As the number of old bridges increases, the number of bridges with structural defects is also increasing. Timely inspection and maintenance of bridges are required because structural degradation is accelerated after bridge damage. Recently, in the field of structural health monitoring, a bridge inspection using an unmanned aerial vehicle system (UAS) is receiving a lot of attention. In this paper, UAS-based automatic damage detection and bridge condition evaluation were performed on existing bridges. From the process of preparing for inspection to the management of inspection data, the entire bridge inspection process was performed through field tests. The necessary element techniques for each stage were explained and the results were confirmed. Finally, UAS-based results were compared with conventional human-based visual inspection results. As a result, it was confirmed that the UAS-based bridge inspection is faster and more objective than the existing technology. Therefore, it was confirmed that the automatic bridge inspection method based on unmanned aerial vehicles can be applied to the field as a promising technology.

Since all structures vibrate due to external loads, measuring and analyzing vibration data is a representative method of structural health monitoring. In this paper, we propose a non-contact cable estimation method using a vision sensor mounted on an unmanned aerial vehicle. A target cable among many cables can be identified through marker detection. In addition, the motion of the structure can be quickly captured using the extracted feature points. Although computer vision can be used to transform displacements of multiple axis, in this study, only the vertical displacement is considered to estimate tension. Finally, the cable tension can be estimated via the vibration method using the modal frequencies derived from the cable displacement. To verify the performance of the proposed method, lab-scale experiments were carried out and the results were compared with the conventional method based on the accelerometer. The proposed method showed a 3.54% error compared with the existing method and confirmed that the cable tension force can be estimated quickly at low cost.

This study aims to develop a decision-making support system for managing aged road facilities in a target road network based on seismic performance evaluation. For this purpose, the seismic fragility considering aging effect is analyzed for bridges, tunnels, retaining walls, and slopes to assess the direct damage to individual road facilities, as described in the companion paper. In this paper, based on the seismic fragilities of road facilities, the degradation of the road network’s seismic performance and social and economic resilience is evaluated. The decision support system is then developed based on the seismic risk assessment method (SRA) for the seismic management of old road facilities suitable for domestic conditions. The SRA method includes the calculation of direct and indirect damage of road networks, the assessment of socio-economic resilience to disaster in South Korea, and the basis for decision-making. In addition, a geospatial information-based software for repair and reinforcement decisions is developed. The developed decision-making support software is verified by using Pohang city located in the East part of Korea as a test-bed example.

Recently, frequent earthquakes have damaged bridge structures, causing great inconvenience owing to the disruption of transportation networks and increasing travel time. In this study, the restoration priority of bridges under seismic conditions was proposed and the seismic resilience curves were compared based on an artificial neural network model. To determine the restoration priority of bridges, 1) performance-based and 2) benefit-cost-based restoration priorities were introduced. For demonstration purposes, the actual Pohang road network was reconstructed and the seismic resilience curves of the transportation network corresponding to the proposed restoration priorities were compared. Numerical analysis showed that the performance-based restoration priority had a more rapid seismic resilience curve than benefit-cost-based restoration priority.