Abstract
This paper develops a decision making framework for post-earthquake assessment of instrumented buildings in a manner consistent with performance-based design criteria. This framework is achieved by simultaneously combining and advancing existing knowledge from seismic structural health monitoring and performance-based earthquake engineering paradigms. The framework consists of (1) measurement, (2) uncertainty modeling, (3) dynamic response reconstruction, (4) damage estimation, and (5) performance-based assessment and decision making. In particular, the main objective is to reconstruct inter-story drifts with a probabilistic measure of exceeding performance-based acceptance limits and determine the post-earthquake re-occupancy classification of the instrumented building of interest. Since the proposed framework is probabilistic, the outcome can be used to obtain the probability of losses based on the defined decision variables and be integrated into a risk-based decision making process by city officials, building owners, and emergency managers. The framework is illustrated using data from the Van Nuys hotel testbed, a seven-story reinforced concrete building instrumented by the California Strong Motion Instrumentation Program (CSMIP Station 24386).
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Abbreviations
- \(\mathrm{arg}\,\mathrm{min}\) :
-
Argument of the minimum
- \(\mathbf {b}_1\) :
-
Spatial distribution of excitation
- \(\mathbf {b}_2\) :
-
Spatial distribution of process noise
- \(c_2\) :
-
Output location matrix
- \(\mathbf {C}_\xi\) :
-
Damping matrix
- e :
-
State error
- \({\mathbb {E}}\) :
-
Expected value
- E :
-
Viscous damping coefficient
- \(\mathbf {E}\) :
-
Feedback matrix
- \(\mathbf {E}_{\text {opt}}\) :
-
Optimal feedback matrix
- F(t):
-
Corrective force
- \(f'_{\text {c}}\) :
-
Compressive strength of concrete
- \(f_{\text {R}}(.)\) :
-
Restoring force function
- \(G_0\) :
-
Constant power spectral density intensity
- \(h_k\) :
-
Height of the kth story
- I(t):
-
Non-negative envelope function
- \(\mathbf {K}\) :
-
Stiffness matrix
- \(\mathbf {M}\) :
-
Mass matrix
- n :
-
Number of degrees-of-freedom
- p[.]:
-
Probability
- \(\mathbf {P}\) :
-
State error covariance
- \(\mathbf {{P}}_{\mathrm{ISD}}\) :
-
Inter-story drift error covariance
- q(t):
-
Displacement vector
- \(\hat{q}(t)\) :
-
Displacement vector estimate
- \(\dot{q}(t)\) :
-
Velocity vector
- \(\ddot{q}(t)\) :
-
Acceleration vector
- \(S_{\ddot{u}^*\ddot{u}^*(\omega )}\) :
-
Kanai–Tajimi power spectral density
- t :
-
Time
- \({\text {tr}}(.)\) :
-
Trace
- \(\ddot{u}_{\text {g}}(t)\) :
-
Ground acceleration vector
- v(t):
-
Measurement noise
- w(t):
-
Process noise
- z(t):
-
Vector of auxiliary variables
- y(t):
-
Measured displacement vector
- \(\dot{y}(t)\) :
-
Measured velocity vector
- \(\ddot{y}(t)\) :
-
Measured acceleration vector
- \(\xi _{\text {g}}\) :
-
Site dominant damping coefficient
- \(\sigma\) :
-
Standard deviation
- \(\sigma ^2\) :
-
Variance
- \(\varvec{\varPhi }(\omega )\) :
-
Power spectral density
- \(\varvec{\varPhi }_{ee}(\omega )\) :
-
Error spectral density matrix
- \(\varvec{\varPhi }_{vv}(\omega )\) :
-
Power spectral density of measurement noise
- \(\varvec{\varPhi }_{ww}(\omega )\) :
-
Power spectral density of uncertain inputs
- \(\omega\) :
-
Frequency
- \(\omega _{\text {g}}\) :
-
Site dominant frequency
- \({\mathcal {N}}\) :
-
Normal distribution
- ASCE:
-
American Society of Civil Engineers
- ATC:
-
Applied Technology Council
- C:
-
Collapse
- CP:
-
Collapse prevention
- CSMIP:
-
California Strong Motion Instrument Program
- DM:
-
Damage measure
- DoF:
-
Degree of freedom
- DV:
-
Decision variable
- EDP:
-
Engineering demand parameter
- EKF:
-
Extended Kalman filter
- FE:
-
Finite element
- FEMA:
-
Federal emergency management agency
- IO:
-
Immediate occupancy
- ISD:
-
Inter-story drift
- KF:
-
Kalman filter
- LS:
-
Life safety
- M:
-
Measurement
- NMBO:
-
Nonlinear model-based observer
- PBA:
-
Performance-based assessment
- PBEE:
-
Performance-Based Earthquake Engineering
- PBM:
-
Performance-based monitoring
- PEER:
-
Pacific Earthquake Engineering Research
- PF:
-
Particle filters
- PL:
-
Performance level
- PSD:
-
Power spectral density
- RC:
-
Reinforced concrete
- RMS:
-
Root-mean-square
- UKF:
-
Unscented Kalman filter
References
ATC (1989) Procedures for postearthquake safety evaluations of buildings, report ATC-20. Technical report, Applied Technology Council (ATC), Redwood City, CA
ATC (1995) Addendum to the ATC-20 postearthquake building safety evaluation procedures. Technical report, Applied Technology Council (ATC), Redwood City, CA
