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Analysis of highway bridges using computer-assisted modeling, neural networks, and data compression techniques.

机译：使用计算机辅助建模，神经网络和数据压缩技术分析公路桥梁。

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By making use of modern computing facilities, it is now possible to routinely apply finite element analysis (FEA) techniques to the analysis of complex structural systems. While these techniques may be successfully applied to the area of highway bridge analysis, there arise certain considerations specific to bridge analysis that must be addressed.; To properly analyze bridge systems for rating purposes, it is necessary to model each distinct structural stage of construction. Also, due to the nature of moving vehicular loading, the modeling of such loads is complex and cumbersome. To address these issues, computer assisted modeling software has been developed that allows an engineer to easily model both the construction stages of a bridge and complex vehicular loading conditions.; Using the modeling software an engineer can create large, refined FEA models that otherwise would have required prohibitively large quantities of time to prepare manually. However, as the size of these models increases so does the demand on the computing facilities used to perform the analysis. This is especially true in regard to temporary storage requirements and required execution time.; To address these issues a real time lossless data compression strategy suitable for FEA software has been developed, implemented, and tested. The use of this data compression strategy has resulted in dramatically reduced storage requirements and, in many cases, also a significant reduction in the analysis execution time. The latter result can be attributed to the reduced quantity of physical data transfer which must be performed during the analysis.; In a further attempt to reduce the analysis execution time, a neural network has been employed to create a domain specific equation solver. The chosen domain is that of two-span flat-slab bridges. A neural network has been trained to predict displacement patterns for these bridges under various loading conditions. Subsequently, a preconditioned conjugate gradient equation solver was constructed using the neural network both to seed the solution vector and to act as a preconditioner. Results are promising but further network training is needed to fully realize the potential of the application.
机译：通过使用现代计算设备，现在可以将有限元分析（FEA）技术常规应用于复杂结构系统的分析。虽然这些技术可以成功地应用于公路桥梁分析领域，但对于桥梁分析存在一些必须解决的注意事项。为了正确评估桥梁系统以进行评级，有必要对建筑的每个不同结构阶段进行建模。而且，由于移动车辆负载的性质，这种负载的建模是复杂且麻烦的。为了解决这些问题，已经开发了计算机辅助建模软件，该软件允许工程师轻松地对桥梁的施工阶段和复杂的车辆载荷条件进行建模。工程师可以使用建模软件来创建大型的，精炼的FEA模型，否则将需要大量的时间进行手动准备。但是，随着这些模型的大小增加，对用于执行分析的计算设备的需求也随之增加。关于临时存储要求和所需的执行时间，尤其如此。为了解决这些问题，已经开发，实施和测试了适用于FEA软件的实时无损数据压缩策略。这种数据压缩策略的使用大大减少了存储需求，并且在许多情况下还大大减少了分析执行时间。后一个结果可以归因于必须在分析期间执行的减少的物理数据传输量。在减少分析执行时间的进一步尝试中，采用了神经网络来创建特定领域的方程求解器。选择的域是两跨平板桥的域。已经训练了神经网络来预测这些桥在各种载荷条件下的位移模式。随后，使用神经网络构造了预处理的共轭梯度方程求解器，以播种解矢量并充当预处理器。结果是有希望的，但是需要进一步的网络培训才能完全实现应用程序的潜力。

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