techniques to harness interaction information between subjects and objects. We employ subject gates, object gates, entity gates, and relationship gates to partition and filter interaction information between relationship triples during the encoding phase. Additionally, we leverage an attention mechanism inspired by the visual domain to capture spatial location relationships between entities during the decoding phase, transforming the entity-relationship joint extraction task into a table-filling task. To evaluate the effectiveness of our model, we conducted extensive experiments on multiple datasets, including WebNLG, NYT, and ADE. Our model achieved impressive F1 values of 93.65%, 92.58%, and 86.16% on these datasets, respectively, outperforming state-of-the-art models.

Ultra-high performance concrete has excellent mechanical properties such as ultra-high strength and high durability, and has a broad application prospect in the field of bridge engineering. To make full use of the superior mechanical properties of UHPC and reduce the cost of the bridge, the particle swarm optimization algorithm is used to optimize the structural design of the UHPC beam bridge; Aiming at the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, the nonlinear adaptive weight update method is used to improve the particle swarm optimization algorithm. Based on the above-mentioned improved particle swarm optimization algorithm, the optimal design method of ordinary reinforced UHPC beams and prestressed UHPC beams with commonly used highway spans is studied. The reliability of the optimized structural limit state is analyzed by Monte Carlo (MC) importance sampling method. The results show that compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm has higher convergence speed and convergence accuracy. The optimal height-span ratio of ordinary reinforced UHPC beams decreases with the increase of the span, and the construction cost gradually increases; The optimal height-span ratio of the prestressed beam decreases first and then increases with the increase of the span, and the construction cost gradually increases. The calculation results of load capacity reliability indexes of optimization results are all higher than the target reliability indexes of similar components stipulated in China’s “Uniform Standard for Structural Reliability Design of Highway Engineering”.