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Using Ambient Vibration Measurements for Risk Assessment at Urban Scale : from Numerical Proof of Concept to a Case Study in Beirut (Lebanon) Christelle Salameh 1 , Pierre-Yves Bard 1 , Bertrand Guillier 1 , Jacques Harb 2 , Ccile Cornou 1 and

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Using Ambient Vibration Measurements for Risk Assessment at Urban Scale : from Numerical Proof of Concept to a Case Study in Beirut (Lebanon)

Christelle Salameh1, Pierre-Yves Bard1, Bertrand Guillier1, Jacques Harb2, Cécile Cornou1 and Michelle Almakari1

1 - ISTerre, University Grenoble-Alpes, France 2 - Notre-Dame University-Louaizé, Lebanon

ESG5, Taipei, Taiwan, 15/08/2016

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Outline

Introduction

? Use of frequency information in large scale damage assessment

Conceptual framework and comprehensive numerical simulation

SDOF elastoplastic oscillators on multilayered 1D (linear) soil profiles ANN analysis

Robustness and field applicability

? easily available site amplification proxy NL soil behavior (MDOF)

Sense-check : example Application to Beirut City (Lebanon) Conclusions, caveats and further steps

ESG5, Taipei, Taiwan, 15/08/2016

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Introductory words

  • Many examples of larger damage due to coincidence between

soil and building frequencies

  • Mexico 1985, Kathmandu 2015, …
  • Obvious for linear systems, not so much for NL systems
  • Building specific studies (detailed information)
  • best GM proxy = SA (f0) or ASA ([0.6 – 1] f0)

– (Perrault & Gueguen, 2015; De Biasio, 2015)

  • ? Urban scale (or larger) : Damage / Risk maps
  • Microzonation, site effects : rather quantitative assessment
  • Site characterization : Geology, VS30, f0 (H/V, …)
  • Site amplification
  • Building surveys : most often only qualitative
  • Gross typology

ESG5, Taipei, Taiwan, 15/08/2016

Lack of consistency hazard / vulnerabilty

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Intensity (or PGA) Damage

Damage Estimation

Bullding scale : Mechanical methods

Vulnerability Curves for various building typologies

Purple: Seismic demand Black: Building Resistance

Estimate damages quantitatively on a large scale with more mechanical input including spectral coincidence

ESG5, Taipei, Taiwan, 15/08/2016

Large scale = qualitative Individual scale = quantitative Large scale (urban) ? Macroseismic approach (Hazus, RISK-UE)

! Challenging !

(spatial variability)

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Outline

Introduction

? Use of frequency information in large scale damage assessment

Conceptual framework and comprehensive numerical simulation

Elastoplastic SDOF oscillator on a single layer Extension through comprehensive numerical simulation

SDOF elastoplastic oscillators on multilayered 1D soil profiles

Neural network analysis

ESG5, Taipei, Taiwan, 15/08/2016

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Soil response

ESG5, Taipei, Taiwan, 15/08/2016

Reflexion/Transmission properties

Bedrock

Soil

f0 3f0 5f0

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SLIDE 7

dmax

Oscillator response : weak input (linear response)

ESG5, Taipei, Taiwan, 15/08/2016

Linear branch of the oscillator

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dmax

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Elastoplastic Behavior of the oscillator

Oscillator response : strong input (non linear domain)

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SLIDE 9

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Conceptual framework : a simple illustrative example

Bedrock

H(m), ρ, Vs (m/s) Qs Vs=1000 m/s 54 SDOF Elastoplastic oscillators 9 x fstructure = 1 → 9 Hz 6 x dy= 0.005 → 0.05 (m)

V

Vy

dy

d

du

36 Linear single-layer sites (No SSI) : 4 x Velocity Contrast= 2 → 8 9 x fsoil= 1 → 9 (Hz) 60 synthetic Input motion (Sabetta and Pugliese 1996 : nonstationary): 5 x Magnitude= 3 → 7 4 x Distance= 5 →100 (km) PGA= 0.02- 8.6 (m/s2)

