Literature Review of Integrated Impact Assessment Models of Climate Change with Emphasis on Damage Functions

35 p.

Physical and economic damage functions for air pollutants by receptor /

Bibliography: p. 143-152.

Using damage from 2010 Haiti earthquake for vulnerability estimation of typical structures in Port-au-Prince (Haiti).

After the 2010 Haiti earthquake, that hits the city of Port-au-Prince, capital city of Haiti, a multidisciplinary working group of specialists (seismologist, geologists, engineers and architects) from different Spanish Universities and also from Haiti, joined effort under the SISMO-HAITI project (financed by the Universidad Politecnica de Madrid), with an objective: Evaluation of seismic hazard and risk in Haiti and its application to the seismic design, urban planning, emergency and resource management. In this paper, as a first step for a structural damage estimation of future earthquakes in the country, a calibration of damage functions has been carried out by means of a two-stage procedure. After compiling a database with observed damage in the city after the earthquake, the exposure model (building stock) has been classified and through an iteratively two-step calibration process, a specific set of damage functions for the country has been proposed. Additionally, Next Generation Attenuation Models (NGA) and Vs30 models have been analysed to choose the most appropriate for the seismic risk estimation in the city. Finally in a next paper, these functions will be used to estimate a seismic risk scenario for a future earthquake.

Using the damage from 2010 Haiti earthquake for calibrating vulnerability models of typical structures in Port-au-Prince (Haiti)

After the 2010 Haiti earthquake, that hits the city of Port-au-Prince, capital city of Haiti, a multidisciplinary working group of specialists (seismologist, geologists, engineers and architects) from different Spanish Universities and also from Haiti, joined effort under the SISMO-HAITI project (financed by the Universidad Politecnica de Madrid), with an objective: Evaluation of seismic hazard and risk in Haiti and its application to the seismic design, urban planning, emergency and resource management. In this paper, as a first step for a structural damage estimation of future earthquakes in the country, a calibration of damage functions has been carried out by means of a two-stage procedure. After compiling a database with observed damage in the city after the earthquake, the exposure model (building stock) has been classified and through an iteratively two-step calibration process, a specific set of damage functions for the country has been proposed. Additionally, Next Generation Attenuation Models (NGA) and Vs30 models have been analysed to choose the most appropriate for the seismic risk estimation in the city. Finally in a next paper, these functions will be used to estimate a seismic risk scenario for a future earthquake.

Endogenous Timing in Pollution Control: Stackelberg versus Cournot-Nash Equilibria

27 p.

The impacts of climate change on river flood risk at the global scale

This paper presents an assessment of the implications of climate change for global river flood risk. It is based on the estimation of flood frequency relationships at a grid resolution of 0.5 × 0.5°, using a global hydrological model with climate scenarios derived from 21 climate models, together with projections of future population. Four indicators of the flood hazard are calculated; change in the magnitude and return period of flood peaks, flood-prone population and cropland exposed to substantial change in flood frequency, and a generalised measure of regional flood risk based on combining frequency curves with generic flood damage functions. Under one climate model, emissions and socioeconomic scenario (HadCM3 and SRES A1b), in 2050 the current 100-year flood would occur at least twice as frequently across 40 % of the globe, approximately 450 million flood-prone people and 430 thousand km2 of flood-prone cropland would be exposed to a doubling of flood frequency, and global flood risk would increase by approximately 187 % over the risk in 2050 in the absence of climate change. There is strong regional variability (most adverse impacts would be in Asia), and considerable variability between climate models. In 2050, the range in increased exposure across 21 climate models under SRES A1b is 31–450 million people and 59 to 430 thousand km2 of cropland, and the change in risk varies between −9 and +376 %. The paper presents impacts by region, and also presents relationships between change in global mean surface temperature and impacts on the global flood hazard. There are a number of caveats with the analysis; it is based on one global hydrological model only, the climate scenarios are constructed using pattern-scaling, and the precise impacts are sensitive to some of the assumptions in the definition and application.

Análise da modelagem experimental da perda de injetividade

Injectivity decline, which can be caused by particle retention, generally occurs during water injection or reinjection in oil fields. Several mechanisms, including straining, are responsible for particle retention and pore blocking causing formation damage and injectivity decline. Predicting formation damage and injectivity decline is essential in waterflooding projects. The Classic Model (CM), which incorporates filtration coefficients and formation damage functions, has been widely used to predict injectivity decline. However, various authors have reported significant discrepancies between Classical Model and experimental results, motivating the development of deep bed filtration models considering multiple particle retention mechanisms (Santos & Barros, 2010; SBM). In this dissertation, inverse problem solution was studied and a software for experimental data treatment was developed. Finally, experimental data were fitted using both the CM and SBM. The results showed that, depending on the formation damage function, the predictions for injectivity decline using CM and SBM models can be significantly different

