Performance assessment under multiple hazards D. Vamvatsikos , Dept - PowerPoint PPT Presentation

Performance assessment under multiple hazards D. Vamvatsikos , Dept of Civil and Environmental Engineering, University of Cyprus, Cyprus E. Nigro , Dept of Structural Engineering, University of Naples “Federico II”, Naples, Italy L.A. Kouris, G. Panagopoulos, A.J. Kappos , Dept of Civil Engineering, Aristotle University of Thessaloniki, Greece T. Rossetto & T.O. Lloyd , Dept of Civil, Environmental and Geomatic Engineering, University College London, UK T. Stathopoulos , Dept of Building, Civil and Environmental Engineering, Concordia University, Canada

Introduction • Vulnerability can be defined in multiple ways – it can be evaluated using widely different formats that are typically inconsistent with each other, especially when considering different hazards! • Emergence of multi-hazard assessment concepts, hence – important to collectively discuss such methods – understand their merits – attempt to cast them in a format that is suitable for integration within a single practical assessment framework • Here: review of vulnerability assessment methods for – earthquake hazard – landslides and flowslides – tsunami – strong wind and hurricanes

1. Vulnerability of structures to earthquakes

Structural Building No. of Code Infills Classification of height storeys system RC1L Low-rise 1 to 3 the building stock Medium- No infills RC1M 4 to 7 rise  several systems available… RC1H High-rise 8+ RC3.1L Low-rise 1 to 3  need to converge! Regularly Medium- Frame RC3.1M 4 to 7 infilled rise Structural Storey Code RC3.1H High-rise 8+ system number RC3.2L Low-rise 1 to 3 Soft MSt1-2 1 2 Medium- storey Stone RC3.2M 4 to 7 rise (pilotis) masonry MSt3+ 3+ RC3.2H High-rise 8+ RC4.1L Low-rise 1 to 3 MBr1-2 1 2 Medium- Brick masonry No infills RC4.1M 4 to 7 rise MBr3+ 3+ RC4.1H High-rise 8+ RC4.2L Low-rise 1 to 3 Dual Regularly Medium- RC4.2M 4 to 7 (walls+frames) infilled rise RISK-UE system, as adapted by AUTh Team RC4.2H High-rise 8+ RC4.3L Low-rise 1 to 3 Soft Medium- storey RC4.3M 4 to 7 rise (pilotis) RC4.3H High-rise 8+

Example of compilation of inventory in Grevena loss assessment project (AUTh) Digital map (ArcMap) Building inventory (Εxcel) UID GIS

Damage definition • six (5+1) damage states (DS0 to DS5) Range of Central Damage Damage state label damage damage State factor factor (%) DS0 None 0 0 DS1 Slight 0-1 0.5 DS2 Moderate 1-10 5 DS3 Substantial to heavy 10-30 20 DS4 Very heavy 30-60 45 DS5 Collapse 60-100 80 • damage threshold different for R/C, URM …

Ground motion characterisation  Choice of a ground motion parameter that represents the seismic demand is crucial. Possible choices:  macroseismic intensity based approaches (e.g. ATC-13)  can be misleading!(rather subjective quantity, associated with great uncertainty, dependent on building stock performance)  but: (limited) available damage data is usually associated (only) with intensity levels!  direct ground motion quantities, such as PGA or PGV or even  spectral quantities, like S d (HAZUS) or S a (Kiremidjian 1996)  pertinent empirical data very scarce!

Determination of vulnerability functions  Empirical approach [e.g. Spence et al. 2008; Rota et al. 2008]  most common problem in purely empirical approach: lack of (sufficient and reliable) statistical data for several intensities  Judgement-based and rating methods  expert opinion [e.g. ATC-13] subjectivity ?...  ‘scoring’ method (questionnaires) [e.g. GNDT] scores=?...  Analytical approach  from (equiv.)SDOF to response-history of full structures!  might seriously diverge from reality, usually overestimating loss!  Hybrid approach  combines empirical data with inelastic analysis (static/dynamic)  critical point: definition of damage in each component!

