确定青路面状况的可行性从下降重量偏向测试和有限元模型更新来确定青路面状况
Yong Deng1, Yazhou Zhang2,3, Xianming Shi1
1National Center for Transportation Infrastructure Durability and Life-Extension (TriDurLE), Department of Civil and Environmental Engineering, Washington State University, Pullman, WA 99164, USA.
概括
人工智能通过更新有限元模型来增强非破坏性路面评估. 准确结果的关键因素包括响应灵敏度,数量和与测试进行平衡分析.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 由于复杂的材料属性 (时间,温度,应力依赖性),以非破坏性的方式确定内部路面状况具有挑战性.
- 这个反向问题需要从路面反应中校准材料和结构性质,往往成为一个高维优化问题.
研究的目的:
- 研究基于人工智能 (AI) 的有限元模型更新的可行性,以评估路面状况.
- 使用这种AI方法,专注于反向计算的路面性能的准确性和稳定性.
主要方法:
- 采用基于人工智能的有限元模型更新来解决路面状况决定的反向问题.
- 描述了代用模型,路面响应,优化算法和逆向计算方案的影响.
主要成果:
- 已确定应用路面对反向计算条件的反应的敏感性至关重要.
- 强调了使用的路面响应数量的重要性.
- 强调需要在反向计算条件和应用测试数据之间保持平衡.
结论:
- 基于人工智能的有限元模型更新显示了准确和稳定的路面状况评估的前景.
- 未来的研究应该专注于优化响应选择,并确保分析模型和现场测试之间的平衡,以提高性能.
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