ABSTRACT

Transverse joint faulting is a common distress that develops in unbonded concrete overlays (UBOLs). In the previous predictive faulting model for UBOLs, artificial neural networks (ANNs) were trained to predict the critical response (deflections) resulting from traffic and environmental loads. These ANNs were trained using an extensive factorial of finite element runs of a field-validated structural model. While the ANNs were developed to predict the critical response of UBOLs with asphalt and fabric interlayers, the predictive models were not able to differentiate the critical response as a function of interlayer type. In this study, the ANNs were improved to address this prior limitation. A separate set of ANNs was produced for UBOLs with asphalt interlayers and UBOLs with fabric interlayers. Several enhancements were also performed, including modification of the ANN architecture and the inclusion of an extensive sensitivity analysis in the validation process. The newly developed ANNs are incorporated into the Pitt UBOL-ME faulting prediction model and design guide.

摘要

横向接头断层是在未粘合混凝土覆盖层 (UBOL) 中发展的常见问题。在之前的 UBOL 预测故障模型中，人工神经网络 (ANN) 被训练来预测交通和环境负荷导致的关键响应（偏转）。这些人工神经网络是使用经过现场验证的结构模型的有限元运行的广泛阶乘进行训练的。虽然开发了人工神经网络来预测具有沥青和织物夹层的 UBOL 的临界响应，但预测模型无法区分临界响应作为夹层类型的函数。在这项研究中，人工神经网络被改进以解决这个先前的限制。为具有沥青夹层的 UBOL 和具有织物夹层的 UBOL 生成了一组单独的 ANN。还进行了一些增强，包括修改 ANN 架构和在验证过程中包含广泛的敏感性分析。新开发的人工神经网络被纳入 Pitt UBOL-ME 故障预测模型和设计指南。