首届国际工程智能大赛赛题解读抢先看！
土木工程赛道：桥梁结构关键构件智能网格生成挑战赛
Civil Engineering Track：Intelligent Mesh Generation Challenge for Key Components of Bridge Structures
在“交通强国”与“新基建”战略背景下，桥梁工程的数字化与智能化转型正在加速。建立高保真度的结构数字模型，是实现桥梁全生命周期性能预测与安全保障的基础。此流程中的核心技术之一，便是有限元分析（Finite Element Analysis, FEA）。
Under the strategic background of "Transportation Power" and "New Infrastructure Construction", the digital and intelligent transformation of bridge engineering is accelerating. Establishing a high-fidelity structural digital model is the foundation for realizing the full-life-cycle performance prediction and safety guarantee of bridges. One of the core technologies in this process is Finite Element Analysis (FEA).
然而，在连接建筑信息模型（BIM）与有限元分析（FEA）的工作流中，有限元网格划分环节长期以来是制约分析效率与可靠性的主要瓶颈。这一过程占据了整个分析流程超过40%的时间，工程师需要投入大量精力，依靠个人经验对桥梁的关键复杂构件（如钢-混结合段、预应力锚固区、异形桥墩等）进行几何清理与参数化调整。高质量的网格是保证计算精度的前提，而质量不佳的网格可能导致计算无法收敛，甚至得出错误的分析结论，给工程安全带来隐患。
However, in the workflow connecting Building Information Modeling (BIM) and FEA, the finite element mesh generation link has long been a major bottleneck restricting analysis efficiency and reliability. This process accounts for more than 40% of the entire analysis workflow. Engineers need to invest a lot of energy to perform geometric cleaning and parametric adjustments on key complex components of bridges (such as steel-concrete composite sections, prestressed anchorage zones, special-shaped piers, etc.) relying on personal experience. High-quality meshes are a prerequisite for ensuring calculation accuracy, while poor-quality meshes may lead to non-convergent calculations or even incorrect analysis conclusions, posing hidden dangers to engineering safety.
这一挑战在重大基础设施的安全评估中尤为突出。以典型的城市立交桥为例，对其关键节点进行精细化受力分析，是保障其长期服役安全的核心步骤。分析不仅需要覆盖标准车辆荷载下的结构响应，更需涵盖地震、车辆撞击等极端灾害工况下的安全性校核。这些高精度分析的结果，是桥梁设计优化与运维加固决策的重要依据。然而，面对几何形态复杂、分析工况多样的任务，现有的网格划分工具难以实现自动化和最优化，其结果高度依赖人工干预，效率和质量一致性难以保证。
This challenge is particularly prominent in the safety assessment of major infrastructure. Taking a typical urban overpass as an example, refined stress analysis of its key nodes is a core step to ensure its long-term service safety. The analysis needs to cover not only the structural response under standard vehicle loads but also the safety verification under extreme disaster conditions such as earthquakes and vehicle collisions. The results of these high-precision analyses are important bases for bridge design optimization and operation-maintenance reinforcement decisions. However, when facing tasks with complex geometric shapes and diverse analysis conditions, existing mesh generation tools make it difficult to achieve automation and optimization, and their results are highly dependent on manual intervention, making it difficult to guarantee efficiency and quality consistency.
人工智能技术为此提供了新的解决思路。我们能否开发一种算法，使其不仅能解析桥梁构件的几何特征，更能根据结构力学原理，预测潜在的应力集中区域，从而自适应地生成最优计算网格？即在应力梯度大的区域自动加密网格，在其他区域采用较稀疏的网格，最终在保证计算精度的前提下，最大限度地提升计算效率。
Artificial intelligence technology provides a new solution. Can we develop an algorithm that not only parses the geometric features of bridge components but also predicts potential stress concentration areas based on structural mechanics principles, thereby adaptively generating optimal computational meshes? That is, automatically densifying meshes in areas with large stress gradients and using sparser meshes elsewhere, ultimately maximizing computational efficiency while ensuring calculation accuracy.
这正是本项挑战赛的核心任务。参赛者需要开发一个AI设计算法，该算法能直接处理给定的桥梁构件几何模型，并自主生成一套高质量的有限元网格方案。我们将通过一套标准化的后续有限元分析流程，从计算精度（与高密度基准网格的计算结果对比）和计算成本（单元数量、分析时长）两个维度，对算法生成的网格进行综合性能评估。
This is precisely the core task of this challenge. Participants need to develop an AI design algorithm that can directly process a given geometric model of bridge components and independently generate a high-quality finite element mesh plan. Through a standardized subsequent finite element analysis process, we will comprehensively evaluate the performance of the mesh generated by the algorithm from two dimensions: calculation accuracy (comparison with the calculation results of high-density benchmark meshes) and calculation cost (number of elements, analysis duration).
本挑战赛的目标是：研发能够服务于真实桥梁工程场景的智能化网格生成方案，将工程师从繁琐、重复的网格划分工作中解放出来，推动高保真结构分析走向高效、可靠与自动化的新阶段。
The goal of this challenge is to develop an intelligent mesh generation plan that can serve real bridge engineering scenarios, free engineers from tedious and repetitive mesh generation work, and promote high-fidelity structural analysis to a new stage of efficiency, reliability, and automation.
关于工程智能的更多宝藏秘籍请关注公众号“同济大学工程智能研究院”
English
