Quantifying the benefits of building instruments to FEMA P-58 rapid post-earthquake damage and loss predictions

Abstract Seismic instrumentation in a building is used to accurately capture its response during an earthquake, and so should enable enhanced rapid post-earthquake predictions of the event’s consequences for the building (among other benefits). Such instrumentation is costly, however, thus it is useful to know the extent to which varying degrees of its implementation within a building affect the accuracy of these predictions. The purpose of this study is to develop a methodology for quantifying the errors in post-earthquake damage and loss consequence predictions calculated from the FEMA P-58 Seismic Performance Assessment procedure, when different numbers of building instruments are used to capture the response of a building in a given event. We use responses measured in instrumented buildings during the 1994 Northridge earthquake, and obtain consequence predictions via the FEMA P-58 methodology. The density of instrumentation examined ranges from the case in which all floors are instrumented to that in which no instrumentation is present and FEMA P-58 simplified procedures are used to predict response and corresponding consequences. We find that errors in consequence predictions decrease as the number of building instruments is increased and that the reduction in error is substantial as soon as more than a small number of floors are instrumented. This is valuable information for a building owner if they wish to use seismic instrumentation as a means of rapidly obtaining damage and loss information for post-earthquake decision-making.

[1]  Ernesto Heredia-Zavoni,et al.  Optimal instrumentation of uncertain structural systems subject to earthquake ground motions , 1998 .

[2]  Hoon Sohn,et al.  Design and performance validation of a wireless sensing unit for structural monitoring applications , 2004 .

[3]  Robert R. Youngs,et al.  Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra , 2014 .

[4]  Anne S. Kiremidjian,et al.  Modular, wireless network platform for monitoring structures , 1998 .

[5]  Mark D. Varljen,et al.  A Jackknife Approach to Examine Uncertainty and Temporal Change in the Spatial Correlation of a VOC Plume , 1999 .

[6]  Mehmet Çelebi,et al.  Seismic response and damage detection analyses of an instrumented steel moment-framed building , 2006 .

[7]  Dimitrios G. Lignos,et al.  Nonmodel-based framework for rapid seismic risk and loss assessment of instrumented steel buildings , 2018 .

[8]  Maria Pina Limongelli,et al.  Optimal location of sensors for reconstruction of seismic responses through spline function interpolation , 2003 .

[9]  Jerome P. Lynch,et al.  Validation of a wireless modular monitoring system for structures , 2002, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[10]  G. Hossein Behforooz.  A comparison of theE(3) and not-a-knot cubic splines , 1995 .

[11]  M. Çelebi.  Seismic instrumentation of buildings , 2000 .

[12]  B. Efron,et al.  The Jackknife: The Bootstrap and Other Resampling Plans. , 1983 .

[13]  Dimitris Hadjiharitou.  Improving estimates of structural seismic motion , 2013 .

[14]  Jack P. Moehle,et al.  A framework methodology for performance-based earthquake engineering , 2004 .

[15]  K. Law,et al.  Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure , 2006 .

[16]  Anthony F. Shakal,et al.  Structure Instrumentation in the California Strong Motion Instrumentation Program , 2001 .

[17]  Dimitrios G. Lignos,et al.  Proposed methodology for earthquake-induced loss assessment of instrumented steel frame buildings: building-specific and city-scale approaches , 2017 .

[18]  T. Fearn.  The Jackknife , 2000 .

[19]  Farzad NAEIM.  LEARNING FROM STRUCTURAL AND NONSTRUCTURAL SEISMIC PERFORMANCE OF 20 EXTENSIVELY INSTRUMENTED BUILDINGS , 1999 .

[20]  James L. Beck,et al.  Near‐real‐time loss estimation for instrumented buildings , 2003 .

[21]  James L. Beck,et al.  Smarter Structures: Real-time Loss Estimation for Instrumented Buildings , 2006 .

[22]  S. R. Uma,et al.  THE GEONET BUILDING INSTRUMENTATION PROGRAMME , 2011 .

[23]  M. Tomczak,et al.  Spatial Interpolation and its Uncertainty Using Automated Anisotropic Inverse Distance Weighting (IDW) - Cross-Validation/Jackknife Approach , 1998 .

[24]  Eduardo Miranda,et al.  Approximate Seismic Lateral Deformation Demands in Multistory Buildings , 1999 .

[25]  Donald L. Phillips,et al.  Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models , 1996 .

[26]  W. Silva,et al.  NGA-West2 Database , 2014 .

[27]  L. L. Zhang,et al.  Optimal placement of sensors for structural health monitoring using improved genetic algorithms , 2004 .

[28]  Firdaus E. Udwadia,et al.  A Methodology for Optimal Sensor Locations for Identification of Dynamic Systems , 1978 .

[29]  G. Bowen,et al.  Interpolating the isotopic composition of modern meteoric precipitation , 2003 .

[30]  Gregory G. Deierlein,et al.  Challenges Towards Achieving Earthquake Resilience Through Performance-Based Earthquake Engineering , 2014 .

[31]  Helmut Krawinkler,et al.  Van Nuys Hotel Building Testbed Report: Exercising Seismic Performance Assessment , 2005 .

[32]  Brendon A. Bradley,et al.  Response of instrumented buildings under the 2016 Kaikoura earthquake. , 2017 .

[33]  Farzad Naeim,et al.  AUTOMATED POST-EARTHQUAKE DAMAGE ASSESSMENT OF INSTRUMENTED BUILDINGS , 2006 .

[34]  M. Çelebi.  Current Practice and Guidelines for USGS Instrumentation of Buildings Including Federal Buildings , 2001 .

[35]  Farzad Naeim,et al.  CSMIP INSTRUMENTED BUILDING RESPONSE ANALYSIS AND 3-D VISUALIZATION SYSTEM (CSMIP-3DV) , 2004 .

[36]  SR Uma.  Seismic Instrumentation of Buildings-A Promising Step for Performance Based Design in New Zealand , 2007 .

[37]  F. Udwadia.  Methodology for Optimum Sensor Locations for Parameter Identification in Dynamic Systems , 1994 .

[38]  Majid Baradaran Shoraka,et al.  Collapse assessment of concrete buildings : an application to non-ductile reinforced concrete moment frames , 2013 .

[39]  Jerome P. Lynch,et al.  Embedment of structural monitoring algorithms in a wireless sensing unit , 2003 .

[40]  Paul A. Longley,et al.  Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models , 2005 .

[41]  M. Celebi,et al.  Real-Time Seismic Monitoring Needs of a Building Owner—and the Solution: A Cooperative Effort , 2004 .

[42]  Jerome P. Lynch,et al.  Embedding damage detection algorithms in a wireless sensing unit for operational power efficiency , 2004 .