Data for \"CAIT-UTC-REG48: Linking Physics-Based Deterioration Model to Field-Based Condition Assessments for Improving Asset Management\"

Currently used asset management programs by various state departments of transportation (DOT) rely typically on field-based condition assessments (periodic inspections and deterioration curves) of bridges for planning repair and maintenance activities. However, field inspections are labor intensive and can be subjective. Furthermore, visual inspections rely on visible signs of deterioration on the exterior surface of a bridge (e.g., corrosion stains and cracks) and may miss severe localized deterioration hidden inside concrete, which could incur significant repair costs in the future. On the other hand, physics-based models rely on simulating the underlying deterioration mechanisms for predicting future deterioration. Several parameters used in physics-based models are often based on extrapolating laboratory data that is not representative of field conditions. Furthermore, the outputs of physics-based deterioration model (e.g., rebar area loss) cannot be translated to condition ratings and are therefore unusable in asset management programs of state DOTs. For addressing these limitations, this research aims to (1) determine a rational basis for linking the outputs of a physics-based corrosion model to field-based condition ratings, and (2) calibration of input parameters of physics-based corrosion model based on deterioration curves obtained from field-based assessments. This report presents a systematic procedure for achieving these two aims. An example bridge column owned by New York State DOT is used to demonstrate the procedure. Links between the surface crack width and spalling and bridge element condition rating were established based on the interpretation of condition ratings. Inputs of the corrosion model were calibrated by matching the crack widths and spalling interpreted from deterioration curves with the outputs of the physics-based corrosion model. This project is expected to improve asset management by supplementing the field-based condition assessments with physics-based deterioration models.当前美国各州交通运输部门（Department of Transportation，DOT）所采用的资产管理系统，通常依托桥梁现场状态评估（包括定期巡检与劣化曲线分析）来规划桥梁的修复与养护作业。然而，现场巡检工作强度大且带有主观性。此外，目视巡检仅能观测桥梁外表面可见的劣化迹象（如锈蚀斑痕与裂缝），可能遗漏隐藏在混凝土内部的严重局部劣化，进而在未来引发高额修复成本。而基于物理机理的模型则通过模拟底层劣化机理来预测未来劣化情况，但该类物理模型中的诸多参数通常基于实验室数据外推得到，无法反映现场实际工况。此外，物理劣化模型的输出结果（如钢筋截面积损失）无法直接转换为状态评级，因此无法应用于各州交通运输部门的资产管理系统中。为解决上述局限，本研究旨在达成两大目标：（1）确立将物理腐蚀模型的输出结果与现场状态评级相关联的合理依据；（2）基于现场评估得到的劣化曲线，对物理腐蚀模型的输入参数进行校准。本报告提出了一套可实现上述两大目标的系统化流程。研究选取纽约州交通运输部门所属的某桥梁桥墩作为示例，对该流程进行演示验证。研究基于对状态评级的解读，建立了表面裂缝宽度、混凝土剥落与桥梁构件状态评级之间的关联关系。通过将从劣化曲线中解读得到的裂缝宽度与混凝土剥落现象，与物理腐蚀模型的输出结果进行匹配，完成了腐蚀模型输入参数的校准工作。本项目有望通过将物理劣化模型作为现场状态评估的补充手段，优化资产管理工作。