The ‘primary’ vulnerability curve (evolution of damage with intensity of seismic action) • Damage-state medians determined from analytical L – PGA relationship, scaled based on statistical data available e.g. DS4 (L=30%)

The probabilistic vulnerability curve ( fragility curve)  lognormal distribution assumed for fragility analysis  for given distribution type, only L DSi and β needed  fragility curves for R/C and URM buildings developed by Kappos et al. using the hybrid approach typical fragility curves for typical fragility curves for URM buildings R/C buildings 1.00 0.80 i |PGA] DS1 0.60 DS2 DS3 P[ds>ds DS4 0.40 DS5 0.20 0.00 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 PGA (g) 1.00 0.80 i |PGA] DS1 0.60 DS2 P[ds>ds DS3 DS4 0.40 DS5 0.20 0.00 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 PGA (g)

Epistemic uncertainty • Uncertainty is important (lack of knowledge of properties, coarse models) • Recent methodologies remain computationally intensive and often difficult to apply for practical purposes…  200 realizations of static pushover capacity curves for a two story masonry building, caused by epistemic uncertainty [Vamvatsikos & Pantazopoulou 2010]

2. Vulnerability of structures subjected to landslides and flowslides Eruption of St. Helens volcano ( 1980) resulting in a catastrophic landslide Landslide triggered by the eruption of Stromboli in 2002

Landslides triggered by the 1949 Khait earthquake, Tajikistan: major bend in Khait landslide path

Building damage due to debris flow breaking of brick or tuff external height of debris flow walls in R.C. framed structure

Building damage due to debris flow: Reinforced concrete buildings Failure of corner column Breaking of brick or tuff external walls

Building damage due to debris flow: Reinforced concrete buildings (contnd.) Plastic collapse mechanism of columns

Building damage due to debris flow: Masonry (URM) buildings Masonry building impacted by debris Remaining parts of masonry flows buildings

Mechanical models for assessment of buildings P 2 1 u p L u L 2 2 2 2 2 p C V cos V cos f g 2 g p 1 g P 1 u u V L 2 cos L 2 cos Type-A Mechanism Collapse of the tuff or brick external walls  uncertainties in both the hydrodynamic and structural models!

Mechanical models for assessment of buildings – R/C 16 M u q u 2 L 2 2 q C D V D V f g 16 g q g M u u V 2 D L D Type-B Mechanism Three-plastic-hinge collapse mechanism in reinforced concrete columns

Mechanical models for assessment of buildings – R/C 4 M u q u 2 L q 4 M g g u u V 2 D L D 4 M g u i , i V 2 L D i i Type-C Mechanism Two-plastic-hinges collapse mechanism in reinforced concrete columns

Mechanical models for assessment of buildings – R/C 2 T u q u L 2 T 1 u q u L L / L 2 L / L 1 1 g q u V D Type-D Mechanism Shear collapse mechanism in reinforced concrete columns

Mechanical models for assessment of buildings - URM 4 M L u p 1 uv 2 3 L 1 s p 2 uo k b p g 1 g u V p p uv uo 2 cos cos Type-E Mechanism Debris flow impact against the ground floor walls of masonry buildings  fragility curves?...

3. Vulnerability to Strong Wind events - Hurricanes  Vulnerability of structures is assessed through:  damage assessment  field examinations of wind-structure interaction  hurricane risk assessment from the insurance perspective 23

Homes destroyed by the storm in Plaquemines, Parish, Louisiana 24 Image from NOAA

Highway 90 bridge from Biloxi, Mississippi to Ocean Springs lies in a twisted mass as result 25 of catastrophic wind and storm surge from Hurricane Katrina (photo from FEMA)

Damage Assessment  Several studies assessed damage through on-site observations  Components that suffered excessive damage were  roofs  gable-end walls  connections and sheathing  Fully engineered buildings showed superior performance over pre- or non-engineered buildings 26

Field Studies  Valuable information has been produced by field (full- scale) studies  Monitoring of both  wind characteristics and  impact on structures  Evaluating wind-induced envelope pressures and structural response 27

Hurricane Risk Assessment  Risk assessment involves several points of view (e.g. engineering, economic etc.)  Research aims at developing appropriate risk assessment models influenced by various factors such as:  weather data  type of building  occupancy  construction method

4. Vulnerability to Tsunami  Tsunami vulnerability is still in its infancy  Generation of tsunami vulnerability curves has been hampered by the rarity of events  Leading to lack of knowledge on tsunami behaviour near and on-shore  Difficulty of current numerical models to accurately reproduce velocity profiles onshore

Empirical tsunami vulnerability curves  Majority of existing tsunami vulnerabilty curves are empirical.  Some examples: Peiris (2006): • For low-rise URM houses in Sri Lanka (SL) affected by the 2004 Indian Ocean Tsunami (n=8672, from 11 locations) • SL census data post- tsunami • 3 damage states • X-axis = submerged height