1,721,007 research outputs found
Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks
Full text linkResilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is known for its capability of handling causal dependencies between different variables in probabilistic terms. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g. natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system's performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two illustrative examples are presented in the paper to demonstrate the applicability of the introduced framework. One example evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Integral Design & Managemen
A CAT bond-based coverage scheme proposal for Italy
Full text linkCatastrophe bonds (CAT bonds) are risk-linked securities used by the insurance industry to transfer risks associated with the occurrence of natural disasters to the capital markets. Despite their growing importance, relatively few studies on CAT bond pricing, design and their application are available in the literature. Indeed, existing pricing formulations for pricing analysis do not account for uncertainties in model parameters and are not contextualized in a more general CAT bond coverage design procedure for an area of interest with a distributed portfolio. For these reasons, this paper presents a general procedure for designing a CAT bond-based coverage for a spatially distributed portfolio against losses due to natural hazards. The procedure is then applied to a case study represented by the residential building portfolio in Italy, aiming to design a CAT bond-based coverage scheme against losses induced by seismic events all over the entire national borders
Probabilistic models for blast parameters and fragility estimates of steel columns subject to blast loads
Full text linkThis paper proposes a probabilistic framework to predict the failure probabilities of steel columns subject to blast loads. The framework considers the uncertainties in the blast phenomenon, the demands imposed on the column, and the capacities of the column for the limit states of flexure, and global buckling. As part of the work, we propose four probabilistic blast load models. For different types of explosives and atmospheric conditions, two models predict the incident and reflected peak pressure generated by the explosion and two models predict the incident and reflected positive time duration of the blast wave. The models are probabilistic to capture the associated uncertainties, including variations in the atmospheric conditions, the inherent variability in the blast load data even for identical experimental conditions, and model error. The blast load models are used to predict the structural demands (maximum internal moment and deflection) imposed by the blast on a column. The demand models are combined with strain-rate dependent capacity models for flexure and global buckling to estimate the conditional probability of failure (or fragility) of a steel column for given scaled distance. As an example, fragility estimates for different columns representative of typical columns in steel frames are developed. The results highlight the importance of the explosive weight and column axial load on the failure probabilities
Physics-based Demand Model and Fragility Functions of Industrial Tanks under Blast Loading
Full text linkBlast hazards represent a serious threat to industrial facilities. Past explosion incidents highlight the severe consequences of such events. A probabilistic approach can help industries and designers mitigate the consequences of blast loading by better organizing industrial plants. In this paper, we propose a physics-based probabilistic demand model and formulate the reliability problem for industrial steel tanks under blast loading. Starting from a deterministic Single-Degree-of-Freedom (SDOF) model based on Donnell shallow-shell theory, we develop a correction term that improves the model accuracy due to the simplified representation of the SDOF model. We use Bayesian inference to estimate the unknown model parameters in the correction term and model error, combining predictions from the SDOF model with experimental data and any prior information. To illustrate, we estimate the reliability of an example cylindrical steel tanks subject to blast loading considering three damage levels. The reliability analysis yields a set of fragility curves that represent the conditional probability of the bending failure of the tank given a scaled distance, as the load intensity measure. Then, as an example, we use the developed fragility functions to estimate the reliability of a chemical industrial facility considering different explosion scenarios
Time-Dependent Probability of Exceeding a Target Level of Recovery
Full text linkThe resilience of a system is generally defined in terms of its ability to withstand external perturbations, adapt, and rapidly recover. This paper introduces a probabilistic formulation to predict the recovery process of a system given past recovery data and to estimate the probability of reaching or exceeding a target value of functionality at any time. A Bayesian inference is used to capture the changes over time of model parameters as recovery data become available during the work progress. The proposed formulation is general and can be applied to continuous recovery processes such as those of economic or natural systems, as well as to discrete recovery processes typical of engineering systems. As an illustration of the proposed formulation, two examples are provided. The paper models the recovery of a reinforced concrete bridge following seismic damage, as well as the population relocation after the occurrence of a seismic event when no data on the duration of the recovery are available a priori
