Sustainable development: Vulnerability functions for the evaluation of physical effects related to mountain hazard

Transcription

1 Sustainable development: Vulnerability functions for the evaluation of physical effects related to mountain hazard Fragility Curve Approach Sami Oguzhan Akbas, Ph.D. Supervisors: Dr. Sterlacchini (CNR), Dr. De Amicis & Prof. Giacomelli (UNIMIB), Dr. Greiving (UNIDO) 24/04/08 23/04/10

2 Objectives for the first year: To contribute to the development of: 1. an understanding of the response of a structure affected by a mass movement 2. vulnerability functions for buildings and infrastructure with an estimation of the inherent uncertainty Research strategy: 1. empirical approach to develop vulnerability functions using the damage & hazard data from past events 2. theoretical approach through development of fragility curves for the main structural types in Valtellina

3 Conceptual Model Define scope and methodology Hazard Scenario Vulnerability Scenario Risk Scenario Hazard Scenario General Scheme Inventory the assets 6 - Prospective Economic Losses Direct damages Economic consequences Vulnerability Scenario 3 - Elements at Risk Structures Activities Goods Hazard levels 4 - Physical Effects Past events Past physical damages Prospective physical damages Indirect damages Statistical Models to define Susceptible areas (1: :25.000) to obtain Critical zones (1: :10.000) to delimit Hazardous area (1: :5.000) where 1 - Identify dangerous event 2 - Profile the event 5 Risk Scenario Level of risk to assets Study the social and economic features of the area Physical effects Procedures 7 - Civil Protection Management of Emergency Situations Instructions Resources People in charge

4 WB2

5 Some basic definitions: Risk : measure of the probability and severity of an adverse effect to life, health, property, or the environment. Quantitatively, Risk = Hazard potential worth of loss. -can also be expressed as probability of an adverse event times the consequences if the event occurs: Risk = Natural Hazard x Elements at Risk x Vulnerability Vulnerability : (engineering & natural sciences perspective) UNDRO (1979) (potential) results from event occurrence expressed with qualitative, semi-quantitative, or quantitative methods in terms of loss, disadvantage of gain, damage, injury, or loss of life ISSMGE (2004) the degree of loss to a given element or set of elements within the area affected by a hazard

6 Vulnerability assessments in landslide risk analysis: much work has been done on hazard analysis, but there is a lack of studies on the vulnerability part many approaches do not specify the type of process, the physical mechanisms, and the structural resistance they are applicable to most approaches describe process intensity only semiquantitatively many existing vulnerability models are based on empirical estimations based on limited local event documentation Due to the reasons above, it is very difficult to compare the few existing approaches, which vary significantly in detail of analysis, and in resulting numerical values for vulnerability. Therefore a universal vulnerability function currently is not available for landslides, and in particular for debris flows.

7 Use of fragility curves in risk assessment: fragility curves express the probable damage to an element at risk for a given level of hazard (specified as a result of hazard assessment) for a hazard scenario, damage to different types of elements at risk are probabilistically estimated using different fragility curves (due to different vulnerabilities) the results are then combined to estimate the level of risk this approach has been successfully used in earthquake and hurricane risk assessments unless vulnerability curves can be developed from comprehensive sets of coupled damage and hazard data, use of fragility curves are the most versatile approach for the estimation of quantitative risk

8 Examples for fragility curves (or surfaces):

9 Consider a given type of structure subjected to a debris flow impact P(t) with intensity ξ. This impact will lead to structural vibrations and will damage the structure approximately in a same way as an earthquake. Assume that the engineering demand parameter (i.e. limit state) of this structure is denoted as z for a specific damage level (i.e. moderate damage, high damage, etc.)

