With the informatization of the building and infrastructure industry, conventional analysis methods are gradually proving inadequate in meeting the demands of the new era, such as intelligent synchronization and real-time simulation. Artificial intelligence (AI) technology has emerged as a promising alternative due to its high expressiveness, efficiency, and scalability. This has given rise to a new research field of AI-based computation in civil engineering. In this study, a state-of-the-art review of the research on material and structural analyses using AI technology in civil engineering was performed to provide a general introduction to the current progress. The research was classified into static feature studies, dynamic feature studies, and composite feature studies according to the problem inputs. The general methodology, commonly used AI models, and representative applications of each research category were elaborated. On these bases, the strengths and weaknesses of current studies were discussed. To demonstrate the accuracy and efficiency of AI models in comparison with conventional numerical methods, a concrete example of an end-to-end deep learning framework for structural analysis was highlighted. Finally, we suggested four open problems from the perspective of engineering applications, indicating the major challenges and future research directions regarding AI-based computational analysis in civil engineering.

随着建筑和基础设施行业的信息化，传统的分析方法逐渐不能满足智能同步、实时仿真等新时代的需求。人工智能 (AI) 技术因其高表现力、效率和可扩展性而成为一种有前途的替代方案。这催生了土木工程中基于人工智能的计算的新研究领域。在本研究中，对土木工程中使用 AI 技术进行材料和结构分析研究的最新进展进行了回顾，以提供对当前进展的一般介绍。根据问题输入，研究分为静态特征研究、动态特征研究和复合特征研究。一般方法，阐述了常用的人工智能模型，以及每个研究类别的代表性应用。在此基础上，讨论了当前研究的优缺点。为了证明 AI 模型与传统数值方法相比的准确性和效率，重点介绍了用于结构分析的端到端深度学习框架的具体示例。最后，我们从工程应用的角度提出了四个开放性问题，指出了土木工程中基于人工智能的计算分析的主要挑战和未来的研究方向。为了证明 AI 模型与传统数值方法相比的准确性和效率，重点介绍了用于结构分析的端到端深度学习框架的具体示例。最后，我们从工程应用的角度提出了四个开放性问题，指出了土木工程中基于人工智能的计算分析的主要挑战和未来的研究方向。为了证明 AI 模型与传统数值方法相比的准确性和效率，重点介绍了用于结构分析的端到端深度学习框架的具体示例。最后，我们从工程应用的角度提出了四个开放性问题，指出了土木工程中基于人工智能的计算分析的主要挑战和未来的研究方向。