Applying Computational Engineering Modelling to Analyse the Social Impact of Conflict and Violent Events

TL;DR

The work addresses the need for location-specific analysis of conflict impacts by introducing a physics-informed social fabric framework that treats communities as thin plates with thickness $h$, Young's modulus $E$, and Poisson's ratio $\nu$, while conflict events act as external forces. It employs a Python-based Finite Element Method implementation of Kirchhoff-Love plate theory, with a bending stiffness $D=\frac{E h^3}{12(1-\nu^2)}$, to map social indicators to material properties and conflict data to force fields, enabling displacement as a proxy for social impact and the visualization of spatial spillovers. A proof-of-concept demonstrates how repeated and overlapping events interact with spatially heterogeneous resilience and vulnerability, offering a unified framework that bridges social science insights with physically grounded modelling. The study highlights both the potential advantages—capturing indirect effects and allowing scenario testing—and limitations, including static social fabric assumptions and data challenges, and it outlines a path toward validation, time-evolving extensions, and ethical considerations. Overall, the physics-based social fabric provides a transparent, adaptable tool to inform research and policy decisions in violence-affected regions by revealing how local vulnerabilities shape the spread and intensification of conflict impacts.

Abstract

This thesis presents a novel framework for analysing the societal impacts of armed conflict by applying principles from engineering and material science. Building on the idea of a "social fabric", it recasts communities as plates with properties, such as resilience and vulnerability, analogous to material parameters like thickness or elasticity. Conflict events are treated as external forces that deform this fabric, revealing how repeated shocks and local weaknesses can compound over time. Using a custom Python-based Finite Element Analysis implementation, the thesis demonstrates how data on socioeconomic indicators (e.g., infrastructure, health, and demographics) and conflict incidents can be translated into a single computational model. Preliminary tests validate that results align with expected physical behaviours, and a proof-of-concept highlights how this approach can capture indirect or spillover effects and illuminate the areas most at risk of long-term harm. By bridging social science insights with computational modelling, this work offers an adaptable frame to inform both academic research and on-the-ground policy decisions for communities affected by violence.

Figures (48)

• Figure 1: Finite element mesh representation showing a four-node quadrilateral element with three degrees of freedom per node: vertical displacement ($w$) and rotations about the x- and y-axes ($\theta_x$, $\theta_y$). These nodal DOFs determine the element's deformation behaviour under applied loads Kandaz2021FiniteMicroplates.
• Figure 2: Computational domain setup for FEA of Nigeria, showing the national boundary (red solid line), extended buffer zone boundary (blue dashed line), and applied clamped boundary conditions (red bars). The mesh grid overlay illustrates the spatial discretisation used for the finite element implementation.
• Figure 3: Effect of plate thickness on maximum displacement and affected area. The dual-axis plot demonstrates the inverse relationship between thickness and both response measures. The blue curve tracks maximum displacement (m) against the bottom axis, while the red curve shows the percentage of affected area against the top axis.
• Figure 4: Relationship between plate thickness and maximum von Mises stress. The curve illustrates the non-linear decay of the maximum von Mises stress with increasing thickness.
• Figure 5: 2D displacement field visualisations for increasing plate thickness: (a) 500 m, (b) 1,000 m, (c) 2,000 m, and (d) 5,000 m. The colour scale represents displacement magnitude, with more intensive reds indicating larger displacements.