Bernal D (2006) Flexibility-based damage localization from stochastic realization results. J Eng Mech 132(6):651–658
Sadeghi Eshkevari S, Heydari N, Nathan Kutz J, Pakzad SN, Diplas P, Sadeghi Eshkevari S (2019) Operational vision-based modal identification of structures: a novel framework. Struct Health Monit. https://doi.org/10.12783/shm2019/32502
Roohi M, Hernandez EM, Rosowsky D (2019) Nonlinear seismic response reconstruction and performance assessment of instrumented wood-frame buildings—validation using NEESWood capstone full-scale tests. Struct Control Health Monit 26(9):e2373
Roohi M, Erazo K, Rosowsky D, Hernandez EM (2020) An extended model-based observer for state estimation in nonlinear hysteretic structural systems. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2020.107015
Erazo K, Hernandez EM (2016) Uncertainty quantification of state estimation in nonlinear structural systems with application to seismic response in buildings. ASCE-ASME J Risk Uncertain Eng Syst Part A: Civ Eng 2(3):B5015001
Ching J, Beck JL, Porter KA, Shaikhutdinov R (2006) Bayesian state estimation method for nonlinear systems and its application to recorded seismic response. J Eng Mech 132(4):396–410
Hu RP, Xu YL (2019) Shm-based seismic performance assessment of high-rise buildings under long-period ground motion. J Struct Eng 145(6):04019038
Şafak E (1999) Wave-propagation formulation of seismic response of multistory buildings. J Struct Eng 125(4):426–437
Todorovska MI, Trifunac MD (2010) Earthquake damage detection in the imperial county services building II: analysis of novelties via wavelets. Struct Control Health Monit 17(8):895–917
Roohi M, Hernandez EM, Rosowsky D (2020) Reconstructing element-by-element dissipated hysteretic energy in instrumented buildings: application to the Van Nuys Hotel testbed. ASCE J Eng Mech. arXiv preprint arXiv:2002.12426(under review)
Hernandez EM, May G (2012) Dissipated energy ratio as a feature for earthquake-induced damage detection of instrumented structures. J Eng Mech 139(11):1521–1529
Simoen E, De Roeck G, Lombaert G (2015) Dealing with uncertainty in model updating for damage assessment: a review. Mech Syst Signal Process 56:123–149
Astroza R, Ebrahimian H, Conte JP (2019) Performance comparison of kalman-based filters for nonlinear structural finite element model updating. J Sound Vib 438:520–542
Naeim F, Hagie S, Alimoradi A, Miranda E (2006) Automated post-earthquake damage assessment of instrumented buildings. In: Wasti ST, Ozcebe G (eds) Advances in earthquake engineering for urban risk reduction. Nato science series: IV: Earth and environmental sciences, vol 66. Springer, Dordrecht
Lenjani A, Bilionis I, Dyke SJ, Yeum CM, Monteiro R (2020) A resilience-based method for prioritizing post-event building inspections. Nat Hazards 100(2):877–896. https://doi.org/10.1007/s11069-019-03849-0
Mohsen A, Pekcan G (2020) Structural health monitoring using extremely compressed data through deep learning. Comput-Aided Civ Infrastruct Eng 35:597–614
Pan H, Azimi M, Gui G, Yan F, Lin Z (2017) Vibration-based support vector machine for structural health monitoring. In: International conference on experimental vibration analysis for civil engineering structures, pp 167–178. Springer, Dordrecht
Mangalathu S, Sun H, Nweke CC, Yi Z, Burton HV (2020) Classifying earthquake damage to buildings using machine learning. Earthq Spectra 36(1):183–208
Zhang Y, Burton HV, Sun H, Shokrabadi M (2018) A machine learning framework for assessing post-earthquake structural safety. Struct Saf 72:1–16
Lenjani A, Dyke SJ, Bilionis I, Yeum CM, Kamiya K, Choi J, Liu X, Chowdhury AG (2020) Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion. Eng Struct 208:109884. https://doi.org/10.1016/j.engstruct.2019.109884
Roohi M (2019) Performance-based seismic monitoring of instrumented buildings. PhD thesis, Graduate College Dissertations and Theses. 1140. University of Vermont
Wu RT, Jahanshahi MR (2020) Data fusion approaches for structural health monitoring and system identification: past, present, and future. Struct Health Monit 19(2):552–586
Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW, Nadler BR, Czarnecki JJ (2003) A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory, USA, pp 1–7
Fan W, Qiao P (2011) Vibration-based damage identification methods: a review and comparative study. Struct Health Monit 10(1):83–111
Azimi M, Eslamlou AD (2020) Data-driven structural health monitoring and damage detection through deep learning: state-of-the-art review. Sensors 20(10):2778