116 640 Models

Linear Plastic

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Bedrock

dmaxrock On soil On outcropping bedrock

Comparison soil / rock

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Bedrock

dmaxsoil

Same input motion

dmaxsoil / dmaxrock

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Statistical analysis for the simple case

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Bedrock H(m), ρ, Vs(m/s)

Vs=1000 m/s V

Vy

dy

d

du

dmax(soil) dmax(rock) Low PGA / linear response

C=2 C=4 C=6 C=8

V

Vy

dy

d

du

dmax(soil) dmax(rock) High PGA fstruct=1 Hz

fsoil=3 Hz

Fstruct = 9 Hz

fstruct/fsoil = 1 fstruct/fsoil = 3

Non-linear behavior of the structure dmax(soil) /dmax(rock ) Fstruct / fsoil

0.1 1.0 10 1.0 10

fstruct / fsoil fstruct/fsoil=0.33 fstruct/fsoil = 3

54 oscillators, 9 site frequencies (1-9 Hz) 1 velocity contrast

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Realistic (less unrealistic…) case: real soil profiles

ESG5, Taipei, Taiwan, 15/08/2016

Risk-UE typologies : 141 SDOF elastoplastic oscillators f struct, dy, du classified into 5 typology classes: 1 = Masonry; 2 = Non-designed RC; 3 = RC Low ductility; 4= RC Medium ductility; 5) RC High ductility 887 multilayered linear soils (still no SSI): 614 KiKnet + 251 USA + 22 Europe fsoil= 0.2-39 Hz Vs30= 111 -2100 m/s depth= 7-1575 m 60 synthetic Input Signal: Magnitude= 3 → 7, Distance = 5 →100 km PGA= 0.02- 8.6 m/s2

~7.5 MILLION MODELS!!!

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Oscillator characteristics

ESG5, Taipei, Taiwan, 15/08/2016

Distribution dy – T

struct

Distribution du/dy – T

struct

Ductility du/dy Yield displacement dy Period (s) Period (s)

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Distribution of site characteristics

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1 3.16 10 31.62 100 316.22 1000 3162.27 50 100 150

Depth (m) Frequency

Sediment Thickness

0.1 0.31 1 3.16 10 31.62 100 10 20 30 40 50 60 70 80

f0 (Hz) Frequency

Fundamental frequency

1 1.58 2.51 3.98 6.30 10 15.84 25.11 39.81 63.09 10 20 30 40 50 60 70 80 90 100

Cv Frequency

Velocity contrast

100 158.48 251.18 398.10 630.95 1000 1584.89 2511.88 10 20 30 40 50 60 70 80

Vs30 (m/s) Frequency

VS30

1 3. 10. 30.

VS30 (m/s)

1 10 100 1000

Thickness (m)

0.1 1.

  • 10. 100 .

100 300. 1000. 3000.

f0 (Hz) CV = Vmax / Vmin

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Classical statistical analysis?

Signal Structure Soil 1 output Damage increment

Input parameters "SSS" ~7.5 MILLION MODELS!!! Artificial Neural Network ANN

ESG5, Taipei, Taiwan, 15/08/2016

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Neural network approach

Goal

  • to look for statistical relationships

between pre-selected input and output variables, without any a priori on the functional forms

Principle (ML perceptron)

  • Combination through weigthed sums

("synaptic weights") and "activation functions"

  • Introduction of a "hidden layer"

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Implementation

  • Selection of input and output parameters
  • Learning, validation and test sets : 70%, 15%, 15%
  • Optimizing
  • Number of neurons in the hidden layer
  • Activation functions
  • Training algorithm
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Inputs Hidden layer Target

wij , wh

j = synaptic weights

Initially random, optimized through training

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Neural Network : principle

Computed Output

Error RMSE R2

∑wi1

Performance parameters

∑wh

j

Activation functions

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SLIDE 18

fstruct/ fsoil Velocity contrast PGA

Damage increment

Input layer Hidden Layer Target

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Neural Network: Our case study

