Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA.
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Sensors (Basel). 2023 Apr 22;23(9):4192. doi: 10.3390/s23094192.
The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state's databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities.
联邦公路管理局 (FHWA) 要求每半年对美国所有桥梁进行一次检查，以评估其状况。这些检查记录在美国国家桥梁清单 (NBI) 和各自州的数据库中，以便管理、研究和分析这些数据。随着 FHWA 规范变得更加复杂，检查需要更多的培训和现场时间。最近，增加了元素级检查，为桥梁中的每个小元素分配一个状况状态。为了满足这一新要求，使用人工智能 (AI) 开发了一种机器辅助桥梁检查方法来协助检查员。所提出的方法侧重于评估钢筋混凝土桥面板元素中裂缝的状况状态。基于深度学习的工作流程结合了图像分类和语义分割方法，用于从图像中提取信息，并根据 FHWA 规范评估裂缝的状况状态。新的工作流程使用深度神经网络提取桥梁检查手册所需的信息，从而能够确定桥面裂缝的状况状态。实验结果表明，该工作流程在此应用中的有效性。该方法还平衡了与增加人工智能参与程度相关的成本和风险，使检查员能够更好地管理其资源。这种基于人工智能的方法可以由资产所有者（如运输部）实施，以更好地为社区服务。