10 Debris flow impact pressure (P(t)) and intensity (ξ): Composed of two main components: hydrodynamic overpressure due to frontal impact P dy = ρ df v 2 (1 + α) < α < 1.20 hydrostatic pressure P st = 0.5 ρ df g d total pressure = P t = P dy + P st for velocities > 4-5 m/s, P dy dominates v or v & d can be selected as intensity measure (ξ) of debris flow

11 Uncertainties in impact pressure assessment uncertainty in the response due to limited knowledge on the impulsive load of debris flow: shape of impulse & the ratio of the impulse duration to the natural period of the structure, as well as errors in the assessment of velocity; strong uncertainties in α due to flow dynamics, homogeneity and flow constituents) P P T T? P P T T

12 Engineering demand parameter (z): spectral displacement, spectral acceleration, interstory drift ratio, PGA and PGD are frequently used for earthquakes; peal gust wind speed is the most commonly used for hurricanes and cyclones. except for a few brittle systems, building damage is primarily a function of building displacement, rather than force. drift ratio is proposed as the engineering demand parameter for this study

13 Drift ratio: drift ratio thresholds are functions of structural system type and damage state (e.g., slight, moderate, extensive, complete) Example from HAZUS (2003)

14 Drift ratio Building Inventory in Valtellina: A basic classification for now: the oldest buildings (beginning of the last century or even older) with cm thick stone walls, wood frame for 1 st floor and roof, mansard roof pre 70s brick columns, unreinforced masonry post 70s reinforced concrete and masonry

15 Drift ratio Building Inventory in Valtellina:

16 Drift ratio Building Inventory in Valtellina:

17 Uncertainties in engineering demand parameter uncertainty in the limiting drift ratio for a given type of structure (experimental results from RC frames suggest a lognormal distribution of drift ratio threshold with a COV of 31%). variability in the capacity (response) properties of the building type of interest (due mainly to varying qualities of workmanship and materials) Erberik, 2008

18 Proposed methodology for generation of fragility curves: obtain equivalent SDOF structural parameters (through dynamic analysis, e.g. SAP 2000), and their statistical descriptors (literature review) for typical building types in Valtellina establish limiting value of engineering demand parameter and its uncertainty for each damage state conduct (many) non-linear time history analyses of generated SDOF models (Nonlin) take uncertainties into account by considering the structural input parameters, impact pressure parameters, and engineering demand parameter as random variables.

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20 Proposed methodology for generation of fragility curves (cont d): estimate the probability of attainment or exceedance of engineering demand parameter at a specific intensity as the output of simulations P(z; ξ ) = f #{z i( ξ ) > z} n repeat the last 2 steps by changing the intensity level ξ to obtain the fragility curve for a given damage state repeat the procedure for different damage states to obtain the complete set of fragility curves transfer the information from fragility curves to vulnerability

21 Intensity Intensity

22 Concluding Remarks the results obtained using fragility curves can successfully be converted to economic terms and thus to vulnerability if and only if the damage states are defined accurately and in a detailed fashion. drift ratio thresholds are also available for non-structural damage; however a method should be developed to incorporate the effect of openings for debris flow induced non-structural damage. fragility curves by themselves are useless without proper hazard (susceptibility, physical parameters etc.) information.

23 Concluding Remarks for a smaller scale, in the case of building populations, the use of fragility curves yields a prediction of the proportion of the exposed building stock in each damage state after a debris flow (or scenario of it) with a certain spatial distribution of velocity and depth. the results we will obtain from this part of the research should be compared to results from compiled historical events in Valtellina parallel research study

24 A few words about the recent event in Selvetta ~ m 3 of material involved, according to a first-order (rough) estimate 1 house completely destroyed, 5 houses suffered functional damage ~1.5-2 m of accumulation height estimated total pressure = kpa (a very rough estimate!)

25 A few words about the recent event in Selvetta Fuchs et al. (2007) Zanchetta et al. (2004) data from Campania, southern Italy

26 Contact Details Sami O. Akbas Universitá Degli Studi di Milano - Bicocca Piazza della Scienza, Milano, IT Tel: Fax: sami.akbas@unimib.it