Summary Structural health monitoring via quantities that can reflect behaviors of concrete dams, like horizontal and vertical displacements, rotations, stresses and strains, seepage, and so forth, is an important method to evaluate operational states of concrete dams correctly and predict the future structural behaviors accurately. Traditionally, statistical model is widely applied in practical engineering for structural health monitoring. In this paper, an extreme learning machine (ELM)‐based health monitoring model is proposed for displacement prediction of gravity dams. ELM is one type of https://en.wikipedia.org/wiki/Feedforward_neural_networks with a single layer of hidden nodes, where the weights connecting inputs to hidden nodes are randomly assigned. The model can produce good generalization performance and learns faster than networks trained using the back propagation algorithm. The advantages such as easy operating, high prediction accuracy, and fast training speed of the ELM health monitoring model are verified by monitoring data of a real concrete dam. Results are also compared with that of the back propagation neural networks, multiple linear regression, and stepwise regression models for dam health monitoring.

Nasal obstruction frequently has been associated with obstructive sleep apnea (OSA). Although correction of an obstructed nasal airway is considered an important component in OSA treatment, the effect of nasal surgery on OSA remains controversial. Variation in airway anatomy between before and after nasal surgery may cause significant differences in airflow patterns within the upper airway. In this paper, anatomically accurate models of the interaction between upper airway and soft palate were developed from prenasal and post-nasal surgery multidetector computed tomography data of a patient with OSA and nasal obstruction. Computational modeling for inspiration and expiration was performed by using fluid-structure interaction method. The airflow characteristics such as velocity, turbulence intensity and pressure drop, and displacement distribution of soft palate are selected for comparison. Airway resistances significantly decrease after the nasal surgery, especially in the velopharynx region because of an enlarged pharyngeal cavity and a reduced upstream resistance. Meanwhile, the decreased aerodynamic force would result in a smaller displacement of soft palates, which would lead to slight impact of the soft palate motion on the airflow characteristics. The present results suggest that airflow distribution in the whole upper airway and soft palate motions have improved following nasal surgery.

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<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">Recruitment prediction is a key element for management decisions in many fisheries. A new approach using neural network is developed as a tool to produce a formula for forecasting fish stock recruitment. In order to deal with the local minimum problem in training neural network with back-propagation algorithm and to enhance forecasting precision, neural network’s weights are adjusted by optimization algorithm. It is demonstrated that a well trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the prediction of fish stock recruitment.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>

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The relationship between thermal conductivity of porous material and fractal dimension is numerically simulated by using the finite element method. The solid matrix and pore space are generated randomly according to material porosity. Material parameters and element properties are changed by using ANSYS parameter design language. The effective thermal conductivity is derived according to thermal flux through some sections computed by FEM and Fourier heat transform law. The investigation shows that the effective thermal conductivity decreases with increasing porosity. The effective thermal conductivity will decrease exponentially with increasing fractal dimension of porosity space and increase exponentially with increasing fractal dimension of solid matrix.

Modeling flow and solute transport in the unsaturated zone requires specifying values for soil hydraulic conductivity. Fractal models describe hierarchical systems and are suitable to model soil structure and soil hydraulic properties. The objectives of the paper are to propose a parameter identification approach and to determine hydraulic parameters using inverse modeling. The inverse problem of parameter identification is formulated as an optimization problem. In order to identify hydraulic parameters of soils efficiently and in a robust manner, the genetic algorithm is presented for the parameter identification of the fractal models. The investigation shows that the proposed inversion procedure is more superior for applying to parameter identification of fractal model of mass transport for unsaturated soils.

A device was developed to determine the viscoplastic parameters of conditioned soil, which works on the principle of torsional shear applied to a standard vane with controlled strain rate. Rheological measurements were taken for three sand samples with the same bentonite slurry injection ratio (14.3%), but with different foam injection ratios (19%, 23%, and 27%), by changing the gasbag pressure levels (i.e., 0, 100, 200, 300, and 400 kPa) and vane speeds (i.e., 1/3.4, 1/4.1, 1/5, 1/6.4, 1/9, 1/15, and 1/30 RPM) to determine the effects of confining pressure and foam injection ratio on the viscoplastic parameters. The results show that the values of viscosity coefficient and yield stress for conditioned sands are in the range of 9.1-53.5 kPa·s and 1.5-10.2 kPa, respectively. The viscoplastic parameters increase with the increase of the confining pressure, and decrease with the increase of the foam injection ratio for all tested samples.