Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.     

Seismic safety of lifelines—an optimum design method

Lifeline systems are vulnerable to two types of hazards arising from potential earthquake sources. These are the hazard of a fault-rupture strike on elements of a lifeline system and the hazard of overstress induced in different elements of the system because of the ground vibration. An optimum design method is presented in this paper for the design of a lifeline system for a maximum accepted probability of failure because of any of the two modes of failure. The method may be used to determine an optimum path between a number of fixed points which represent supply or demand stations in the system.     

Fuzzy reliability analysis of nanocomposite ZnO beams using hybrid analytical-intelligent method

A hybrid analytical-intelligent approach is proposed for fuzzy reliability analysis of the composite beams reinforced by zinc oxide (ZnO) nanoparticle. The fuzzy reliability index corresponding to buckling failure mode of nanocomposite beam under thickness-direction external voltage is computed based on three-levels: (1) fuzzy analysis, (2) reliability analysis and (3) analytical buckling analysis. In fuzzy analysis level, an improved gravitational search algorithm has been applied to determine uncertainty interval for membership levels of reliability index. The adaptive formulation with a dynamical self-adjusting process is used for reliability analysis level based on conjugate first-order reliability method (FORM). The self-adjusting term in conjugate sensitivity vector is used to satisfy the sufficient descent condition for controlling instability of FORM formula while the proposed conjugate scalar factor is computed less than the original conjugate FORM, thus it may be provided with the efficient results for the convex problem. The new and previous sensitivity vectors obtained by conjugate and steepest descent vectors dynamically adjusted the proposed conjugate factor. In the buckling analysis level, an exponential theory in conjunction with the method of energy is utilized. Fuzzy random variables including applied voltage, the volume fraction of ZnO, thickness of beam, spring constant and shear constant of the foundation are considered in studied nanocomposite beam. Survey results indicated that the proposed method can provide stable and acceptable fuzzy membership functions for parametric study. Moreover, the ratio of length to thickness and spring constant of foundation are the more sensitive parameters which affect fuzzy reliability index significantly.

     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

Linear principal transformation: toward locating features in N-dimensional image space

Projection Functions have been widely used for facial feature extraction and optical/handwritten character recognition due to their simplicity and efficiency. Because these transformations are not one-to-one, they may result in mapping distinct points into one point, and consequently losing detailed information. Here, we solve this problem by defining an N-dimensional space to represent a single image. Then, we propose a one-to-one transformation in this new image space. The proposed method, which we referred to as Linear Principal Transformation (LPT), utilizes Eigen analysis to extract the vector with the highest Eigenvalue. Afterwards, extrema in this vector were analyzed to extract the features of interest. In order to evaluate the proposed method, we performed two sets of experiments on facial feature extraction and optical character recognition in three different data sets. The results show that the proposed algorithm outperforms the observed algorithms in the paper and achieves accuracy from 1.4 % up to 14 %, while it has a comparable time complexity and efficiency.     

Use of surface modified recycled rubber particles for toughening of epoxy polymers

The objective of this research is to investigate the feasibility of using surface treated recycled rubber particles for toughening of epoxy polymers. These particles are obtained through grinding of scrap tires followed by oxidizing the surface of the particles in a reactive gas atmosphere. Surface treated recycled rubber particles with a nominal particle size of approximately 75 μm and a commonly used reactive liquid elastomer, CTBN, have been incorporated in a DGEBA epoxy resin. It has been shown that the recycled rubber particles are not as effective as CTBN in toughening of the epoxy matrix. However, blending of the two modifiers results in a synergistic toughening. Microscopy reveals that, when used alone, recycled rubber particles simply act as large stress concentrators and modestly contribute to toughening via crack deflection and microcracking. In the presence of micron size CTBN particles, which cavitate and induce massive shear yielding in the matrix, however, the recycled particles “stretch” the plastic deformation to distances far from the crack tip. This mechanism causes plastic zone branching and provides an unexpectedly high fracture toughness value. This study, therefore, provides a practical approach for manufacturing engineering polymer blends utilizing the surface modified recycled rubber particles.     

Characterization of minocycline loaded chitosan/polyethylene glycol/glycerol blend films as antibacterial wound dressings

Wound care has been a challenging subject for medical teams and researchers. Bacterial infections are one of the most serious complications in injured skins that often affect healing process. Antibacterial wound dressings can be used to facilitate wound healing process. The purpose of this study is to fabricate chitosan (Chito)/polyethylene glycol (PEG) antibacterial wound dressing doped with minocycline, and to evaluate the influence of composition ratio on the blending properties of the films. To improve the mechanical properties of these films, we examined various amounts of glycerol as a plasticizer. Moreover, we investigated morphological and mechanical aspects, water uptake, degradation, water vapor transmission and wettability properties of the films prepared with various ratios of Chito/PEG/Gly. Assessment of mechanical properties revealed that film containing 80:20 ratio Chito/PEG with 40 PHR Gly content exhibits the highest ultimate tensile strength and elongation at break (9.74 MPa and 45.73% respectively). Furthermore, results demonstrated that upon increasing PEG and Gly contents, degradability and hydrophilicity of the films increased whereas water uptake decreased. Water vapor transmission rate of the films was close to the range of 530–1200 g/m²d, indicating that the as formed films are possible candidates for dressing low exudate wounds or burns. Minocycline loaded films exhibited a biphasic drug release profile and it was more effective on gram-positive bacteria than on gram-negative bacteria. The polymeric film with the highest amount of loaded drug (2%) exhibited insignificant cytotoxicity (88%) against normal fibroblast cell line.     

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.