Simulating interacting multiple natural-hazard events for lifecycle consequence analysis

Among different types of natural-hazard interactions (simply multi-hazard interactions hereinafter), some occur through the nature of the hazards themselves, regardless of the presence of any physical assets: they are often called ﾓLevel Iﾔ (or occurrence) interactions. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., severe wind and flooding; liquefaction and landslides triggered by an earthquake), thus creating a dependency between the parameters characterising such hazard events. They differ from ﾓLevel IIﾔ (or consequence) interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impact due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard Life Cycle Analysis (LCA) aims to quantify the consequences (e.g., repair costs, downtime, and casualty rates) expected throughout a systemﾒs service life, accounting for both Level I and Level II interactions. Nevertheless, the available literature generally considers these interactions mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events in terms of their occurrence times and features and resulting consequences. This paper aims to partly fill this gap by identifying modelling approaches associated with different Level I interactions. It describes a simulation-based approach for generating multi-hazard scenarios (i.e., a sequence of hazard events and associated features through the systemﾒs life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs potential sequences of events throughout a systemﾒs life cycle, which can be integrated into LCA frameworks to quantify interacting hazard consequences. A simple application is presented to illustrate the potential of the proposed method.

PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts

Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources months before proper classification into structured databases. In order to facilitate timely discovery of such knowledge, we propose a novel semi-supervised learning algorithm, PACE, for identifying and classifying relevant entities in text sources. The main contribution of this paper is an enhancement of the traditional bootstrapping method for entity extraction by employing a time-memory trade-off that simultaneously circumvents a costly corpus search while strengthening pattern nomination, which should increase accuracy. An implementation in the cyber-security domain is discussed as well as challenges to Natural Language Processing imposed by the security domain.Comment: 6 pages, 3 figures, ieeeTran conference. International Conference on Machine Learning and Applications 201

A Markovian framework for multi-hazard life-cycle consequence analysis of deteriorating structural systems

Multiple-hazard (or simply multi-hazard) interactions are either disregarded or addressed inadequately in most existing computational risk modelling frameworks for natural hazards, leading to inaccurate life-cycle consequence estimates. This, in turn, can lead to ineffective risk-informed decisionmaking for disaster-mitigation strategies and/or resilience-enhancing policies. Probabilistic multi-hazard life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, and casualties over an asset’s service life) enables optimal life-cycle management of critical assets under uncertainties. However, despite recent advances, most available LCCon formulations fail to accurately incorporate the damage-accumulation effects due to incomplete (or absent) repairs in between different hazard events. This paper introduces a Markovian framework for efficient multi-hazard LCCon analysis of deteriorating structural systems, appropriately accounting for complex interactions between hazards and their effects on a system’s performance. The proposed framework can be used to test various risk management and adaptation pathways. Specifically, the Markovian assumption is used to model the probability of a system being in any performance level (e.g., damage or functionality state) after multiple hazards inducing either “shock deterioration” or “gradual deterioration”, as well as after potential repair actions given such deteriorating processes. The expected LCCon estimates are then obtained by combining the performance level distribution with suitable system-level consequence models. The proposed framework is illustrated for a case-study reinforced concrete building considering earthquake-induced ground motions and environmentally-induced corrosion deterioration during its service life

A Markovian framework to model life-cycle consequences of infrastructure systems in a multi-hazard environment

Existing frameworks for multi-hazard life-cycle consequence (LCCon) analysis typically disregard the interactions between multiple hazards and obtain the total LCCon as the sum of the consequences caused by the individual hazards modelled independently. This practice leads to inaccurate life-cycle consequence estimates and ineffective risk-informed decision-making for disaster-mitigation strategies and/or resilience-enhancing policies. In addition, most available LCCon formulations fail to accurately incorporate the damage-accumulation effects due to incomplete (or absent) repairs between different hazard events. To address these challenges, this paper introduces a Markovian framework for efficient multi-hazard LCCon analysis of deteriorating structural systems, appropriately accounting for complex interactions between hazards and their effects on a system’s performance. The changes in the system’s performance level (e.g., damage or functionality state) are quantified with transition probability matrices following the Markovian assumption and the expected LCCon estimates are obtained by combining the performance level distribution with suitable system-level consequence models, which can include direct asset losses as well as socio-economic consequences. To showcase the framework applicability, a simple road network with a single case-study ordinary reinforced concrete bridge subject to earthquake-induced ground motions and environmentally-induced corrosion deterioration is investigated, estimating consequences in terms of community welfare loss

