A data‐driven approach for regional bridge condition assessment using inspection reports

Abstract

Summary Bridges play an important role in the highway transportation network. There is an increasing concern that highway bridges have been suffering from structural degradation and deficiency due to severe environment, overloading, initial structural defects, and other factors. Ensuring the safety of a large number of regional bridges at the network level becomes a major challenge. This study proposes an entire data‐driven condition assessment framework for network‐level bridges, including data integration, condition assessment, and maintenance management. The periodic bridge inspection reports in China could provide quite a few condition‐related information to reveal the time‐variant bridge condition deterioration and effect of maintenance behaviors. The proposed framework is applied to a real highway bridge network located in Hebei province, China. Inspection data are obtained from thousands of bridges over quite a few years. The established regional deterioration model could effectively predict the bridge future condition based on the extracted hidden features. The regional maintenance strategies are optimized to satisfy the economic and performance constraints, which allows bridge managers to select the most appropriate one for implementation. It is shown that the proposed data‐driven approach can provide a guideline to bridge managers to estimate the future condition and allocate the maintenance resources.

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

“…Li et al [ 40 ] used narrative descriptions in bridge inspection reports as data sources and proposed a data-driven framework to support automatic condition recommendation and real-time quality control. Xia et al [ 41 ] proposed a data-driven bridge condition assessment framework to effectively predict the future condition of bridges. Although the research in related fields can provide reference for the field of bridge management, the existing methods cannot be directly applied to the question answering task of bridge management due to the strong field characteristics.…”

A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan

“…An example of such studies includes the work of Li et al (2021) wherein they used the descriptive text from more than 8000 inspection reports to develop a neural network model aimed at promoting consistency and reliability in bridge condition assessment among inspectors [19]. Xia et al (2021) developed a fully data-driven, regionallevel bridge maintenance management framework using data extracted from inspection reports [22]. Lei et al (2022) developed a framework for regional-level bridge maintenance planning based on deep reinforcement learning using data accumulated from years of bridge-inspection reports [23].…”

Two Dimensional Finite Difference Model with a Singularity Attenuation Factor for Structural Health Monitoring of Single Lap Joints

“…When the application of the adhesives is related to structural parts demanding in terms of strength and/or fatigue requirements, the concern of the structural monitoring of the bonding integrity can rise [1][2][3][4]. Structural health monitoring (SHM) has received growing interest with multiple applications oriented to the development of damage/defect detection systems able to allow the transition from scheduled maintenance to condition-based maintenance in several areas [5][6][7][8]. An efective SHM method is the result of a reliable mathematical description of the structural part under control and the subsequent implementation of this model into a monitoring technique [9][10][11].…”