Fatemeh Jalayer

Flood risk assessment for informal settlements

The urban informal settlements are particularly vulnerable to flooding events, due to both their generally poor quality of construction and high population density. An integrated approach to the analysis of flooding risk of informal settlements should take into account, and propagate, the many sources of uncertainty affecting the problem, ranging from the characterization of rainfall curve and flooding hazard to the characterization of the vulnerability of the portfolio of buildings. This paper proposes a probabilistic and modular approach for calculating the flooding risk in terms of the mean annual frequency of exceeding a specific limit state for each building within the informal settlement and the expected number of people affected (if the area is not evacuated). The flooding risk in this approach is calculated by the convolution of flooding hazard and flooding fragility for a specified limit state for each structure within the portfolio of buildings. This is achieved by employing the flooding height as an intermediate variable bridging over the fragility and hazard calculations. The focus of this paper is on an ultimate limit state where the life of slum dwellers is endangered by flooding. The fragility is calculated by using a logic tree procedure where several possible combinations of building features/construction details, and their eventual outcome in terms of the necessity to perform structural analysis or the application of nominal threshold flood heights, are taken into account. The logic tree branch probabilities are characterized based on both the orthophoto recognition and the sample in situ building survey. The application of the methodology is presented for Suna, a sub-ward of Dar es Salaam City (Tanzania) in the Msimbazi River basin having a high concentration of informal settlements.

Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Abstract Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia.

Bayesian Cloud Analysis: efficient structural fragility assessment using linear regression

Cloud Analysis is based on simple regression in the logarithmic space of structural response versus seismic intensity for a set of registered records. A Bayesian take on the Cloud Analysis, presented herein, manages to take into account both record-to-record variability and other sources of uncertainty related to structural modelling. First, the structural response to a suite of ground motions, applied to different realizations of the structural model generated through a standard Monte Carlo, is obtained. The resulting suite of structural response is going to be used as “data” in order to update the joint probability distribution function for the two regression parameters and the conditional logarithmic standard deviation. In the next stage, large-sample MC simulation based on the updated joint probability distribution is used to generate a set of plausible fragility curves. The robust fragility is estimated as the average of the generated fragility curves. The dispersion in the robust fragility is estimated as the variance of the plausible fragility curves generated. The plus/minus one standard deviation confidence interval for the robust fragility depends on the size of the sample of “data” employed. Application of the Bayesian Cloud procedure for an existing RC frame designed only for gravity-loading demonstrates the effect of structural modelling uncertainties, such as the uncertainties in component capacities and those related to construction details. Moreover, a comparison of the resulting robust fragility curves with fragility curves obtained based on the Incremental Dynamic Analysis shows a significant dependence on both the structural performance measure adopted and the selection of the records.

Preliminary ranking of alternative scalar and vector intensity measures of ground shaking

In Performance-Based Earthquake Engineering, seismic demand in structures is predicted by building probabilistic seismic demand models that link measures of earthquake intensity (IMs) to measures of structural demand. Investigations are carried out herein for evaluating the predictive capability of a wide range of commonly-used scalar and vector-valued IMs for different peak-related demand parameters. To accomplish this goal, both efficiency and sufficiency of the candidate IMs are taken into account. The latter is evaluated with the recently-proposed “relative sufficiency measure”. This measure, which is derived based on information theory concepts, quantifies the amount of information gained (on average) by an IM relative to another about the demand parameter of interest. Evaluation of the IMs, herein, uses two sets of ground motions consisting of ordinary and pulse-like near-fault records. Two-dimensional RC frame structures, both fixed and isolated at the base, are selected. The most suitable IMs for predicting the considered different demand parameters and types of structure are identified in terms of both efficiency and sufficiency. The use of these most informative IMs is suggested to build improved probabilistic demand models.

