Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2204.04335

Measuring and improving community resilience: a Fuzzy Logic approach

Melissa De Iuliis·Omar Kammouh·Gian Paolo Cimellaro
View original source ↗Read source document ↗Export metadata ↓

Abstract

Due to the increasing frequency of natural and man-made disasters worldwide, the scientific community has paid considerable attention to the concept of resilience engineering in recent years. Authorities and decision-makers, on the other hand, have been focusing their efforts to develop strategies that can help increase community resilience to different types of extreme events. Since it is often impossible to prevent every risk, the focus is on adapting and managing risks in ways that minimize impacts to communities (e.g., humans and other systems). Several resilience strategies have been proposed in the literature to reduce disaster risk and improve community resilience. Generally, resilience assessment is challenging due to uncertainty and unavailability of data necessary for the estimation process. This paper proposes a Fuzzy Logic method for quantifying community resilience. The methodology is based on the PEOPLES framework, an indicator-based hierarchical framework that defines all aspects of the community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of hard data, accounting also for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, data regarding the functionality of the city San Francisco before and after the Loma Prieta earthquake are used to obtain a resilience index of the Physical Infrastructure dimension of the PEOPLES framework. The results show that the methodology can provide good estimates of community resilience despite the uncertainty of the indicators. Hence, it serves as a decision-support tool to help decision-makers and stakeholders assess and improve the resilience of their communities.

Explore related subjects

nlin.AO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Melissa De Iuliis, Omar Kammouh, Gian Paolo Cimellaro. 2022-04-09. Measuring and improving community resilience: a Fuzzy Logic approach. https://arxiv.org/abs/2204.04335

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A solvable normal form for coupled swarmalators

Swarmalators are mobile generalizations of phase oscillators. Introduced to model systems in which sync and self-assembly interact, they remain poorly understood theoretically. Unlike the Kuramoto model for coupled oscillators, existing swarmalator models lack a normal-form foundation, and their basic stabilities and bifurcations remain largely unsolved. Here we address both problems. Building on Tanaka's reduction of chemotactic oscillators, we show that the canonical one-dimensional swarmalator model -- previously introduced as an ad hoc toy model -- is recovered in the first-harmonic, zero-lag limit, implying its behavior is generic. We then derive the stability boundaries organizing its four collective states, show they meet at a single cusp, correct a previously published order-parameter formula, and uncover a non-monotonic sync response absent in the Kuramoto model.

nlin.AO↗
arXiv2026

Arithmetic of the sync basin for pulse-coupled oscillators

A population of $N$ identical pulse-coupled oscillators ultimately settles into one of two outcomes: full synchrony or a state of co-existing synchronized clusters. We show that which outcome occurs is controlled by the prime factorization of $N$. At the critical charging curve --- linear, the boundary between the synchronizing and clustering regimes --- the synchronization basin acquires exact arithmetic structure. The synchronization probability is $\Psync=A_{N,1}/N^N$ for all $N$, where $A_{N,1}$ satisfies an exact recurrence relation. For prime $N$, $A_{N,1}=N^N-1$ giving the closed form $\Psync=1-1/N^N$; for composite $N$, the observed asymptotic scaling is $1-\Psync\sim C_m N^{-(m-1)}$, where $m$ is the smallest prime divisor. The result adds a new member to the atlas of exotic basin geometries: alongside fractal, riddled, and tentacled basins, we now have a basin that is arithmetic.

nlin.AO↗
arXiv2026

Recovery Random Walks and Extreme Events on Complex Networks

Extreme events are widely studied within simple random walk frameworks, where their probability is determined by the network structure and stationary walker distribution. Here, we propose a recovery random walk (RRW) model in which extreme events temporally `freeze' the nodes where they occur for a fixed duration $Δ$ ($Δ=0$ recovers the original model), trapping walkers and reducing the effective mobile population, thereby making the model more practical. We derive a first-principles description of this feedback and a delayed differential equation for the frozen-node fraction. This finite freezing produces an initial overshoot, followed by damped oscillatory relaxation to a steady state for the fraction of the frozen nodes. We find that freezing suppresses extreme event probability while preserving its degree dependence dynamics, and suppresses the EE frequency. Further, the analytical prediction for the fraction of the frozen nodes agrees closely with simulations. This model closely reflects real-world scenarios, yielding more practical EE statistics.

nlin.AO↗