Cascaded Deep Learning Network for Postearthquake Bridge Serviceability Assessment

Damages assessment of bridges is important to derive immediate response after severe events to decide serviceability. Especially, past earthquakes have proven the vulnerability of bridges with insufficient detailing. Due to lack of a national and unified post-earthquake inspection procedure for bridges, conventional damage assessments are performed by sending professional personnel to the onsite, detecting visually and measuring the damage state. To get accurate and fast damage result of bridge condition is important to save not only lives but also costs.There have been studies using image processing techniques to assess damage of bridge column without sending individual to onsite. Convolutional neural networks (CNNs) have shown state-of-art results in object detection and image classification tasks. This study proposed cascaded deep learning network for post-earthquake bridge serviceability assessment. Major target deficiency components (crack, spalled area, transverse bar, and longitudinal bar) were used to determine the proposed damage states to assess serviceability of bridge. Cascaded network is composed by Mask R-CNN and MobileNet v2 which have been proved as powerful network for each instance segmentation and image classification.In this study, proposed network successfully detected target deficiency components and measured each damage state by following 5 stages. Column area is detected as first step, and counting exposed bars, finding maximum distance in spalled region within column area are followed to decide damage state. To determine deficiency of crack in bridge column, crack patch classification module is attached in proposed network. Counting diagonal and horizontal cracks with angle measurement are used to analyze type of cracks

Post-earthquake Serviceability Assessment of RC Bridge Columns Using Computer Vision

Modern seismic design codes ensure a large displacement capacity and prevent total collapse for bridges. However, this performance objective is usually attained at the cost of damage to target ductile members. For reinforced concrete (RC) bridges, the columns are usually the main source of ductility during an earthquake in which concrete cover, core, and reinforcement may damage, and the column may experience a large permanent lateral deformation. A significant number of the US bridges will experience large earthquakes in the next 50 years that may result in the bridge closure due to excessive damage. A quick assessment of bridges immediately after severe events is needed to maximize serviceability and access to the affected sites, and to minimize casualties and costs. The main goal of this project was to accelerate post-earthquake RC bridge column assessment using \u201ccomputer vision\u201d. When sending trained personnel to the affect sites is limited or will take time, local personnel equipped with an assessment software (on various platforms such as mobile applications, cloud-based tools, or built-in with drones) can be deployed to evaluate the bridge condition. The project in this phase was focused on the damage assessment of modern RC bridge columns after earthquakes. Substandard columns, other bridge components, and other hazards were not included