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Abstract The objective of this paper is to advance the design of slender steel tubes by developing a practical approach for utilizing high-resolution measurements of geometric imperfections to estimate the buckling location and strength of such tubes in bending. This approach includes a novel measure of imperfection severity that is designed to be insensitive to noise. The ability of this measure to predict buckling behavior of slender tubes in bending is assessed through comparison with eight large-scale tests, and, for this set of data, the predictions are promising. This study is intended to be a starting point in the development of a simple, but accurate, design method to quantify the impact of imperfections on the buckling behavior of slender tubes.

A newly evolving concept in utility-scale floating wind turbines is investigated through design optimization. The ideal hull length is selected from seven new designs, based on optimizing the trade-off between minimizing structural weight and maximizing electricity harvest. The seven new spar-buoy designs have drafts ranging from 62 to 120 m. Each floater hull has been developed in conformance with industry-standard structural guide API Bulletin 2U. Performance is assessed with the Loose software using time-domain analysis and irregular winds and waves. The simulation method is based on multi-body theory in Euler space, which does not rely on small-angle assumptions. Nonlinear hydrostatic restoring forces and moments are derived for large rotational angles in the pitch-roll-yaw Euler angle sequence. Environmental forcing by wind is computed using aero-elastic theory. A new individual pitch control (IPC) method based on DISCON control routine (Jonkman) is investigated as a possible means to improve generating efficiency. The pitch angle of each blade is individually controlled relative to a mean angle computed by DISCON to maintain the ideal angle of attack by removing the angle change caused by inclining tower. A comparison of the generating efficiency between IPC and traditional collective pitch control shows that IPC is not an effective way to increase output power.

Nonlinear Energy Sinks (NES) are used to passively reduce the amplitude of vibrations. This reduction is made possible by introducing a nonlinearly stiffening behavior in the NES, which might lead to an irreversible transfer of energy between the main system (e.g., a building) and the NES. However, this irreversible transfer, and therefore the efficiency of the NES, is strongly dependent on the design parameters of the NES. In fact, the efficiency of the NES might be so sensitive to changes in design parameters and other factors (e.g., initial conditions) that it is discontinuous, switching from efficiency to inefficiency for a small perturbation of parameters. For this reason, this work introduces a novel technique for the optimization under uncertainty of NES. The approach is based on a support vector machine classifier, which is insensitive to discontinuities and allows one to efficiently propagate uncertainties. This enables one to efficiently solve an optimization under uncertainty problem. The various techniques presented in this paper are applied to an analytical NES example.

Abstract This paper introduces a framework for the assessment of damage of offshore wind turbines (OWTs) supported by jackets under extreme environmental loadings. Performance levels/damage states, ranging from operational/undamaged to near collapse/severely damaged, are defined based on static pushover analyses. An example performance assessment is presented for an OWT supported by a jacket based on environmental conditions for a site off Massachusetts along U.S. Atlantic coast. The environmental conditions are characterized based on two methods for estimating wind and wave conditions, one on extrapolation of NOAA buoy measurements and one on a stochastic hurricane catalog, and two models for extreme wave height, one on the crest height and one on the zero-up-crossing height. Using probabilistic models for demands and capacities, two curves of fragility, one estimating the initiation of yielding and the other estimating the onset of collapse, are developed to distinguish between the three damage states. The curves are applied to four combinations of two environmental hazard models and two extreme wave height models, and significant differences are found in the probability of damage among the four combinations of models. The findings have potential implications for the evaluation of the overall risk profile and associated performance for offshore wind farms.

Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Abstract This study aims to characterize the rate of change that takes place in the rheological properties of asphalt binders modified with numerous types and contents of nano-materials. The effects of nano-materials on the activation energy of modified asphalt binders under the viscous and visco-elastic behavior were also investigated. Through the research findings, the application of non-dimensional analyses using relative viscosity and relative G ∗ /sin δ (NSRP) are very meaningful to evaluate the rate of change in the rheological properties of asphalt binder per one unit percent of nano-material. It is also found that the non-dimensional viscosity index (∇ η ) and non-dimensional Superpave™ rutting factor gradient (∇NSRP) are influenced by the type and content of nano-material, and test temperature. The activation energy analysis has confirmed that the changes in amount of energy consumed are not only influenced by the type and content of nano-material, but also the physical phase of the modified asphalt binders.