Understanding, modelling, and reducing time-varying risk induced by natural and man-made hazards
|
Among different types of natural-hazard
interactions, some occur through the
nature of the hazards themselves,
regardless of the presence of any physical
assets: they are often called “Level I”
(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 “Level II” (or
consequence) interactions, which instead
occur through impacts/consequences on
physical assets/components and systems.
Multi-hazard Life Cycle Analysis (LCA)
aims to quantify the consequences (e.g.,
repair costs, downtime, and casualty
rates) expected throughout a system’s
service life, accounting for Level I and
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 work 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’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’s life cycle,
which can be integrated into LCA
frameworks to quantify interacting hazard
consequences.
|
|
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’ 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. The aim of this
work is to detail 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.
Collaborators: Vibek Manandhar (NSET), Januka Gyawali (Practical Action), Prayash Malla (NSET), Roberto Gentile (UCL), Maggie Creed (University of
Glasgow), Ramesh Guragain (NSET), and
Carmine Galasso (UCL)
|
|
In this work, we propose a general
formulation to model the physical state
and functionality of deteriorating
infrastructure throughout its service
life. The paper unifies physics-based
models for deterioration and recovery. It
also develops resilience metrics to
quantify the temporal and spatial
variations of the infrastructure’s ability
to recover after disruptive events. These
metrics can assess the disparity in the
recovery process across various sections
of the infrastructure. The practical
application of this framework is
demonstrated through a case study on the
water infrastructure of a coastal
community in Oregon, USA.
|
|
In this work we develop probabilistic
physics-based Repair Rate (RR) curves to
quantify the damage to segmented pipelines
due to seismic excitations. First, a
mechanical model for segmented pipelines
is proposed. Then, the model is used to
generate a set of synthetic data to
calibrate the model parameters of the
developed probabilistic formulation. The
proposed RR curves are compared with the
ones available in the literature, and the
advantages of the proposed formulation are
discussed.
|
|
In this work we develop a novel numerical
method based on physics-based mixture
models for the transient and steady-state
solutions of the Fokker–Planck equation.
The unknown parameters of the mixture
model are estimated via Bayesian inference
while considering the physical constraints
on their numerical value. The proposed
method can be used to efficiently estimate
the time-varying distribution of variables
that evolve according to processes
described by stochastic differential
equations.
|
|
In this work we developed an innovative
analytical framework to predict the
capacity of novel tank car designs to
resist puncturing forces during
derailments. A finite element model is
developed to generate impact forces on
railcars after the initiation of train
derailments. Such forces are then
integrated within a probabilistic
framework to predict the probability of
puncture and release of hazardous
materials into the environment.
Collaborators: Steven W. Kirkpatrick (ARA), Chen-Yu Lin (UIUC), Paul Gharzouzi (UIUC), Todd Treichel (RSI-AAR), Christopher P.L. Barkan (UIUC) and
Paolo Gardoni (UIUC)
|
|
In this work we develop probabilistic
capacity models for unreinforced masonry
walls with hollow clay units subject to
horizontal in-plane forces. The models are
developed considering diagonal cracking,
flexural/rocking, and sliding failure as
possible failure modes. They are
constructed from existing physics-based
models that attempt to capture the
underlying physics, then integrated with
correction terms that improve their
accuracy and remove the inherent bias.
Unknown parameters for the proposed models
are calibrated using a Bayesian updating
approach.
|
|
In this work we propose a formulation to
compute the Value of Information (VoI) of
inspections and structural health
monitoring for degrading engineering
systems. In the proposed formulation, the
information collected is used to improve
the prediction of the effects of gradual
and shock deterioration processes and the
future probability of failure. The results
of the VoI analysis can be used to
evaluate whether it is convenient to
install monitoring and/or perform
inspections on a given system (such as a
bridge) after a disastrous event.
Collaborators: Pier Francesco Giordano , Maria Pina Limongelli (Politecnico di Milano) and
Paolo Gardoni (UIUC)
|
|
In this work we propose to use a system
of Stochastic Differential Equations to
model the evolution of the physical
properties of an engineering system over
time. The proposed formulation captures
the continuous nature of the degradation
processes acting on the system and
considers their possible interactions. In
addition, results from stochastic calculus
are used to facilitate the simulation of
the processes and to obtain closed-form
solutions for the distribution of the
physical properties over time. An extended
version of this conference paper has been
submitted to the Journal for Reliability
Engineering and System Safety and is
currently under review.
|
© 2026 Leandro Iannacone. All rights reserved. |
|
|
