Multi-agent decision-making dynamics inspired by honeybees

When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological models of decision-making. Using tools of nonlinear dynamics we show how the designed agent-based dynamics recover the high performing value-sensitive decision-making of the honeybees and rigorously connect investigation of mechanisms of animal group decision-making to systematic, bio-inspired control of multi-agent network systems. We further present a distributed adaptive bifurcation control law and prove how it enhances the network decision-making performance beyond that observed in swarms

Pressure-impulse diagram method:a fundamental review

Accidental and deliberate explosions stemming from catastrophic events in the petroleum industry, incidents during complex manufacturing processes, mishandling or failure of domestic gas appliances or installations, terrorist attacks and military engagements, are becoming increasingly relevant in structural design. Pressure‐impulse (P‐I) diagrams are widely used for the preliminarily assessment and design of structures subjected to such extreme loading conditions. A typical P‐I diagram provides information concerning the level of damage sustained by a specific structural member when subjected to a blast load. This paper presents a state‐of‐the‐art review describing the development of the P‐I diagram method over the last 70 years, the main assumptions upon which its development is based and the framework through which such the method is applied in practice. The structural analysis methods used for the derivation of P‐I curves are discussed and the existing approaches are categorised according to algorithms used. A review of the P‐I curve formulae proposed to date is performed, where the formulae are classified according to the formulation methods