Seismic Fragility Analysis of Deteriorating Reinforced Concrete Buildings from a Life-Cycle Perspective

Structural systems in seismically-active regions typically undergo multiple ground-motion sequences during their service life (including multiple mainshocks, mainshocks triggering other earthquakes on nearby fault segments, mainshock-aftershock, and aftershock-aftershock sequences). These successive ground motions can lead to severe structural/non-structural damage and significant direct/indirect earthquake-induced losses. Nevertheless, the effects of a pre-damaged state during ground-motion sequences are often neglected in assessing structural performance. Additionally, environmentally-induced deterioration mechanisms may exacerbate the consequences of such groundmotion sequences during the structural system’s designed lifetime. Yet, such combined effects are commonly overlooked. This paper proposes an end-to-end computational methodology to derive timeand state-dependent fragility relationships (i.e., explicitly depending on time and the damage state achieved by a system during a first shock) for structural systems subjected to chloride-induced corrosion deterioration and earthquake-induced ground-motion sequences. To this aim, a vector-valued probabilistic seismic demand model is developed. Such a model relates the dissipated hysteretic energy in the ground-motion sequence to the maximum inter-storey drift induced by the first shock and the intensity measure of the second shock for a given corrosion deterioration level. Moreover, a vectorvalued generalised logistic model is developed to estimate the probability of collapse, conditioning on the same parameters as above. An appropriate chloride-penetration model is then used to model the timevarying evolution of fragility relationships’ parameters using a plain Monte-Carlo approach, capturing the continuous nature of the deterioration processes (i.e., gradual and shock deterioration). The significant impact of such a multi-hazard threat on structural fragility is demonstrated by utilising a casestudy reinforced concrete building. Due to deteriorating effects, reductions up to 33.3% can be noticed in the fragility median values

Simulation-based flood fragility and vulnerability analysis for expanding cities

Accurately quantifying flood-induced impacts on buildings and other infrastructure systems is essential for risk-sensitive planning and decision-making in expanding urban regions. Flood-induced impacts are directly related to the physical components of assets damaged due to contact with water. Such components include building contents (e.g., appliances, furniture) and other non-structural components whose damage/unavailability can severely impact the buildingsﾒ functionality. Conventional fragility analysis approaches for flooding do not account for the physical damage to the individual components, mostly relying on empirical methods based on historical data. However, recent studies proposed simulation-based, assembly-based fragility models that account for the damage to the building components. Such fragility models require developing detailed inventories of vulnerable components of households and identifying building archetypes to be considered in a building portfolio for the region of interest. Content inventories and building portfolios have so far been obtained for specific socio-economic contexts such as the United States of America. However, building types and their content can significantly differ between countries, making the available fragility models and computational frameworks unsuitable for flood vulnerability analysis in rapidly expanding cities characterised by extensive informal settlements, such as low- and middle-income countries. This paper details how to adapt the available methodologies for flood vulnerability assessment to the context of formal and informal settlements of expanding cities in the global south. It also details the development of content inventories for households in these cities using field surveys. The proposed survey is deployed in various areas vulnerable to floods in Kathmandu, Nepal. Based on the survey results, each component within the household is associated with a corresponding flood capacity (resistance) distribution (in terms of water height and flood duration). These distributions are then employed in a simulation-based probabilistic framework to obtain fragility relationship and consequence models. The relevant differences between the results obtained in this study and those from previous studies are then investigated for a case-study building type. In addition, the influence of socio-economic factors (e.g., household income) and past flood experience (possibly resulting in various flood-risk mitigation strategies at a household level) on the resulting flood impacts is also included in the model