Index-Oriented Methodologies for Landslide Consequence Analysis: An Application to a Mountain Community in the French Alps

Consequence analysis is a key aspect of anchoring assessment of landslide impacts to present and long-term development planning. Although several approaches have been developed over the last decade, some of them are difficult to apply in practice, mainly because of the lack of valuable data on historical damages or on damage functions. In this paper, two possible consequence indicators based on a combination of descriptors of the exposure of the elements at risk are proposed in order to map the potential impacts of landslides and highlight the most vulnerable areas. The first index maps the physical vulnerability due to landslide; the second index maps both direct damage (physical, structural, functional) and indirect damage (socio-economic impacts) of landslide hazards. The indexes have been computed for the 200 km2 area of the Barcelonnette Basin (South French Alps), and their potential applications are discussed.

Estimación del riesgo sísmico en puerto príncipe (Haití)

Tras el devastador terremoto del 12 de enero de 2010 en Puerto Príncipe, Haití, las autoridades locales, numerosas ONGs y organismos nacionales e internacionales están trabajando en el desarrollo de estrategias para minimizar el elevado riesgo sísmico existente en el país. Para ello es necesario, en primer lugar, estimar dicho riesgo asociado a eventuales terremotos futuros que puedan producirse, evaluando el grado de pérdidas que podrían generar, para dimensionar la catástrofe y actuar en consecuencia, tanto en lo referente a medidas preventivas como a adopción de planes de emergencia. En ese sentido, este Trabajo Fin de Master aporta un análisis detallado del riesgo sísmico asociado a un futuro terremoto que podría producirse con probabilidad razonable, causando importantes daños en Puerto Príncipe. Se propone para ello una metodología de cálculo del riesgo adaptada a los condicionantes de la zona, con modelos calibrados empleando datos del sismo de 2010. Se ha desarrollado en el marco del proyecto de cooperación Sismo-Haití, financiado por la Universidad Politécnica de Madrid, que comenzó diez meses después del terremoto de 2010 como respuesta a una petición de ayuda del gobierno haitiano. El cálculo del riesgo requiere la consideración de dos inputs: la amenaza sísmica o movimiento esperado por el escenario definido (sismo de cierta magnitud y localización) y los elementos expuestos a esta amenaza (una clasificación del parque inmobiliario en diferentes tipologías constructivas, así como su vulnerabilidad). La vulnerabilidad de estas tipologías se describe por medio de funciones de daño: espectros de capacidad, que representan su comportamiento ante las fuerzas horizontales motivadas por los sismos, y curvas de fragilidad, que representan la probabilidad de que las estructuras sufran daños al alcanzar el máximo desplazamiento horizontal entre plantas debido a la mencionada fuerza horizontal. La metodología que se propone especifica determinadas pautas y criterios para estimar el movimiento, asignar la vulnerabilidad y evaluar el daño, cubriendo los tres estados del proceso. Por una parte, se consideran diferentes modelos de movimiento fuerte incluyendo el efecto local, y se identifican los que mejor ajustan a las observaciones de 2010. Por otra se clasifica el parque inmobiliario en diferentes tipologías constructivas, en base a la información extraída en una campaña de campo y utilizando además una base de datos aportada por el Ministerio de Obras Públicas de Haití. Ésta contiene información relevante de todos los edificios de la ciudad, resultando un total de 6 tipologías. Finalmente, para la estimación del daño se aplica el método capacidad-demanda implementado en el programa SELENA (Molina et al., 2010). En primer lugar, utilizado los datos de daño del terremoto de 2010, se ha calibrado el modelo propuesto de cálculo de riesgo sísmico: cuatro modelos de movimiento fuerte, tres modelos de tipo de suelo y un conjunto de funciones de daño. Finalmente, con el modelo calibrado, se ha simulado un escenario sísmico determinista correspondiente a un posible terremoto con epicentro próximo a Puerto Príncipe. Los resultados muestran que los daños estructurales serán considerables y podrán llevar a pérdidas económicas y humanas que causen un gran impacto en el país, lo que pone de manifiesto la alta vulnerabilidad estructural existente. Este resultado será facilitado a las autoridades locales, constituyendo una base sólida para toma de decisiones y adopción de políticas de prevención y mitigación del riesgo. Se recomienda dirigir esfuerzos hacia la reducción de la vulnerabilidad estructural - mediante refuerzo de edificios vulnerables y adopción de una normativa sismorresistente- y hacia el desarrollo de planes de emergencia. Abstract After the devastating 12 January 2010 earthquake that hit the city of Port-au-Prince, Haiti, strategies to minimize the high seismic risk are being developed by local authorities, NGOs, and national and international institutions. Two important tasks to reach this objective are, on the one hand, the evaluation of the seismic risk associated to possible future earthquakes in order to know the dimensions of the catastrophe; on the other hand, the design of preventive measures and emergency plans to minimize the consequences of such events. In this sense, this Master Thesis provides a detailed estimation of the damage that a possible future earthquake will cause in Port-au-Prince. A methodology to calculate the seismic risk is proposed, adapted to the study area conditions. This methodology has been calibrated using data from the 2010 earthquake. It has been conducted in the frame of the Sismo-Haiti cooperative project, supported by the Technical University of Madrid, which started ten months after the 2010 earthquake as an answer to an aid call of the Haitian government. The seismic risk calculation requires two inputs: the seismic hazard (expected ground motion due to a scenario earthquake given by magnitude and location) and the elements exposed to the hazard (classification of the building stock into building typologies, as well as their vulnerability). This vulnerability is described through the damage functions: capacity curves, which represent the structure performance against the horizontal forces caused by the seisms; and fragility curves, which represent the probability of damage as the structure reaches the maximum spectral displacement due to the horizontal force. The proposed methodology specifies certain guidelines and criteria to estimate the ground motion, assign the vulnerability, and evaluate the damage, covering the whole process. Firstly, different ground motion prediction equations including the local effect are considered, and the ones that have the best correlation with the observations of the 2010 earthquake, are identified. Secondly, the classification of building typologies is made by using the information collected during a field campaign, as well as a data base provided by the Ministry of Public Works of Haiti. This data base contains relevant information about all the buildings in the city, leading to a total of 6 different typologies. Finally, the damage is estimated using the capacity-spectrum method as implemented in the software SELENA (Molina et al., 2010). Data about the damage caused by the 2010 earthquake have been used to calibrate the proposed calculation model: different choices of ground motion relationships, soil models, and damage functions. Then, with the calibrated model, a deterministic scenario corresponding to an epicenter close to Port-au-Prince has been simulated. The results show high structural damage, and therefore, they point out the high structural vulnerability in the city. Besides, the economic and human losses associated to the damage would cause a great impact in the country. This result will be provided to the Haitian Government, constituting a scientific base for decision making and for the adoption of measures to prevent and mitigate the seismic risk. It is highly recommended to drive efforts towards the quality control of the new buildings -through reinforcement and construction according to a seismic code- and the development of emergency planning.