One ANN for each building typology class (1-5) Output layer

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Damage level index

Risk-UE project : correspondence between EMS98 damage states and maximum structural displacement (Lagomarsino and Giovinazzi, 2006)

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V Vy dy d du 0.7dy 1.5dy 0.5(dy+du) du Damage index DI 1 2 3 4 D0 No damage D1 Slight D2 moderate D3 extensive D4 Complete

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Performance of the ANN models

ANN Model / Vulnerability Class Initial standard deviation Error RMSE RMSE Reduction Variance reduction Coeffificient of determination R2 Class 1 (Masonry) 0.182 0.126 31% 52% 0.81 Class 2 (Non-designed RC) 0.170 0.102 40% 64% 0.80 Class 3 (Low ductility RC) 0.172 0.112 35% 58% 0.81 Class 4 (Medium ductility RC) 0.153 0.094 39% 62% 0.81 Class 5 (High ductility RC) 0.147 0.096 35% 57% 0.82

ESG5, Taipei, Taiwan, 15/08/2016

Variance Reduction 50-64% + Good R2 Satisfactory performance (given the small number of input parameters)

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Relative importance of input parameters : synaptic weights

ESG5, Taipei, Taiwan, 15/08/2016

0.1 0.2 0.3 0.4 0.5 0.6

fstruc/fsoil Impedance Contrast PGA Synaptic weight Proportion

Class 1 Class 2 Class 3 Class 4 Class 5

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Dependence of damage increment on SSS inputs (example: class 3 - Low Ductility RC)

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C=20 C=10 C = 5 C = 2

Pga (m/s2) 0.05 0.5 2 4 Scale for Dsoil - Drock

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Outline

Introduction Proof of concept : comprehensive numerical simulation Robustness and field applicability

Field applicability : site amplification proxy NL soil behavior (MDOF)

ESG5, Taipei, Taiwan, 15/08/2016

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Fiel applicability : Input parameters

Loading : PGA Spectral coincidence : fstruct / fsoil Building mechanical behavior : typology class Site amplification : velocity contrast Cv

  • ? Other site amplification proxies : VS30, VS10, A0HV, ….

ESG5, Taipei, Taiwan, 15/08/2016

✔ ✔ ✗ ✔

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Numerical simulation of ambient noise

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Step 1: Definition of sources–receiver configuration Step 2: Computation of Greens functions : DWN [Hisada, 1995] Step 3: Summation of all the individual noise synthetics in the time domain. Total ambient noise synthetics for each of the 887 soil profiles (5-10 min)

receiver sources After Bonnefoy-Claudet et al., (2006)

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Derivation and check

  • f the "expected" H/V spectral ratio

ESG5, Taipei, Taiwan, 15/08/2016

Geopsy

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fstruct/ fsoil Velocity contrast PGA Disoil - DIrock Inputs Hidden Layer Target

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Modified Neural Network

Class 3 Buildings H/V amplitude + other "site proxies Vs30, Vs10, Vb/Vs30, Vb/Vs10

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Performance of each site amplification proxy : RMSE

ESG5, Taipei, Taiwan, 15/08/2016 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18

Vs10 Vs30 A0HV Vb/Vs10 Vb/Vs30 Contrast Initial standard deviation 0.104 0.103 0.100 0.0981 0.0957 0.110 0.1724

RMSE

Variance Reduction ~60 % Variance reduction = 66% H/V = satisfactory proxy

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Robustness : accounting for soil non-linear response

ESG5, Taipei, Taiwan, 15/08/2016

Low pga, Linear behavior

High pga, NonLinear behavior

Shift of frequency towards lower values + decrease of amplification

(see also Almakari et al., ESG5 2016)