Celebi M, Sanli A, Sinclair M, Gallant S, Radulescu D (2004) Real-time seismic monitoring needs of a building owner—and the solution: a cooperative effort. Earthq Spectra 20(2):333–346
Miranda E (2006) Use of probability-based measures for automated damage assessment. Struct Des Tall Spec Build 15(1):35–50
Porter K, Mitrani-Reiser J, Beck JL (2006) Near-real-time loss estimation for instrumented buildings. Struct Des Tall Spec Build 15(1):3–20
Mitrani-Resier J, Wu S, Beck JL (2016) Virtual inspector and its application to immediate pre-event and post-event earthquake loss and safety assessment of buildings. Nat Hazards 81(3):1861–1878
Hwang SH, Lignos DG (2018) Assessment of structural damage detection methods for steel structures using full-scale experimental data and nonlinear analysis. Bull Earthq Eng 16(7):2971–2999
Cremen G, Baker JW (2018) Quantifying the benefits of building instruments to FEMA p-58 rapid post-earthquake damage and loss predictions. Eng Struct 176:243–253
Hernandez E, Roohi M, Rosowsky D (2018) Estimation of element-by-element demand-to-capacity ratios in instrumented SMRF buildings using measured seismic response. Earthq Eng Struct Dyn 47(12):2561–2578
SEAOC (1995) Vision 2000: performance based seismic engineering of buildings. Structural Engineers Association of California, Sacramento
ATC (1996) Seismic evaluation and retrofit of concrete buildings. 2. Appendices. Applied Technology Council (ATC), Redwood City, CA
FEMA (1997) NEHRP guidelines for the seismic rehabilitation of buildings. FEMA-273, Federal Emergency Management Agency, Washington, DC
FEMA (2000) Commentary for the seismic rehabilitation of buildings. FEMA-356, Federal Emergency Management Agency, Washington, DC
ASCE (2013) ASCE/SEI 41-13: seismic evaluation and retrofit of existing buildings. Technical report, American Society of Civil Engineers, ASCE/SEI 41–13, Reston, VA
Porter KA (2003) An overview of PEER’s performance-based earthquake engineering methodology. In: Proceedings of ninth international conference on applications of statistics and probability in civil engineering. Citeseer
Gelb A (1974) Applied optimal estimation. MIT press, Cambridge
Julier S, Uhlmann J, Durrant-Whyte HF (2000) A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans Autom control 45(3):477–482
Doucet A, Godsill S, Andrieu C (2000) On sequential monte carlo sampling methods for bayesian filtering. Stat Comput 10(3):197–208
Hernandez EM (2011) A natural observer for optimal state estimation in second order linear structural systems. Mech Syst Signal Process 25(8):2938–2947
Hernandez EM (2013) Optimal model-based state estimation in mechanical and structural systems. Struct Control Health Monit 20(4):532–543
Roohi M, Hernandez EM, Rosowsky D (2019) Nonlinear seismic response reconstruction in minimally instrumented buildings—validation Using Neeswood capstone full-scale tests. Structural health monitoring 2019. Presented at the structural health monitoring 2019. https://doi.org/10.12783/shm2019/32390
FEMA-356 (2000) Prestandard and commentary for the seismic rehabilitation of buildings. American Society of Civil Engineers (ASCE), Reston
Krawinkler H (2005) Van Nuys hotel building testbed report: exercising seismic performance assessment. Pacific Earthquake Engineering Research Center, College of Engineering of California
Trifunac MD, Ivanovic SS, Todorovska MI (1999) Instrumented 7-storey reinforced concrete building in Van Nuys, California: description of the damage from the 1994 Northridge earthquake and strong motion data. Report CE 99:2
Trifunac MD, Ivanovic SS (2003) Analysis of drifts in a seven-story reinforced concrete structure. University of Southern California Report CE, pp 3–10
Frank M, Fenves GL, Scott MH et al (2000) Open system for earthquake engineering simulation. University of California, Berkeley
Jalayer F, Ebrahimian H, Miano A, Manfredi G, Sezen H (2017) Analytical fragility assessment using unscaled ground motion records. Earthq Eng Struct Dyn 46(15):2639–2663
Saiful Islam M (1996) Analysis of the northridge earthquake response of a damaged non-ductile concrete frame building. Struct Des Tall Build 5(3):151–182
Acknowledgements
Support for this research provided, in part, by award No. 1453502 from the National Science Foundation is gratefully acknowledged.
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Roohi, M., Hernandez, E.M. Performance-based post-earthquake decision making for instrumented buildings. J Civil Struct Health Monit 10, 775–792 (2020). https://doi.org/10.1007/s13349-020-00416-1
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DOI: https://doi.org/10.1007/s13349-020-00416-1