Robust optimization of structures subjected to stochastic earthquake with limited information on system parameter uncertainty

The optimization of structures subjected to stochastic earthquake and characterized by uncertain parameters is usually posed in the form of non-linear programming with stochastic performance measures where the uncertain parameters are modelled as random variables. Such an approach, however, cannot be adopted in many real life situations when the limited information about uncertainty can be only modelled as of the uncertain but bounded (UBB) type. A robust optimization strategy for stochastic dynamic systems characterized by UBB parameters is proposed in the present study in the framework of the response surface method (RSM). In evaluating the stochastic constraints, repeated computations of the dynamic responses are avoided by applying an adaptive RSM based on the moving least squares method. Numerical results are presented to highlight the effectiveness of the proposed procedure. The effect of parameter uncertainty is also studied by comparing the results obtained from the proposed optimization approach with the conventional stochastic optimization results.     

Hierarchically structured unit-simplex transformations for paralleldistributed optimization problems

A stable deterministic approach is presented for incorporating unit-simplex constraints based on a hierarchical deformable-template structure. This approach (i) guarantees strict confinement of the search to the unit-simplex constraint set without introducing unwanted constraints; (ii) leads to a hierarchical, rather than a global, network interconnection structure; (iii) allows multiresolution processing; and (iv) allows easy closed-form incorporation of certain other inherently global constraints, such as general recursive symmetries. Selected examples are presented which illustrate and demonstrate large-scale application of the template method.     

Hexafluoroacetone hydrate as a structure modifier in proteins: characterization of a molten globule state of hen egg-white lysozyme

A molten globule-like state of hen egg-white lysozyme has been characterized in 25% aqueous hexafluoroacetone hydrate (HFA) by CD, fluorescence, NMR, and H/D exchange experiments. The far UV CD spectra of lysozyme in 25% HFA supports retention of native-like secondary structure while the loss of near UV CD bands are indicative of the overall collapse of the tertiary structure. The intermediate state in 25% HFA exhibits an enhanced affinity towards the hydrophobic dye, ANS, and a native-like tryptophan fluorescence quenching. 1-D NMR spectra indicates loss of native-like tertiary fold as evident from the absence of ring current-shifted 1H resonances. CD, fluorescence, and NMR suggest that the transition from the native state to a molten globule state in 25% HFA is a cooperative process. A second structural transition from this compact molten globule-like state to an "open" helical state is observed at higher concentrations of HFA (> or = 50%). This transition is characterized by a dramatic loss of ANS binding with a concomitant increase in far UV CD bands. The thermal unfolding of the molten globule state in 25% HFA is sharply cooperative, indicating a predominant role of side-chain-side-chain interactions in the stability of the partially folded state. H/D exchange experiments yield higher protection factors for many of the backbone amide protons from the four alpha-helices along with the C-terminal 3(10) helix, whereas little or no protection is observed for most of the amide protons from the triple-stranded antiparallel beta-sheet domain. This equilibrium molten globule-like state of lysozyme in 25% HFA is remarkably similar to the molten globule state observed for alpha-lactalbumin and also with the molten globule state transiently observed in the kinetic refolding experiments of hen lysozyme. These results suggest that HFA may prove generally useful as a structure modifier in proteins.     

Flooding Probability-Based Optimal Design of Trapezoidal Open Channel Using Freeboard as a Design Variable

A flooding probability based cost effective design of open channel section has been proposed using freeboard as an additional design variable. The freeboard of the channel is calculated based on the flooding probability value. The proposed model is solved using classical optimization techniques as well as a nondominated sorting genetic algorithm. The results of the model are compared with an earlier reported model to demonstrate its superiority and field applicability.     

Optimal Design of Composite Channels Using Genetic Algorithm

In the past, studies involving optimal design of composite channels have employed Horton’s equivalent roughness coefficient, which uses a lumped approach in assuming constant velocity across a composite channel cross section. In this paper, a new nonlinear optimization program (NLOP) is proposed based on a distributed approach that is equivalent to Lotter’s observations, which allows spatial variations in velocity across a composite channel cross section. The proposed NLOP, which consists of an objective function of minimizing total construction cost per unit length of a channel, is solved using genetic algorithm (GA). Several scenarios are evaluated, including no restrictions, restricted top width, and restricted channel side slopes, to account for certain site conditions. In addition, the proposed NLOP is modified to include constraints on maximum permissible velocities corresponding to different lining materials of the composite channel cross section, probably for the first time. The proposed methodology is applied to trapezoidal and triangular channel cross sections but can be easily extended to other shapes or compound channels. Optimal design graphs are presented to determine the channel dimensions of a composite trapezoidal channel cross section. The results obtained in this study indicate that cost savings up to 35% can be achieved for the unconstrained velocity case and up to 55% for the limiting velocity case when the proposed NLOP is solved using GA as compared with the existing NLOP solved using either the classical optimization solution technique or GA.