Evolution of site transfer functions with PGA

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Nonlinear simulations

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Finite difference software: NOAH

(Bonilla 2001)

Time domain, Hysteretic behavior 887 profiles Site NL characteristics PEER NGAW2 assumptions (Kamai et al., 2014)

Cohesive Vs30<190 m/s Cohesionless Vs30>190 m/s

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fstruct/ fsoil Velocity contrast PGA Modified Dsoil - Drock Inputs Hidden Layer Target

New neural network

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(Class 3) Site Non-linear response + recomputation of

  • scillator response on soil
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Results with NL soil for building typology class 3

ESG5, Taipei, Taiwan, 15/08/2016

Linear Non- Linear

Slight modification (shift of frequency, reduction of the amplitude), but fstruct/fsoil remains the predominant parameter

C = 10, L

Damage increment

C = 10, NL Pga (m/s2) : 0.05 0.5 2 4 Scale for Dsoil - Drock

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Summary of ANN performances

ESG5, Taipei, Taiwan, 15/08/2016

Model 1

velocity contrast, linear site response

Model 2

H/V amplitude, linear site response

Model 3

Non-linear site response, impedance contrast

Site amplification proxy C = Vmax/Vmin A0HV C = Vmax/Vmin

Performance indicators Standard deviation (initial value :

0.1724) 0.112 0.099 0.103

Coefficient of determination R2

0.81 0.86 0.82

Synaptic weights fstruct/fsoil

0.51 0.51 0.51

Site amplification proxy

0.19 0.20 0.16

PGA

0.30 0.29 0.33

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Outline

Introduction Conceptual model and comprehensive numerical simulation Robustness and field applicability Sense-check : example application to Beirut City (Lebanon)

  • Seismic hazard in Beirut / Lebanon
  • Gathering of required data for Beirut City : ambient vibration

measurements at ground level and in buildings

  • Results

ESG5, Taipei, Taiwan, 15/08/2016

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SLIDE 35

AFRICAN PLATE ARABIAN PLATE

Yammouneh Serghaya Rachaya Roum

LEBANON

43 Km

BEIRUT

ESG5, Taipei, Taiwan, 15/08/2016

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SLIDE 36

ESG5, Taipei, Taiwan, 15/08/2016

H/V Ground surface Buildings on rock Buildings on soft site Mediterranean Sea

Needed : Soil frequency H/V amplitude Building frequencies PGA on rock Building typology

f0 soil (Brax, 2013) H/V amplitude (Brax, 2013)

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Lennartz LE-3D-5s seismometer CitySharkII recorder

ESG5, Taipei, Taiwan, 15/08/2016

L T

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Building set Description

ESG5, Taipei, Taiwan, 15/08/2016

Rock Sites

  • 197 measurements
  • Typology: reinforced

concrete frames

  • N= 1-26 floors
  • Age: 1910-2014

"Soft" Sites

  • 133 measurements
  • Typology: reinforced

concrete frames

  • N= 1-33 floors
  • Age: 1910-2014

330 buildings = 660 frequency and damping values

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Determination of empirical formulae for Beirut buildings

ESG5, Taipei, Taiwan, 15/08/2016

Longer periods on soils fully consistent with larger damping : indicative of some SSI (but with only slight frequency shifts) Period vs number of stories Damping vs frequency

Rock T ~ N/23 R2 = 0.89 Soil T ~ N/18 R2 = 0.90

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Building inventory

ESG5, Taipei, Taiwan, 15/08/2016

Survey of 7362 buildings by members

  • f Saint Joseph

University (USJ) noting :

  • the age of

construction + material

  • number of floors
  • position of each

building

 Assignment of a period for each building in the surveyed areas T0=f(N, geology)

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Integration

ESG5, Taipei, Taiwan, 15/08/2016

Damage increment

PGA

  • n

rock

Building class and frequency Soil Frequenc y and H/V amplitude

Brax 2013

Loading scenarii : Rock pga = 0.05g to 0.5g Period: T~N/23 rock sites T~N/18 soft sites Typology class

