Analysis of resistance factors for LRFD of soil nail pullout limit state using default FHWA load and resistance models

Resistance factors for load and resistance factor design (LRFD) of pullout limit state of both permanent and temporary soil nails are calibrated against a wide design space using the current Federal Highway Administration (FHWA) nail load and resistance models. The calculated resistance factors were shown to scatter broadly among design scenarios that differ in wall face batter, soil friction angle, nail ultimate bond strength, and surcharge live load. An important lesson learned from the analysis results is that the current practice of using a single resistance factor for LRFD of nail pullout limit state could not result in uniform reliabilities across different design scenarios. Simple artificial neural network (ANN) models were developed for computation of resistance factors. Design examples demonstrated the ability of the ANN models in providing resistance factors that yield satisfactory and consistent reliabilities in different nail pullout designs.

本研究依托现行美国联邦公路管理局（Federal Highway Administration, FHWA）发布的土钉荷载与抗力模型，在覆盖宽泛设计参数的空间内，对永久及临时土钉拔出极限状态的荷载与抗力系数设计法（Load and Resistance Factor Design, LRFD）抗力系数进行了校准。研究结果显示，计算得到的抗力系数在不同设计场景下呈现显著离散性，这些场景的差异变量包括墙面倾角、土体内摩擦角、土钉极限粘结强度以及附加活荷载。分析得到的一项重要结论为：当前土钉拔出极限状态LRFD采用单一抗力系数的工程实践，无法在各类设计场景中实现均匀一致的可靠度水平。本研究开发了简易人工神经网络（Artificial Neural Network, ANN）模型，用于抗力系数的计算。设计案例验证表明，该ANN模型能够生成可在各类土钉拔出设计中实现满意且一致可靠度的抗力系数。