Modeling Flood Stage-Duration-Frequency: A Risk Assessment of Critical Infrastructure in the Tidal Potomac

The service of a critical infrastructure, such as a municipal wastewater treatment plant (MWWTP), is taken for granted until a flood or another low frequency, high consequence crisis brings its fragility to attention. The unique aspects of the MWWTP call for a method to quantify the flood stage-duration-frequency relationship. By developing a bivariate joint distribution model of flood stage and duration, this study adds a second dimension, time, into flood risk studies. A new parameter, inter-event time, is developed to further illustrate the effect of event separation on the frequency assessment. The method is tested on riverine, estuary and tidal sites in the Mid-Atlantic region. Equipment damage functions are characterized by linear and step damage models. The Expected Annual Damage (EAD) of the underground equipment is further estimated by the parametric joint distribution model, which is a function of both flood stage and duration, demonstrating the application of the bivariate model in risk assessment. Flood likelihood may alter due to climate change. A sensitivity analysis method is developed to assess future flood risk by estimating flood frequency under conditions of higher sea level and stream flow response to increased precipitation intensity. Scenarios based on steady and unsteady flow analysis are generated for current climate, future climate within this century, and future climate beyond this century, consistent with the WWTP planning horizons. The spatial extent of flood risk is visualized by inundation mapping and GIS-Assisted Risk Register (GARR). This research will help the stakeholders of the critical infrastructure be aware of the flood risk, vulnerability, and the inherent uncertainty.

Análise da modelagem experimental da perda de injetividade

Injectivity decline, which can be caused by particle retention, generally occurs during water injection or reinjection in oil fields. Several mechanisms, including straining, are responsible for particle retention and pore blocking causing formation damage and injectivity decline. Predicting formation damage and injectivity decline is essential in waterflooding projects. The Classic Model (CM), which incorporates filtration coefficients and formation damage functions, has been widely used to predict injectivity decline. However, various authors have reported significant discrepancies between Classical Model and experimental results, motivating the development of deep bed filtration models considering multiple particle retention mechanisms (Santos & Barros, 2010; SBM). In this dissertation, inverse problem solution was studied and a software for experimental data treatment was developed. Finally, experimental data were fitted using both the CM and SBM. The results showed that, depending on the formation damage function, the predictions for injectivity decline using CM and SBM models can be significantly different

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Damage identification and condition assessment of building structures using frequency response functions and neural networks

This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.