Study of Building Safety Monitoring by Using Cost-Effective MEMS Accelerometers for Rapid After-Earthquake Assessment with Missing Data

Suffering from structural deterioration and natural disasters, the resilience of civil structures in the face of extreme loadings inevitably drops, which may lead to catastrophic structural failure and presents great threats to public safety. Earthquake-induced extreme loading is one of the major reasons behind the structural failure of buildings. However, many buildings in earthquake-prone areas of China lack safety monitoring, and prevalent structural health monitoring systems are generally very expensive and complicated for extensive applications. To facilitate cost-effective building-safety monitoring, this study investigates a method using cost-effective MEMS accelerometers for buildings’ rapid after-earthquake assessment. First, a parameter analysis of a cost-effective MEMS sensor is conducted to confirm its suitability for building-safety monitoring. Second, different from the existing investigations that tend to use a simplified building model or small-scaled frame structure excited by strong motions in laboratories, this study selects an in-service public building located in a typical earthquake-prone area after an analysis of earthquake risk in China. The building is instrumented with the selected cost-effective MEMS accelerometers, characterized by a low noise level and the capability to capture low-frequency small-amplitude dynamic responses. Furthermore, a rapid after-earthquake assessment scheme is proposed, which systematically includes fast missing data reconstruction, displacement response estimation based on an acceleration response integral, and safety assessment based on the maximum displacement and maximum inter-story drift ratio. Finally, the proposed method is successfully applied to a building-safety assessment by using earthquake-induced building responses suffering from missing data. This study is conducive to the extensive engineering application of MEMS-based cost-effective building monitoring and rapid after-earthquake assessment.

[1]  Hao Sun,et al.  Incremental Bayesian matrix/tensor learning for structural monitoring data imputation and response forecasting , 2021 .

[2]  Wei Fan,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[3]  Xinqun Zhu,et al.  Structural damage detection from wavelet packet sensitivity , 2005 .

[4]  Vincenzo Gattulli,et al.  Long-term structural monitoring of the damaged Basilica S. Maria di Collemaggio through a low-cost wireless sensor network , 2015 .

[5]  Ting-Yu Hsu,et al.  Evaluating Post-Earthquake Building Safety Using Economical MEMS Seismometers , 2018, Sensors.

[6]  You‐lin Xu,et al.  SHM-Based Seismic Performance Assessment of High-Rise Buildings under Long-Period Ground Motion , 2019, Journal of Structural Engineering.

[7]  Yi-Qing Ni,et al.  Bayesian multi-task learning methodology for reconstruction of structural health monitoring data , 2018, Structural Health Monitoring.

[8]  Giancarlo Pastor,et al.  A Low-Rank Tensor Model for Imputation of Missing Vehicular Traffic Volume , 2018, IEEE Transactions on Vehicular Technology.

[9]  Giuseppe D'Anna,et al.  Urban MEMS based seismic network for post-earthquakes rapid disaster assessment , 2014 .

[10]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[11]  Budhaditya Hazra,et al.  Online damage detection using recursive principal component analysis and recursive condition indicators , 2017 .

[12]  Soobong Shin,et al.  Structural system identification in time domain using measured acceleration , 2005 .

[13]  Yi-Qing Ni,et al.  Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data , 2018, Journal of Structural Engineering.

[14]  Angelos Amditis,et al.  MEMS-based sensors for post-earthquake damage assessment , 2011 .

[15]  Daniel J. Inman,et al.  TIME DOMAIN ANALYSIS FOR DAMAGE DETECTION IN SMART STRUCTURES , 1997 .

[16]  Tommy H.T. Chan,et al.  Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: A feasibility study , 2015 .

[17]  Hyo Seon Park,et al.  A Wireless MEMS-Based Inclinometer Sensor Node for Structural Health Monitoring , 2013, Sensors.

[18]  Yi-Qing Ni,et al.  A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment , 2018, Comput. Aided Civ. Infrastructure Eng..

[19]  X. Y. Li,et al.  Structural Damage Detection from Wavelet Coefficient Sensitivity with Model Errors , 2006 .

[20]  You-Lin Xu,et al.  Structural Health Monitoring of Long-Span Suspension Bridges , 2011 .

[21]  Prashanth Ragam,et al.  Application of MEMS-based accelerometer wireless sensor systems for monitoring of blast-induced ground vibration and structural health: a review , 2019, IET Wirel. Sens. Syst..

[22]  Chien-Chih Chen,et al.  How Well Can We Extract the Permanent Displacement from Low-Cost MEMS Accelerometers? , 2017, Sensors.

[23]  Siu-Seong Law,et al.  Structural Damage Diagnosis-Oriented Impulse Response Function Estimation under Seismic Excitations , 2019, Sensors.

[24]  Antonino D'Alessandro,et al.  A Review of the Capacitive MEMS for Seismology , 2019, Sensors.

[25]  C. Yun,et al.  Probabilistic principal component analysis‐based anomaly detection for structures with missing data , 2021, Structural Control and Health Monitoring.

[26]  You-Lin Xu,et al.  Multistage damage detection of a transmission tower: Numerical investigation and experimental validation , 2019, Structural Control and Health Monitoring.

[27]  B. Spencer,et al.  SMART SENSING TECHNOLOGY FOR STRUCTURAL HEALTH MONITORING , 2002 .

[28]  Norris Stubbs,et al.  Damage identification in structures using the time-domain response , 2004 .