Risk-based catastrophe bond pricing
No full textCatastrophe bonds (CAT bond) are risk-linked securities used by the insurance industry to transfer risks associated with the occurrence of natural disasters to the capital markets (Cummins, 2008). CAT bonds are usually structured as coupon-paying bonds with a default linked to the occurrence of a trigger event. A commonly used trigger event is the overcoming of a loss threshold (Kunreuther and Pauly, 2010). Current formulations for pricing analysis do not account for the uncertainties in the model parameters. However, neglecting such uncertainties might result in assuming risks that are higher than intended. This paper develops a risk-based bond pricing formulation considering the uncertainties in the model parameters. The proposed formulation allows for the definition of CAT bond pricing for a pre-set acceptable level of risk. The proposed theory is illustrated with a numerical example
Probabilistic models of concrete compressive strength and elastic modulus with rubber aggregates
No full textThe disposal of waste rubber products is a significant issue globally and it poses a serious threat to the environment creating long-term ecological problems. The possible use of rubber aggregates in concrete is a valid alternative to obtain a green construction material. This paper develops probabilistic models for the concrete Strength Reduction Factor (SRF) and Elastic modulus Reduction Factor (ERF) accounting for the amount of rubber aggregates as well as several variables defining the mix design. Three different rubber aggregate types are considered, namely fine and coarse replaced individually and fine and coarse replaced simultaneously. A total of 644 sets of concrete compressive strength and elastic modulus tests are collected from the literature for the model calibration. The paper presents a discussion about the formulation of the models, variance stabilizing transformations, model calibration, and model selection. Once formulated, we calibrate the probabilistic models using data from experimental tests. The unknown model parameters are estimated using a Bayesian approach implemented using A Markov Chain Monte Carlo (MCMC) simulation method. The proposed probabilistic models are used to evaluate the reliability of rubberized concrete structures. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example column made of rubberized concrete under compressive axial force and of an example one-way slab made of rubberized concrete under distributed load
Probabilistic Models to Assess the Seismic Safety of Rigid Block-Like Elements and the Effectiveness of Two Safety Devices
No full textWhen subject to earthquakes, some objects and structures, such as statues, obelisks, storage systems, and transformers, show a dynamic behavior that can be modeled considering the object/structure as a rigid block. To reduce the likelihood of the failure of these kinds of elements, they can be paired with safety devices that are designed to avert the overturning of the blocks. Although safety devices have proven to be effective, their effectiveness changes substantially as a function of the seismic input (which is generally uncertain) and the parameters that characterize the system. Furthermore, there is a need to quantify probabilistically the effectiveness of safety devices. This paper proposes probabilistic models to evaluate the failure probability of block-like elements coupled with a safety device. The paper considers two candidate safety devices: an isolating base and a pendulum mass damper. The proposed models are then used to compare the seismic responses of coupled block-device systems with one of stand-alone rigid block-like elements. To account for the relevant uncertainties, expressions for the probability of failure are developed using a logistic regression model calibrated with a Bayesian approach. The expressions of the probability of failure are then used to construct fragility curves that give estimates of the conditional probability of overturning occurrence as a function of some characteristics of the blocks (i.e., the slenderness of the rigid body) and the safety devices (i.e., the characteristic period) for a given seismic excitation (i.e., the peak ground acceleration). The data needed to develop the probabilistic model are obtained integrating the nonlinear equations of motion of the two systems subject to selected ground motions. The proposed models and fragility estimates can be used to quantify the likelihood of failure of rigid block-like elements due to seismic excitations as well as the effectiveness of two common safety devices. It is found that, for the adopted ratio between the mass of the block-like element and the mass of the safety devices, base isolation works better than a pendulum mass damper
- …
