Earthquake Damage Monitoring for Underground Structures Based Damage Detection Techniques

Transcription

1 IJR International Journal of Railway Vol. 7, No. 4 / December 2014, pp The Korean Society for Railway Earthquake Damage Monitoring for Underground Structures Based Damage Detection Techniques Jin Ho Kim and Na Eun Kim* Abstract Urban railway systems are located under populated areas and are mostly constructed for underground structures which demand high standards of structural safety. However, the damage progression of underground structures is hard to evaluate and damaged underground structures may not effectively stand against successive earthquakes. This study attempts to examine initial damage-stage and to access structural damage condition of the ground structures using Earthquake Damage Monitoring (EDM) system. For actual underground structure, vulnerable damaged member of Ulchiro-3ga station is chosen by finite element analysis using applied artificial earthquake load, and then damage pattern and history of damaged members is obtained from measured acceleration data introduced unsupervised learning recognition. The result showed damage index obtained by damage scenario establishment using acceleration response of selected vulnerable members is useful. Initial damage state is detected for selected vulnerable member according to established damage scenario. Stiffness degrading ratio is increasing whereas the value of reliability interval is decreasing. Keywords: Earthquake damage, Underground structures, Damage detection, Structural health monitoring, Unsupervised learning pattern recognition 1. Introduction Urban railway systems which are public transportation and are mostly constructed in metropolitan area are located underground. Therefore, underground structures demand high standards of structural safety. Potential risks owing to the occurrence of frequent small and medium scale earthquakes in domestic area gradually increase in the recent years, but appropriate safety net against earthquakes is insufficient for underground structures. Furthermore, the damage progression of underground structures is hard to evaluate and damaged underground structures may not effectively stand against successive earthquakes [1,3,9]. Also, vibration and shock induced by long time and repetitive train service make cumulative structural damage for underground * Corresponding author: Korea Railroad Research Institute, Korea ziminpa@krri.re.kr Korea Railroad Research Institute, Korea cthe Korean Society for Railway structures and require continuous damage detection. Though various researches related to structural health monitoring and structural integrity evaluation for earth structures progress actively, research cases for continuous monitoring and damage detection for underground structures are few and far between [2,6]. The purpose in this study is to check that pre-selected underground structure behave as intended and in good state. Among the different available damage diagnostic techniques, an outlier analysis has introduced as an unsupervised learning pattern recognition tool [2,3] for damage detection of the underground structures. For actual underground structure, vulnerable damaged member of Ulchiro-3ga station is chosen by finite element analysis using applied artificial earthquake load, and then damage pattern and history of damaged members can be obtained from measured acceleration data introduced statistical pattern recognition based on unsupervised learning recognition [2]. This paper attempts to examine the effectiveness of EDMS (Earthquake Damage Monitoring System) introduced statistical pattern recognition based on unsupervised learning recognition Vol. 7, No. 4 / December

2 Jin Ho Kim and Na Eun Kim / IJR, 7(4), 94-99, 2014 Fig. 1 Flowchart for unsupervised learning pattern for underground structures. 2. Earthquake Damage Monitoring System 2.1 Unsupervised learning pattern recognition EDMS can monitor the change in real time behavior of the structures during earthquake and it may provide an efficient monitoring tool for underground structures. In this study, as a structural detection methodology introduced unsupervised learning recognition is imposed to detect initial damage-stage and to access structural damage condition of the ground structures. The outlier analysis has become obvious as an unsupervised learning pattern recognition method for damage detection of the structures to satisfy this necessity. Also a damage index proposed using the acceleration response and it is applied outlier analysis, one of unsupervised learning based pattern recognition methods [3]. Limitation values for initial damage condition are determined based on reliability of the probabilistic distribution of the acceleration response. The introduced damage-detection algorithm depends on signals obtained at a specified location on of a structure. With current smart sensing capabilities that provide computational power at the sensor location, the algorithm can be embedded and executed at the data collection site [3]. The algorithm which is used in this study to evaluate structural health status of an underground structure is shown as Fig. 1. Damage index is defined by the equation (1) and can be obtained from acceleration data in normal using damage pattern. [2] 1 N DI i --- x 2 1 N = (1) N t = 0 i0 () t --- N x 2 t = 0 i () t Where, N is number of measured data, x i0 (t) is normal Fig. 2 Component of health monitoring system based on USN acceleration time series by the i th accelerometer, and x i (t) is current acceleration time series by the i th accelerometer. 2.2 Damage monitoring system based on ubiquitous sensor network USN (Ubiquitous Sensor Network) which is fundamental unit to compose wireless communication is mainly used to perceive structural condition or surrounding environment at close range. A large sensor network node can be easily installed at vulnerable area that is difficult access. Main technologies of USN are composed of sensor network technologies such as wireless personal area network, subminiature aperture satellite network equipment, and etc. Fig. 2 shows component of structural health monitoring system based on USN, and transmission and reception is achieved for data and control command by bidirectional local area network between sensor node and gateway. 3. Application Results and Validation 3.1 Damage scenario establishment and numerical analysis To evaluate structural health-state of ground structure, Ulchiro-3ga station is pre-selected. Damage scenario establishment and numerical analysis are performed to select accelerometer and its measurement place selection. Structure system and ground simultaneously is considered for numerical simulation using ABAQUS program as shown in Fig. 3. To simulate the model which includes 95

3 Earthquake Damage Monitoring for Underground Structures Based Damage Detection Techniques Fig. 5 Selection of vulnerable member for Ulchiro-3ga station Fig. 3 Finite element model for Ulchiro-3ga station Fig. 6 Comparison of 1st mode shape between intact and damaged condition Fig. 4 Seismic analysis result for Ulchiro-3ga station concrete and its surrounding ground, it is assumed that there are no changes for ground properties, and reinforced concrete properties based on nonlinear behavior of concrete. Eight nodal volume element as concrete members and ground are considered for finite element modeling, and infinite stiffness element is applied to boundary condition of modeled ground to consider continuity of ground. Hachinohe earthquake is applied to select vulnerable members of Ulchiro-3ga station induced by stress concentration, and damage scenario is established considering Fig. 7 Comparison of 19st mode shape between intact and damaged condition peripheral equipment or measurement condition to install accelerometers. Fig. 4 shows the result of earthquake response analysis, and 4th column as shown in Fig. 5 is selected as main vulnerable member because its stress 96

4 Jin Ho Kim and Na Eun Kim / IJR, 7(4), 94-99, 2014 change amount is greatly increased. Measurable frequency range depending on resonant frequency of structure system is determined through selection of accelerometer. Thus mode analysis is performed to choose the sensor having appropriate sensitivity. To figure out the dynamic influence of global system because of the damaged local member, damage mode of structure system having 40% stiffness degraded column is simulated according to damage scenario. Fig. 6 shows intact and damaged condition at 1st mode for the ground structure, respectively. It is observed that mode analysis results of intact and damaged condition show similarity. Mode shapes induced by structural damage are observed more than 19th mode as shown in Fig. 7. Resonant frequency is calculated Hz for good condition and 9.862Hz for damaged condition, respectively. 3.2 Unsupervised learning data construction Fig. 8 shows node set to select locations for acceleration measurement. Two accelerometers on the vulnerable column, seven accelerometers to examine influence caused by damage on the ceiling and eight accelerometers on the Fig. 8 Selected node set and sensor location Fig. 9 Installation plan and site of accelerometers at Ulchiro3ga station Fig. 10 Changing pattern of obtained acceleration response for damaged column Fig. 11 Detection results for damaged column 97

5 Earthquake Damage Monitoring for Underground Structures Based Damage Detection Techniques and 32 exceed threshold mean initial damage state. However, when stiffness degrading ratio is 30% and 40%, Fig 11 (b) and (c) show reasonable results which exceed threshold only for damaged column. 4. Conclusion Fig. 12 Establishment of threshold floor are installed. The installation plan and site which present acceleration measurement locations for data construction of EDM system are shown in Fig Application results Numerical analysis introduced by unsupervised learning pattern recognition is carried out to examine usability of it into the structural health-condition assessment of ground structures [2]. Vibration mode under operational condition is simulated for train stop on the platform using input load caused by measured acceleration data. Changing pattern of obtained acceleration data for the damaged column which is having 0%, 10%, 20%, and 40% stiffness degraded are shown in Fig. 10. When damaged condition or stiffness degrading ratio is increasing, it is observed that the acceleration amplitude difference between good condition and damaged state is bigger as shown in Fig. 11. Also threshold can be determined from probability distribution using cumulative probability of damage index as shown in Fig. 12. The calculation results using damage index and acceleration response yield the same conclusion; stiffness degrading ratio is increasing and damage index value for damaged column grow whereas damage index values for floor and ceiling are decreasing. When stiffness degrading ratio is 20%, Fig. 11 (a) shows wrong diagnosis which exceed threshold for ceil part and damage values of location 31 This study attempts to examine initial damage-stage and to access structural damage condition of the ground structures using EDMS. For actual underground structure, vulnerable damaged member of Ulchiro-3ga station is chosen by finite element analysis using applied artificial earthquake load, and then damage pattern and history of damaged members is obtained from measured acceleration data introduced statistical pattern recognition based on unsupervised learning recognition. The following conclusions can be obtained from this study. It is effective to select vulnerable member of ground structure and to utilize damage index from acceleration response obtained by damage scenario establishment and numerical analysis. Initial damage state is detected for selected vulnerable member according to established damage scenario, and this means the approach introduced unsupervised learning recognition to evaluate structural health-state of ground structures is appropriate. Stiffness degrading ratio is increasing whereas the vaule of reliabilty inteval is decreasing. However, it is observed that used methodology in this study is useful when stiffness degrading ratio is 40%, Acknowledgement This research is supported by the grant from Development of core technology and establishment of foundation for railway safety enhancement (PK B) funded by For Future Creation Ministry of Science of Korean government. References 1. Kim, J.H., Jang, Y.D., Jang, W.R. (2011). Intelligent Monitoring of Seismic Damage Identification using Wireless Smart Sensors-Design and Validation, Proc. of SPIE, Vol. 7984, pp Shin, J.R., An, T.K., Kim, J.H., Lee, C.G., Nam, M.J., and Park, S.H. (2011). Numerical Verification on the Structuralhealth Evaluation of Subway Stations based on Statistical Pattern Recognition Technique, Journal of Korea Society of Hazard Mitigation, Vol. 12, No. 2, April, pp Shin, J.R., An, T.K., Lee, C.G., and Park, S.H. (2011). Structural Health Monitoring Methodology based on Outlier Analysis using Acceleration of Subway Stations,

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