Analyzing the Sufficiency of Alternative Scalar and Vector Intensity Measures of Ground Shaking Based on Information Theory

The seismic risk assessment of a structure in performance-based design (PBD) may be significantly affected by the representation of ground motion uncertainty. In PBD, the uncertainty in the ground motion is often represented by a probabilistic description of a scalar parameter, or low-dimensional vector of parameters, known as the intensity measure (IM), rather than a full probabilistic description of the ground motion time history in terms of a stochastic model. In this work, a new procedure employing relative sufficiency measure is introduced on the basis of information theory concepts to quantify the suitability of one IM relative to another in the representation of ground motion uncertainty. On the basis of this relative sufficiency measure, several alternative scalar- and vector-valued IMs are compared in terms of the expected difference in information they provide about a predicted structural response parameter, namely, the seismically induced drift in an existing reinforced-concrete frame structure. It is concluded that the most informative of the eight considered IMs for predicting the nonlinear drift response are two scalar IMs and a vector IM that depend only on the spectral ordinates at the periods of the first two (small-amplitude) modes of vibration.

Knowledge-Based Performance Assessment of Existing RC Buildings

One of the most challenging aspects of the seismic assessment of existing buildings is the characterization of structural modeling uncertainties. Recent codes, such as Eurocode 8, seem to synthesize the effect of structural modeling uncertainties in the so-called confidence factors that are applied to mean material property estimates. The confidence factors are classified and tabulated as a function of discrete knowledge levels acquired based on the results of specific in-situ tests and inspections. In this approach, the effect of the application of the confidence factors on structural assessment is not explicitly stated. This work presents probabilistic performance-based proposals for seismic assessments of RC buildings based on the knowledge levels. These proposals take advantage of the Bayesian framework for updating the probability distributions for structural modeling parameters based on the results of tests and inspections. As structural modeling parameters, both the mechanical material properties and also the structural detailing parameters are considered. These proposals can be categorized based both on the amount of structural analysis effort required and on the type of structural analysis performed. An efficient Bayesian method is presented which relies on simplified assumptions and employs a small sample of structural model realizations and ground motion records in order to provide an estimate of structural reliability. As an alternative proposal suitable for code implementation, the simplified approach implemented in the SAC-FEMA guidelines is adapted to existing structures by employing the efficient Bayesian method. This method takes into account the effect of both ground motion uncertainty and the structural modeling uncertainties on the global performance of the structure, in a closed-form analytical safety-checking format. These alternative proposals are demonstrated for the case study structure which is an existing RC frame. In particular, it is shown how the parameters for the safety-checking format can be estimated and tabulated as a function of knowledge level, outcome of tests, and the type of structural analysis adopted.

A multi-dimensional assessment of urban vulnerability to climate change in Sub-Saharan Africa

In this paper, we develop and apply a multi-dimensional vulnerability assessment framework for understanding the impacts of climate change-induced hazards in Sub-Saharan African cities. The research was carried out within the European/African FP7 project CLimate change and Urban Vulnerability in Africa, which investigated climate change-induced risks, assessed vulnerability and proposed policy initiatives in five African cities. Dar es Salaam (Tanzania) was used as a main case with a particular focus on urban flooding. The multi-dimensional assessment covered the physical, institutional, attitudinal and asset factors influencing urban vulnerability. Multiple methods were applied to cover the full range of vulnerabilities and to identify potential response strategies, including: model-based forecasts, spatial analyses, document studies, interviews and stakeholder workshops. We demonstrate the potential of the approach to assessing several dimensions of vulnerability and illustrate the complexity of urban vulnerability at different scales: households (e.g., lacking assets); communities (e.g., situated in low-lying areas, lacking urban services and green areas); and entire cities (e.g., facing encroachment on green and flood-prone land). Scenario modeling suggests that vulnerability will continue to increase strongly due to the expected loss of agricultural land at the urban fringes and loss of green space within the city. However, weak institutional commitment and capacity limit the potential for strategic coordination and action. To better adapt to urban flooding and thereby reduce vulnerability and build resilience, we suggest working across dimensions and scales, integrating climate change issues in city-level plans and strategies and enabling local actions to initiate a ‘learning-by-doing’ process of adaptation.

Adaptive daily forecasting of seismic aftershock hazard

Abstract Seismic aftershock‐hazard analysis is one of the first steps toward establishing an integrated risk‐based decision‐making support framework for emergency management in the event of an ongoing aftershock sequence. This work focuses on providing adaptive daily forecasts of the mean daily rate of exceeding various spectral acceleration values (the aftershock hazard). Two well‐established earthquake‐occurrence models suitable for daily seismicity forecasts associated with the evolution of an aftershock sequence, namely, the modified Omori’s aftershock model (MO) and the epidemic‐type aftershock sequence (ETAS) are adopted. An adaptive and evolutionary MO‐based aftershock occurrence model with distinct spatial and temporal components is proposed. In this model, the parameters deciding the temporal decay are updated based on the data provided by the ongoing aftershock sequence. This model adopts an evolutionary spatial seismicity pattern loosely based on spatial clustering of aftershock events in the sequence. Bayesian updating is also employed to provide sequence‐based parameter estimates for a given ground‐motion prediction model. Daily forecasts of the mean rate of exceedance of various spectral acceleration levels are calculated based on alternative occurrence models and the updated ground‐motion prediction relation. As a numerical example, daily forecasts of the aftershock‐hazard curve are obtained for the L’Aquila aftershock sequence based on the MO‐based and ETAS occurrence models, and an updated version of the Sabetta and Pugliese (1996) ground‐motion prediction model. These daily hazard forecasts are then compared with the observed daily rates of exceeding various spectral acceleration thresholds.

Towards quantifying the effect of aftershocks in seismic risk assessment

The state of the art in seismic design and assessment of structures implicitly relies on structural redundancy in order to deal with the effect of the triggered sequence of aftershock events on a building. Calculating the time-dependent limit state exceedance probability for a structure considering both the main event and the triggered sequence of aftershocks is complicated both by the time-dependent rate of aftershock occurrence and also the cumulative damage caused by the sequence of events. Taking advantage of a methodology developed in previous works by the authors for postmainshock risk assessment, the limit state probability due to a sequence of a mainshock and the triggered aftershocks is calculated herein. Moreover, a closedand analytic-form approximation to the post-mainshock limit state probability given the mainshock magnitude is derived. This closedand analytic formulation facilitates the estimation of the limit state probability by employing the standard tools in risk assessment such as the fragility curve for the intact structure. Applying the proposed methodology for a reinforced concrete (RC) concrete moment-resisting frame with infills subjected to the main event and the triggered sequence emphasizes the importance of taking into account the cumulative damage caused by the triggered sequence for the case-study model. Moreover, it is demonstrated that the proposed analytical approximation based on the fragility curve for the intact structure leads to a surprisingly close agreement with the best-estimate results obtained by considering the timeand event-dependent degradation in the structure.

A Case-Study On Scenario-Based Probabilistic Seismic Loss Assessment For A Portfolio Of Bridges

ABSTRACT The majority of bridge infrastructure in Italy has been built in the 60’s and 70’s without specific seismic provisions. Therefore, it is expected that they reveal high seismic vulnerability if subjected to a significant seismic event. Given this background, it is natural that rapid and accurate assessment of economic losses incurred to bridge infrastructure can play a crucial role in emergency management in the immediate aftermath of an earthquake. Focusing on the infrastructure system of highway bridges in the Campania region, it is shown how state of the art methodologies in portfolio loss assessment and available data can be implemented in order to assess the probability distribution of the repair costs incurred to the portfolio in question due the Irpinia 1980 earthquake. Formulating the probabilistic loss assessment for the portfolio of bridges as a standard Monte Carlo Simulation problem helps in resolving the spatial risk integral efficiently. One of the specific features of this case-study is the use of statistical methods for updating, a) ground motion prediction; b) vulnerability/fragility; and c) exposure/cost models based on available data. It has been observed that alternative hypotheses with regard to the ground motion correlation structure can significantly affect the distribution of the direct economic loss. Furthermore, updating the ground motion prediction based on available recordings may significantly reduce the dispersion in the estimation of the direct economic losses. 1. INTRODUCTION In the immediate aftermath of a strong earthquake, the road networks play a crucial role in rescue and recovery operations. Since their loss of functionality may undermine the performance of the entire network, the bridge infrastructure can be considered as the so-called weak links within a road network affected by an earthquake. A large part of the Italian bridge infrastructure portfolio, dating back to the first half/beginning of the second half of the last century, is not designed based on earthquake resistant criteria. Therefore, the Italian highway bridges are potentially vulnerable to seismic actions, considering that the Italian territory is classified by medium to high seismicity. The total direct losses incurred to the bridge infrastructure in a road network can be used as a scalar proxy for the performance of the entire network right after the seismic event. This quantity encompasses various parameters, such