Performance-based optimization of structures: theory and applications

Performance-based Optimization of Structures introduces a method to bridge the gap between optimization theory and its practical applications to structural engineering. The performance-based optimization (PBO) method combines modern structural optimization theory with performance-based design concepts to produce a powerful technique for use in structural design. This book provides the latest PBO techniques for achieving optimal topologies and shapes of continuum structures with stress, displacement and mean compliance constraints. The emphasis is strongly placed on practical applications of automated PBO techniques to the strut-and-tie modeling of structural concrete, which includes reinforced and prestressed concrete structures. Basic concepts underlying the development of strut-and-tie models, design optimization procedure, and detailing of structural concrete are described in detail. The design optimization of lateral load resisting systems for multi-story steel and steel-concrete composite buildings is also presented. Numerous practical design examples are given which illustrate the nature of the load transfer mechanisms of structures

Structural design optimization

Guest Editorial, Special Issue on Structural Design Optimization, Advances in Structural Engineering, An International Journal, 2007, Vol. 10, No.6

A channel Brownian pump powered by an unbiased external force

A Brownian pump of particles in an asymmetric finite tube is investigated in the presence of an unbiased external force. The pumping system is bounded by two particle reservoirs. It is found that the particles can be pumped through the tube from a reservoir at low concentration to one at the same or higher concentration. There exists an optimized value of temperature (or the amplitude of the external force) at which the pumping capacity takes its maximum value. The pumping capacity decreases with increasing the radius at the bottleneck of the tube.Comment: 14 pages, 9 figure

Complex quantum network model of energy transfer in photosynthetic complexes

The quantum network model with real variables is usually used to describe the excitation energy transfer (EET) in the Fenna-Matthews-Olson(FMO) complexes. In this paper we add the quantum phase factors to the hopping terms and find that the quantum phase factors play an important role in the EET. The quantum phase factors allow us to consider the space structure of the pigments. It is found that phase coherence within the complexes would allow quantum interference to affect the dynamics of the EET. There exist some optimal phase regions where the transfer efficiency takes its maxima, which indicates that when the pigments are optimally spaced, the exciton can pass through the FMO with perfect efficiency. Moreover, the optimal phase regions almost do not change with the environments. In addition, we find that the phase factors are useful in the EET just in the case of multiple-pathway. Therefore, we demonstrate that, the quantum phases may bring the other two factors, the optimal space of the pigments and multiple-pathway, together to contribute the EET in photosynthetic complexes with perfect efficiency.Comment: 8 pages, 9 figure

Constrain intergalactic medium from the SZ effect map

In this paper, we try to detect the SZ effect in the 2MASS DWT clusters and less bound objects in order to constrain the warm-hot intergalactic medium distribution on large scales by cross-correlation analysis. The results of both observed WMAP and mock SZ effect map indicate that the hot gas distributes from inside as well as outside of the high density regions of galaxy clusters, which is consistent with the results of both observation and hydro simulation. Therefore, the DWT measurement of the cross-correlation would be a powerful tool to probe the missing of baryons in the Universe.Comment: 9 pages,2 figures. Accepted for publication in Mod. Phys. Lett.

Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure