This paper presents a convolutional neural network to automatically conduct the peak picking in frequency domain of structural responses. The peaks in frequency domain have a high potential to be the natural frequencies, which are one of the important indicator to be used for structural health monitoring purposes, such as damage detection, cable tension estimation, and finite element model updating. In general, the peaks with the corresponding natural frequencies are manually selected by the users from the frequency domain. Although this previous approach is possible to simply extract the candidate of natural frequencies, it is inappropriate in the practical applications of the long-term monitoring and the implementation for wireless smart sensor. To overcome the drawbacks, this study proposes the convolutional neural network that can automatically identify the peaks with the corresponding natural frequencies from the frequency domain of structural responses.

This study proposes a flood fragility analysis of bridges for considering debris impacts. The reliability analysis is conducted using the first-order reliability method (FORM) with an finite element model of a bridge column built in ABAQUS. A fragility curve of the bridge column is obtained based on the procedure described in this study.

In countries that suffer from heavy rain such as Korea, bridges have to be prepared for a sudden water level increase. However, research on the flood risk assessment for bridges has gained less attention than earthquakes, even though one of the major causes of bridge failures has been reported to be flood. In addition, various sources of uncertainty make it challenging to evaluate the flood fragility of a bridge, and there have been few studies on the flood fragility curve derivation for bridges. The present study proposes a new methodology employing finite element reliability analysis to derive flood fragility curve. In the proposed method, two software packages, ABAQUS and FERUM, are connected so that reliability analysis can be performed in conjunction with sophisticated finite element analysis flood fragility assessment. The method is applied a real bridge in Korea, and flood fragility curves are derived for multiple damage states.

Recently, an indirect displacement estimation method using data fusion of acceleration and strain (i.e., acceleration-strain-based method) has been developed. This paper proposes an improved displacement estimation method that can be applied to more general types of bridges by building the mapping using the finite element model of the structure. An experimental validation of the proposed method was carried out on a prestressed concrete girder bridge, and the method provides the best estimate for dynamic displacements.

Computer vision-based displacement measurement is regarded as an excellent alternative to conventional displacement measurement devices due to its convenient installation process with high accuracy. Based on the strong potential, this study proposes a computer vision approach for displacement measurement with enhanced field applicability. Main features of the proposed method are (1) robustness against adverse light conditions and (2) convenient camera installation that allows the camera to be arbitrarily placed. An adaptive image processing procedure is developed to overcome false identification of target markers that can be induced by strong lights. Location information of the identified markers are used to obtain displacement using the homography transformation, which allows the camera to be installed in any place as long as the target markers are in the line of sight. With these features, the computer vision approach is experimentally proven to be practical in field testing for measuring structural displacement.

This study proposes a FE model updating strategy based on data fusion of acceleration and angular velocity. The use of acceleration and angular velocity gives richer information than the sole use of acceleration, allowing the enhanced performance particularly in determining the boundary conditions. A numerical simulation is presented to demonstrate the proposed FE model updating approach using the data fusion.

Scouring of bridge foundation is one of the major cause of bridge failure. Scour can be defined as the excavation of foundation or other material from the bed and banks of streams, due to water flow. Scour monitoring is one of the major requirements to ensure bridge safety. There are some underwater instruments such as float-out devices which are used in scour monitoring. The available conventional underwater instruments are expensive and difficulties in maintenance. Thus vibration based monitoring techniques are emerging, this paper is one of such effort. This paper develops a vibration-based scour monitoring technique. The effect of sour on the vibration characteristics of pier is not significant at the early stages of scouring but significant changes in vibration characteristic can be identified during moderate level of scour. Thus this method can be used to identify and alert the safety of bridge prior its failure. An Extended Kalman filter is employed in this process. This paper numerically validates the monitoring capability of developed method over other vibrations based methods.

Assessment of surface cracks is important to ensuring the health of concrete structures. Traditional visual inspection processes are time-consuming and their performance depends heavily on the inspector’s skill and experience. In this paper, digital image processing techniques are employed to monitor the surface cracks in concrete. The automated processing method is proposed for further implementation to an flight drone.

SHM에서 변위는 구조물의 동적 특성을 파악하는 핵심정보다. 이를 구조물과 직접 접촉하여 자기장 변화를 전기적신호(LVDT), 직접적으로 측정하는 방안으로 LVDT, GPS, LDV Displacement is one of the most fundamental responses, containing useful information regarding dynamic behavior of a structure. Traditional displacement measurement devices such as LVDT have disadvantages in its high-cost and few options on installation place. For the sake of economic reason, vision-based displacement measurement systems using low-cost cameras have been developed, yet these approaches still have difficulties in finding appropriate camera positions; camera should be placed perpendicular to targets. This study presents a new vision-based displacement measurement system using the planar homography method that gives accurate displacement, allowing the camera to have an arbitrary angle toward target. The vision-based system is experimentally verified using a low-cost camera and a target with four circles.

This study proposes a FE model updating strategy based on data fusion of acceleration and angular velocity. The use of acceleration and angular velocity gives richer information than the sole use of acceleration, allowing the enhanced performance particularly in determining the boundary conditions. A numerical simulation is presented to demonstrate the proposed FE model updating approach using the data fusion.

System identification was performed on the swing span of a steel truss bridge using a wireless sensor network. The swing span can rotate 360° to allow river traffic to pass through the locks located under the bridge. The twenty-two wireless sensor nodes were installed on the span to measure synchronized tri-axial acceleration. Modal properties of the bridge according to its different positions were successfully obtained using frequency domain decomposition method, and compared with the ones from the FE model of the bridge.

Displacement is one of the most fundamental responses, containing useful information regarding dynamic behavior of a structure. Traditional displacement measurement devices such as LVDT have disadvantages in their high cost and few options on installation locations. For the sake of economic reason, vision-based displacement measurement systems using low cost cameras have been developed, yet these systems still have difficulties in finding appropriate camera positions; camera should be placed perpendicular to targets. This study presents a new vision-based displacement measurement system using the planar homography method that results in accurate displacement measurement, allowing the camera to face targets with an arbitrary angles. The proposed vision-based system was experimentally validated from a laboratory test.