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.

翻译：本论文提出了一种新颖的框架，通过应用工程学和材料科学原理来分析武装冲突的社会影响。该框架基于"社会结构"的概念，将社区重新定义为具有属性的"板材"，其韧性、脆弱性等属性类似于材料的厚度或弹性等参数。冲突事件被视为使这一结构发生形变的外部力量，揭示了反复冲击与局部薄弱点如何随时间推移而相互叠加。通过使用基于Python的自定义有限元分析实现，本论文展示了如何将社会经济指标（如基础设施、健康状况和人口统计数据）与冲突事件数据转化为单一的计算模型。初步测试验证了结果与预期的物理行为相符，概念验证则突显了该方法如何捕捉间接或溢出效应，并揭示最有可能遭受长期损害的区域。通过将社会科学见解与计算建模相结合，这项工作为受暴力影响的社区提供了一个适应性强的框架，可为学术研究和实地政策决策提供参考。