[29]  Yurii Nesterov,et al.  Smooth minimization of non-smooth functions , 2005, Math. Program..

[30]  Vikram Pakrashi,et al.  First-Order Eigen-Perturbation Techniques for Real-Time Damage Detection of Vibrating Systems: Theory and Applications , 2019, Applied Mechanics Reviews.

[31]  Bingru Yang,et al.  A SVM Regression Based Approach to Filling in Missing Values , 2005, KES.

[32]  Jian Cai,et al.  Calculation Methods for Inter-Story Drifts of Building Structures , 2014 .

[33]  X. Y. Li,et al.  Structural Condition Assessment from White Noise Excitation and Covariance of Covariance Matrix , 2012 .

[34]  Ting-Yu Hsu,et al.  Application of the low-cost MEMS-type seismometer for structural health monitoring: A pre-study , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[35]  S. S. Law,et al.  Condition Assessment of Structures Under Ambient White Noise Excitation , 2008 .

[36]  Billie F. Spencer,et al.  Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors , 2018, Sensors.

[37]  Alessandro Sabato,et al.  Wireless MEMS-Based Accelerometer Sensor Boards for Structural Vibration Monitoring: A Review , 2017, IEEE Sensors Journal.

[38]  Y. L. Xu,et al.  Two-Stage Covariance-Based Multisensing Damage Detection Method , 2017 .

[39]  Vikram Pakrashi,et al.  Real time damage detection using recursive principal components and time varying auto-regressive modeling , 2018 .

[40]  Vikram Pakrashi,et al.  Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis , 2019 .

[41]  Paola Pierleoni,et al.  Performance Evaluation of a Low-Cost Sensing Unit for Seismic Applications: Field Testing During Seismic Events of 2016-2017 in Central Italy , 2018, IEEE Sensors Journal.

[42]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[43]  Feng Gao,et al.  A novel time-domain auto-regressive model for structural damage diagnosis , 2005 .

[44]  Maria Q. Feng,et al.  Citizen Sensors for SHM: Towards a Crowdsourcing Platform , 2015, Sensors.

[45]  Yih-Min Wu,et al.  A High-Density Seismic Network for Earthquake Early Warning in Taiwan Based on Low Cost Sensors , 2013 .

[46]  B. F. Spencer,et al.  Development of a High-Sensitivity Wireless Accelerometer for Structural Health Monitoring , 2018, Sensors.

[47]  Yingfeng Cai,et al.  Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation , 2017, Knowl. Based Syst..

[48]  Lelitha Vanajakshi,et al.  Short-term traffic flow prediction using seasonal ARIMA model with limited input data , 2015, European Transport Research Review.

[49]  Siu-Seong Law,et al.  Fusion of structural damage identification results from different test scenarios and evaluation indices in structural health monitoring , 2020 .

[50]  Salvatore Stramondo,et al.  Urban Seismic Networks, Structural Health and Cultural Heritage Monitoring: The National Earthquakes Observatory (INGV, Italy) Experience , 2019, Front. Built Environ..

[51]  Zhenhong Du,et al.  A matrix completion-based multiview learning method for imputing missing values in buoy monitoring data , 2019, Inf. Sci..

[52]  D. Losanno,et al.  Structural monitoring system for the management of emergency due to natural events , 2013, 2013 IEEE Workshop on Environmental Energy and Structural Monitoring Systems.

[53]  James Caverlee,et al.  Tensor Completion Algorithms in Big Data Analytics , 2017, ACM Trans. Knowl. Discov. Data.

[54]  A. Kibangou,et al.  Traffic data imputation via tensor completion based on soft thresholding of Tucker core , 2017 .

[55]  Vikram Pakrashi,et al.  Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection , 2020 .

[56]  E. Cochran,et al.  A novel strong-motion seismic network for community participation in earthquake monitoring , 2009, IEEE Instrumentation & Measurement Magazine.

[57]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[58]  Jiawei Wang,et al.  Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model , 2019, Transportation Research Part C: Emerging Technologies.

[59]  Lori Dengler,et al.  MEMS Accelerometer Mini-Array (MAMA): A Low-Cost Implementation for Earthquake Early Warning Enhancement , 2019, Earthquake Spectra.

[60]  Yuichiro Yamabe,et al.  Fundamental Tests on a Structural Health Monitoring System for Building Structures Using a Single-board Microcontroller , 2015 .

[61]  Yih-Min Wu,et al.  Progress on Development of an Earthquake Early Warning System Using Low-Cost Sensors , 2015, Pure and Applied Geophysics.

[62]  Xinjian Shan,et al.  Performance Evaluation of Low-Cost Seismic Sensors for Dense Earthquake Early Warning: 2018–2019 Field Testing in Southwest China , 2019, Sensors.

[63]  Andreas Krause,et al.  Community Seismic Network , 2012 .

[64]  Vikram Pakrashi,et al.  Robust linear and nonlinear structural damage detection using recursive canonical correlation analysis , 2020 .

[65]  Hoon Sohn,et al.  A Review of Structural Health Review of Structural Health Monitoring Literature 1996-2001. , 2002 .

[66]  Jian-fu Lin,et al.  Unit Impulse Response Estimation for Structural Damage Detection Under Planar Multiple Excitations , 2014 .

[67]  Qingkai Kong,et al.  MyShake: A smartphone seismic network for earthquake early warning and beyond , 2016, Science Advances.

[68]  Thomas H. Heaton,et al.  Structural Health Monitoring of Buildings Using Smartphone Sensors , 2018 .