Class 1: Masonry

  • <1950
  • N<4

Class 2: Non- designed RC

  • 1950-2005
  • <1950 &

N≥4

Class 3: Low ductlity

  • >2005

(Lebanese seismic code introduction)

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Damage increment maps of Beirut

ESG5, Taipei, Taiwan, 15/08/2016

Scale for Dsoil - Drock

PGA 0.05g 0.5g 0.25g 0.45g 0.1g 0.15g 0.2g 0.3g 0.35g 0.4g River of Beirut

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Absolute damage in Beirut City

ESG5, Taipei, Taiwan, 15/08/2016

PGA 0.05g 0.5g 0.25g 0.45g 0.1g 0.15g 0.2g 0.3g 0.35g 0.4g

Evolution of damage level proportions with pga

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Summary

Key factors controlling damage level Linear soil H/V amplitude Proxy to site amplification Linear soil Non-linear soil

Beirut

ESG5, Taipei, Taiwan, 15/08/2016

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50% 30% 20%

Conclusions

  • 1. Key parameters controlling the rock to soil damage

increment

  • 1. Easy implementation based on
  • Classical building inventory surveys
  • Extensive use of amnbient vinration measurements (ground level +

building roofs)

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Key factor: spectral coincidence even with non-linear soils and structures PGA Contrast / H/V amplitude

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Quite promising approach, but … a few caveats and further steps

Limitations

Input Site Structure ANN model

Synthetic accelerograms Crude NGAW2 assumptions for NL site characteristics Definition of damage index SDOF structures only Oversimplified elastoplastic model Neural networks : only 3 "basic parameters"

Perspectives

Real accelerograms

(No real change on NL site response)

More realistic NL behavior

(Shallow NL underestimated, deep NL overestimated

? Other ? MDOF

(some changes,mostly in the linear domain)

More realistic structural NL models (Takeda, …) Other, or additional input parameters

(loading : PGA  spectral shape, ??...)

ESG5, Taipei, Taiwan, 15/08/2016

+ testing in areas recently hit by damaging earthquakes (ex.: Puerto Viejo, Ecuador)

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References

  • Bonilla, L. F. (2001). NOAH: Users Manual. Institute for Crustal Studies, University of

California, Santa Barbara.

  • Bonnefoy-Claudet, S., Cornou, C., Bard , P.-Y., Cotton , F., Moczo, P., Kristek , J., et al.

(2006). H/V ratio: a tool for site effects evaluation. Results from 1-D noise simulations. Geophysical Journal International, 167(2), 827-837.

  • Hisada, Y. (1995). An efficient method for computing Green's functions for a layered

half-space with sources and receivers at close depths (Part 2). Bulletin of the Seismological Society of America, 85(4), 1080-1093.

  • Kamai, R., N.A. Abrahamson and W.J. Silva (2014). Nonlinear horizontal site

amplification for constraining the NGA-West 2 GMPEs, Earthquake Spectra 30, 1223– 1240

  • Lagomarsino , S., & Giovinazzi, S. (2006). Macroseismic and mechanical models for the

vulnerability and damage assessment of current buildings. Bulletin of Earthquake Engineering, 4(4), 415–443.

  • Sabetta, F., & Pugliese, A. (1996). Estimation of response spectra and simulation of

nonstationary earthquake ground motions. Bulletin of the Seismological Society of America, 86(2), 337-352.

ESG5, Taipei, Taiwan, 15/08/2016

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SLIDE 48

Acknowledgements

IRD, France: C. Salameh's PhD fellowship

  • M. Brax, CRG/CNRS Beirut: Beirut H/V map

Saint-Joseph University, Beirut: Building inventory LIBRIS project: C. Voisin + French ANR

ESG5, Taipei, Taiwan, 15/08/2016

+ your kind and patient attention THANKS