Development and Performance Evaluation of Lightweight Mitigation Systems for Extreme Dynamic Loading Scenarios

Abstract

The growing frequency and sophistication of terrorism-related incidents, ranging from severe explosions to high-impact velocity loadings such as projectiles and vehicle-borne improvised explosive devices (VBIEDs), pose escalating threats to public safety, infrastructure resilience, and national security. These attacks often target critical infrastructures and vehicles, whether civilian or military, causing severe socio-economic damage and human casualties. As such, developing protection systems is continuously essential, as it is now clearer than ever before that the competition between terrorist attacks and protection systems is a never-ending competition. In response to these challenges, the current research dissertation aims at developing and validating the performance of several mitigation systems for extreme dynamic loading scenarios, including blast and impact events. Such systems are supported by data-driven models that classify and predict their potential safety levels, thus providing proactive solutions under sudden threats. Chapter 2 focuses on developing a Bilayer Lightweight Arched Steel (BLASt) system, which is designed to protect structures against explosions with scaled distances up to 0.36 m/kg1/3. Unlike current conventional panels that rely on special materials (e.g., high-strength reinforced concrete), the BLASt system uses multiple lightweight concave and convex steel strips with inner air gaps. The analysis results showed that this system can effectively mitigate the pressure waves of severe explosions through the developed mechanisms, namely reflection, deviation, and splitting. Chapter 3 tackles the lack of existing lightweight protection systems that can withstand severe explosions up to scaled distances of 0.2 m/kg1/3. This is achieved by developing bio-inspired mitigation systems that mimic unique features and morphologies of living organisms such as the falcon’s nostril, the orca’s jaw, and the pangolin-armadillo scales. Such systems incorporate integrated mitigation mechanisms such as interlocking bearings, wave deviations and reflections, and controlled deformations, which result in effectively reducing the transmitted pressures and impulses behind the developed bio-inspired mitigation systems. One of the most dangerous terrorist threats is using high-impact velocity loadings such as projectiles and VBIEDs, where the latter, for example, can crash into security fences to weaken them before detonation. This situation highlights the need for protection systems that can maintain their structural integrity under impacts to resist subsequent explosions. Auxetic structures, with their negative Poisson's ratio, present a potential solution due to their unique densification behaviour under high-impact loads. As such, Chapter 4 introduces and experimentally validates the performance of an auxetic steel system, namely enhanced re-entrant with straight and curved members (ERSAC), under quasi-static loading to verify its developed design mechanism. The impact performance of the ERSAC system is then numerically evaluated under two impact loading scenarios of solid spheres and VBIDs with different velocities. The experimental and numerical results demonstrated that the system’s specific energy absorption is improved, relative to conventional re-entrant auxetics, due to the introduction of multiple sequential interlocking points within the densification zone, leading to higher mean crushing forces and lower peak crushing forces. Chapter 5 focuses on exploiting the double densification effects and the high energy absorption of the ERSAC system in resisting severe explosions up to scaled distances of 0.2 m/kg1/3, which was reported as a limitation of current conventional auxetics. The double densification effects are validated for single and multiple cells subjected to quasi-static loading, showing enhancements in the specific energy compared to conventional re-entrant and arrowhead auxetics. The blast response of the ERSAC system is then obtained for different scaled distances and compared to conventional auxetics. This is followed by interpretability analyses to quantify the effect of the ERSAC’s design parameters and geometrical configurations on mitigating blast loadings. Chapter 6 introduces a data-driven framework that employs machine learning models to classify and predict blast risks behind protection systems. To demonstrate its practical use, the developed framework is applied to the BLASt mitigation system through two main tasks: (1) classification of different protection levels through several explosion characteristics and mitigation system variables, where these protection levels are defined based on threshold pressure values; and (2) prediction of the generated pressure values in terms of explosion characteristics and mitigation system variables. This framework offers a promising approach to advancing the design and implementation of effective protection systems for critical infrastructures and vehicles under sudden-threatening risk scenarios.