This research introduces unfamiliarity index (UFI) that calculated from the FFT results of the short term timeline acceleration responses. If this algorithm, which can detect an abnormal behavior from the maximum constant signal, is used to the terminal sensors of a structure, more accurate safety control criteria will be prepared efficiently.
Recent frequent occurrence of urban sinkhole serves as a momentum of the periodic inspection of sewer pipelines. Sewer inspection using a CCTV device needs a lot of time and efforts. Many of previous studies which reduce the laborious tasks are mainly interested in the developments of image processing S/W and exploring H/W. And there has been no attempt to find meaningful information from the existing CCTV images stored by the sewer maintenance manager. This study adopts a cross-correlation based image processing method and extracts sewer inspection device’s location data from CCTV images. As a result of the analysis of location-time relation, it show strong correlation between device stand time and the sewer damages. In case of using this method to investigate sewer inspection CCTV images, it will save the investigator’s efforts and improve sewer maintenance efficiency and reliability.
Damage to infrastructure is a real concern at present, caused primarily by worldwide climate anomalies, global warming, and natural disasters. Korea has begun research to develop a high precision patch/implant system using new IT techniques since 2011 and technologies which must be developed for this research are those which measure and evaluate the soundness and safety of structures based on the measurements of an attached sensor. During the research period since 2011, optical fiber sensor patches and wireless sensor capsule implants along with various sensor technologies, stress sensing and structural condition evaluation algorithm have been developed effectively for network hardware technologies as prototype version. Similarly high precision image processing for automatic crack extraction have been developed along with radiation sensor application technologies, combined management/control technologies for developed systems, and practical technologies for building and large scale structure. Through the results, it is expected that we acquire higher sensor system performance with a measurement scope (for precision, etc.) goal at least 200% better than conventional sensor systems.
In the field of civil engineering, analyzing dynamic response was main concern for a long time. These analysis methods can be divided into moving load analysis method and moving mass analysis method, and formulating each an equation of motion has recently been studied after dividing vehicles and bridges. In this study, the numerical method is presented, which can consider the various train types and can solve the equations of motion for a vehicle-bridge interaction analysis by non-iteration procedure through formulating the coupled equations for motion. Also, 3 dimensional accurate numerical models was developed by KTX-vehicle in order to analyze dynamic response characteristics. The equations of motion for the conventional trains are derived, and the numerical models of the conventional trains are idealized by a set of linear springs and dashpots with 18 degrees of freedom. The bridge models are simplified by the 3 dimensional space frame element which is based on the Euler-Bernoulli theory. The rail irregularities of vertical and lateral directions are generated by PSD functions of the Federal Railroad Administration (FRA).
Proc. SPIE. 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016
KEYWORDS: Edge detection, Safety, Data modeling, Sensors, Fourier transforms, Buildings, Signal processing, Nonlinear optics, Structural health monitoring, Bridges, Signal analysis, Signal detection, Time-frequency analysis
The recent constructed structures are featured by the combination of their functions and shapes as well as by their enlarged dimensions, which increase the demand for Structural Health Monitoring technology. Since every structure bears unique dynamic characteristics and is exposed to diverse external forces, various methods are studied to monitor the health of the structure. This study applies the Hilbert-Huang transform, the variance analysis and the edge detection method on the acceleration response of the structure to identify the initiation time of the abnormal behavior in which the structure experiences abnormal vibration. A scaled cable-supported bridge model is fabricated and subjected to cable failure test from which data before and after the occurrence of the abnormal behavior are acquired and compared to validate the proposed anomaly-extraction technique.