Hybrid mathematical and informational modeling of beam-to-column connections

The analysis of steel and composite frames has traditionally been carried out by idealizing beam-to-column connections as either rigid or pinned. Although some advanced analysis methods have been proposed to account for semi-rigid connections, the performance of these methods strongly depends on the proper modeling of connection behavior. The primary challenge of modeling beam-to-column connections is their inelastic response and continuously varying stiffness, strength, and ductility. In this dissertation, two distinct approaches—mathematical models and informational models—are proposed to account for the complex hysteretic behavior of beam-to-column connections. The performance of the two approaches is examined and is then followed by a discussion of their merits and deficiencies. To capitalize on the merits of both mathematical and informational representations, a new approach, a hybrid modeling framework, is developed and demonstrated through modeling beam-to-column connections. Component-based modeling is a compromise spanning two extremes in the field of mathematical modeling: simplified global models and finite element models. In the component-based modeling of angle connections, the five critical components of excessive deformation are identified. Constitutive relationships of angles, column panel zones, and contact between angles and column flanges, are derived by using only material and geometric properties and theoretical mechanics considerations. Those of slip and bolt hole ovalization are simplified by empirically-suggested mathematical representation and expert opinions. A mathematical model is then assembled as a macro-element by combining rigid bars and springs that represent the constitutive relationship of components. Lastly, the moment-rotation curves of the mathematical models are compared with those of experimental tests. In the case of a top-and-seat angle connection with double web angles, a pinched hysteretic response is predicted quite well by complete mechanical models, which take advantage of only material and geometric properties. On the other hand, to exhibit the highly pinched behavior of a top-and-seat angle connection without web angles, a mathematical model requires components of slip and bolt hole ovalization, which are more amenable to informational modeling. An alternative method is informational modeling, which constitutes a fundamental shift from mathematical equations to data that contain the required information about underlying mechanics. The information is extracted from observed data and stored in neural networks. Two different training data sets, analytically-generated and experimental data, are tested to examine the performance of informational models. Both informational models show acceptable agreement with the moment-rotation curves of the experiments. Adding a degradation parameter improves the informational models when modeling highly pinched hysteretic behavior. However, informational models cannot represent the contribution of individual components and therefore do not provide an insight into the underlying mechanics of components. In this study, a new hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections. Mechanics-based components of deformation such as angles, flange-plates, and column panel zone, are idealized to a mathematical model by using a complete mechanical approach. Although the mathematical model represents envelope curves in terms of initial stiffness and yielding strength, it is not capable of capturing the pinching effects. Pinching is caused mainly by separation between angles and column flanges as well as slip between angles/flange-plates and beam flanges. These components of deformation are suitable for informational modeling. Finally, the moment-rotation curves of the hybrid models are validated with those of the experimental tests. The comparison shows that the hybrid models are capable of representing the highly pinched hysteretic behavior of beam-to-column connections. In addition, the developed hybrid model is successfully used to predict the behavior of a newly-designed connection.

Single particle motion of polymeric, colloidal, and biological materials

Small particles and their dynamics are of widespread interest due both to their unique properties and their ubiquity. Here, we investigate several classes of small particles: colloids, polymers, and liposomes. All these particles, due to their size on the order of microns, exhibit significant similarity in that they are large enough to be visualized in microscopes, but small enough to be significantly influenced by thermal (or Brownian) motion. Further, similar optical microscopy and experimental techniques are commonly employed to investigate all these particles. In this work, we develop single particle tracking techniques, which allow thorough characterization of individual particle dynamics, observing many behaviors which would be overlooked by methods which time or ensemble average. The various particle systems are also similar in that frequently, the signal-to-noise ratio represented a significant concern. In many cases, development of image analysis and particle tracking methods optimized to low signal-to-noise was critical to performing experimental observations. The simplest particles studied, in terms of their interaction potentials, were chemically homogeneous (though optically anisotropic) hard-sphere colloids. Using these spheres, we explored the comparatively underdeveloped conjunction of translation and rotation and particle hydrodynamics. Developing off this, the dynamics of clusters of spherical colloids were investigated, exploring how shape anisotropy influences the translation and rotation respectively. Transitioning away from uniform hard-sphere potentials, the interactions of amphiphilic colloidal particles were explored, observing the effects of hydrophilic and hydrophobic interactions upon pattern assembly and inter-particle dynamics. Interaction potentials were altered in a different fashion by working with suspensions of liposomes, which, while homogeneous, introduce the possibility of deformation. Even further degrees of freedom were introduced by observing the interaction of particles and then polymers within polymer suspensions or along lipid tubules. Throughout, while examination of the trajectories revealed that while by some measures, the averaged behaviors accorded with expectation, often closer examination made possible by single particle tracking revealed novel and unexpected phenomena.