POST-EARTHQUAKE COLLAPSE PROGNOSTICATION OF STRUCTURAL SYSTEMS USING SPARSE RESPONSE DATA

ABSTRACT

 

In order to reduce the impact of major earthquakes on communities, a rapid assessment of the state of structural systems for the purpose of re-occupancy of safe buildings is necessary.

However, this determination is typically carried out through time-consuming visual inspections. Structural health monitoring is a promising alternative but existing techniques require a dense array of instrumentation on the structure rendering them impractical for many reasons including prohibitive costs of installation, maintenance, and data management and processing. A methodology is proposed in this study that uses sparse response data, for example the accelerations at a few floor levels, to identify the state of nonlinear numerical models that are further used for prognosticating collapse under future seismic hazards. The utility of the proposed methodology is demonstrated using the data from the shake table tests performed on a 4-story scaled model. The methodology is able to successfully predict the observed collapse of the physical structure. Furthermore, it has been observed that the vulnerability to collapse following an earthquake is greatly increased even to lower intensity earthquakes due to the accumulation of residual deformations and stresses in the structure.

 

 

TABLE OF CONTENTS

List of Figures ………………………………………………………………………………………………………….. v

List of Tables …………………………………………………………………………………………………………… vi

Chapter 1 Introduction ………………………………………………………………………………………………. 1

Background ……………………………………………………………………………………………….. 1

Research objective………………………………………………………………………………………. 2

Research scope …………………………………………………………………………………………… 2

Chapter 2 Methodology …………………………………………………………………………………………….. 3

Step 1: Model formulation and selection of sparse response data ………………………. 4

Step 2: Parameter and state estimation using sparse response data …………………….. 5

Step 3: Collapse prognostication using fragility analyses …………………………………. 7

Chapter 3 Application ……………………………………………………………………………………………….. 10

Overview of experimental setup …………………………………………………………………… 10

Numerical model development …………………………………………………………………….. 11

Selection of sparse response data ………………………………………………………………….. 14

Parameter estimation of the numerical model using sparse response data …………… 15

Collapse prognostication of the test frame ……………………………………………………… 18

Chapter 4 Summary and Conclusions ………………………………………………………………………….. 22

Future work ……………………………………………………………………………………………….. 23

 

Appendix A  Validation and performance evaluation of modified genetic algorithm …………. 24

Appendix B  Modeling of frictional damping ……………………………………………………………….. 30

Appendix C  Effect of sparse response data on the identifiability of model parameters ……… 32

Appendix D  Modified genetic algorithm source code …………………………………………………… 35

References ……………………………………………………………………………………………………………….. 51

Chapter 1  

 

Introduction

Background

During a major earthquake, some amount of damage to structures is unavoidable. The current seismic design philosophy permits structures to deform in-elastically. For example, ASCE 7-10 [1] incorporates the response modification coefficient R, which allows the design lateral strength to be lower than that required for the structure to remain in the elastic range. This results in the development of residual deformations and stresses leading to damage in the structure. A rapid assessment of the state of such damaged structural systems from a life safety perspective is necessary to reduce the socio-economic impact of major earthquakes on communities.

Currently, visual structural inspections [2-3] are preformed to determine the safety of a structure for re-occupancy. Depending on the extent of the damage observed in the structure, colored placards are used to tag the structure as safe, limited-use or unsafe. However, tagging of structures through visual inspections is time consuming, subjective, qualitative, unreliable [4] and often presents hazardous working environments for the inspectors. Structural health monitoring (SHM) is a promising alternative approach for the assessment and prognosis of the safety of the structural systems. SHM involves the installation of sensors on the structure which trigger a notification when a damage threshold is exceeded. However, current SHM technologies rely upon a dense array of sensors on the structures to detect damage which is impractical for many reasons including the prohibitive costs of installation, maintenance, long term reliability of monitoring system, data management and processing. Recognizing these issues associated with obtaining complete response data, efforts have been made to identify linear systems using partial structural response data [5-8] but no research exists for nonlinear systems. Therefore, a rapid and an automated state assessment procedure for nonlinear structural systems that requires minimal sensory information is needed.

Research objective

The goal of the current study is to develop and evaluate a methodology for the parameter estimation of nonlinear structural systems using sparse response data i.e. the response measured at a proper subset of the system’s degrees of freedom (DOFs) to be further used for the purpose of prognostication and reliability analysis. A potential benefit of the proposed methodology is that it facilitates an automated near real-time assessment of the vulnerability to collapse following an earthquake which can be used for better decision-making pertaining to the re-occupancy of safe buildings thereby contributing to a faster recovery of communities.

Research scope

The scope of this study is limited to structural systems exhibiting material nonlinearities during a major earthquake. A-prior information pertaining to the characterization of nonlinearity is assumed to be known. Two-dimensional, low-rise moment resisting frames which do not exhibit a soft-story behavior are studied. Side-sway collapse in which lateral displacements of floor levels cause significant P-Δ effects in the lateral force resisting systems is the type of

collapse studied in the current research.

POST-EARTHQUAKE COLLAPSE PROGNOSTICATION OF STRUCTURAL SYSTEMS USING SPARSE RESPONSE DATA

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