ABSTRACT

Three-dimensional Finite Element (3D-FE) stress computations involved in the current rigid airport pavement design methodology, are time consuming when considering top-down cracking failure mode. In this study, Artificial Neural Network (ANN) models are integrated into a tool called ANNFAA to replace such 3D-FE computations. ANNFAA makes use of the best ANN models developed in MATLAB for 156 different airplanes without requiring any additional software installation or cumbersome learning of a new program. Within ANNFAA development, about 4,000 of 3D-FE simulations and many ANN models have been developed for each of these airplanes. Three useful tools were also developed using C# and MATLAB for implementing the 3D-FE analysis, post-processing the results, training the ANN models, and determining accuracy and performance of the ANN models. ANNFAA provides an accurate and rapid procedure for practitioners, engineers, and researchers for computing the critical stress responses associated with top-down cracking in multiple-slab rigid airfield pavements. This should make pavement design and analysis more practical, especially when a significantly large number of different cases that include top-down cracking failure mode are investigated. Also, this will help when currently used bottom-up cracking mode in the FAA standard rigid pavement design procedures is being considered in a design.

摘要

在考虑自上而下的开裂失效模式时，当前刚性机场路面设计方法中涉及的三维有限元 (3D-FE) 应力计算非常耗时。在这项研究中，人工神经网络 (ANN) 模型被集成到一个名为 ANNFAA 的工具中，以取代此类 3D-FE 计算。ANNFAA 利用 MATLAB 为 156 架不同的飞机开发的最佳 ANN 模型，无需任何额外的软件安装或繁琐的新程序学习。在 ANNFAA 开发过程中，为每架飞机开发了大约 4,000 个 3D-FE 模拟和许多 ANN 模型。还使用 C# 和 MATLAB 开发了三个有用的工具，用于实施 3D-FE 分析、后处理结果、训练 ANN 模型以及确定 ANN 模型的准确性和性能。ANNFAA 为从业人员、工程师和研究人员提供了一种准确、快速的程序，用于计算与多板刚性机场路面中自上而下开裂相关的临界应力响应。这应该使路面设计和分析更加实用，特别是在研究大量包括自上而下开裂破坏模式的不同情况时。此外，当在设计中考虑 FAA 标准刚性路面设计程序中当前使用的自下而上开裂模式时，这将有所帮助。尤其是当研究大量包括自上而下的开裂失效模式的不同情况时。此外，当在设计中考虑 FAA 标准刚性路面设计程序中当前使用的自下而上开裂模式时，这将有所帮助。尤其是当研究大量包括自上而下的开裂失效模式的不同情况时。此外，当在设计中考虑 FAA 标准刚性路面设计程序中当前使用的自下而上开裂模式时，这将有所帮助。