Journal of Numerical Methods in Civil Engineering
Volume:4 Issue: 1, Sep 2019

Cavitation is among the most complicated and common damages of spillway structures. This phenomenon is controlled by different parameters including the pressure, flow velocity, spillway surface material, operation time, and air flow content. The cavitation index is calculated along the spillway and compared with its critical value using the measured values of the flow’s hydraulic parameters. The high cost of experimental models for determining hydraulic parameters, the time required for developing experimental models, and the ever-increasing capabilities of computational fluid mechanics (CFD) models have led to the use of numerical simulation in the flow analyses. The present study employs ANSYS FLUENT to simulate the flow on the spillway of Aydoghmush Dam (Iran), calculate flow parameters, and determine the cavitation index at the flow rates of 35, 800, 1500, and 1850 m3/s. The standard k-ε equations were applied to model the turbulent flow, while the volume of fluid (VOF) method was employed to determine the flow’s free surface profile. The results showed acceptable consistency between the FLUENT and physical model results. It was also found that cavitation did not occur at any of the flow rates.

Limit-equilibrium method (LEM) and finite element method (FEM) with strength reduction method (SRM) techniques are the most widely used analysis tools in slope stability assessment. Recently, researchers have reported that both factor of safety (FOS) values and failure surfaces obtained from LEM and FEM are generally in good agreement, except in some particular cases. In this paper, the FOS and the location of critical failure surfaces procured by FE-SRM with various modeling types are compared. Eventually, the outcomes of FE-SRM with high mesh density are assumed as reference. The results of this study demonstrated that in FEM, determining the shear band zone by primary analysis of slopes with coarse meshes and consequently modifying the mesh configuration by imposing the program to locate the nodes in that zone can provide accurate and low-cost results. In addition, the comparison between the results of the proposed method and Bishop’s simplified approach as a limit equilibrium method (LEM) represented good agreement and it was evident that better results can be achieved with less cost and time.

In this paper, the effect of mathematical representation method of an MR damper on the performance of control algorithm is investigated. The most exact and common Maxwel Nonlinear Slider (MNS) and modified Bouc-Wen hysteretic models are employed through a nonlinear  comparatve numerical study. In many of semi-active control algorithms, a mathematical modelling method is required for determinig the Magneto-Rheological (MR) damper voltage at each time instant. Using different modelling methods can lead to different voltages for the MR damper, which subsequently results in changes to the responses of the controlled structure. A three story office building steel structure is excited by seven acceleration time histories. Nonlinear instantaneous optimal control (NIOC) and linear quadratic regulator (LQR) controllers are utilized as two active-based  semi-active algorithms. Results of nonlinear investigations show an obvious difference between the MNS and the modified Bouc-Wen models in the performance of control algorithms. Outputs show a higher performance for the modified Bouc-Wen model in reducing the hysteretic energy in the structure.

The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role in modeling and data mining of climatic phenomena as a result of their significant advantages, including their availability and analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms using remote sensing data is inevitable. In this study, 7 well-known machine learning algorithms are applied with different initial data to show that satellite images are able to estimate the combined indices more accurately. A new index (HT) is also defined by combining the quantities of relative humidity and temperature. Then, machine learning algorithms are trained for each of these three quantities. For each of the temperature and relative humidity quantities, four optimal bands were selected using the PCA method, then a combination of these optimal bands was determined for the HT index. Two criteria are used to validate the results Root Mean Square Error (RMSE) statistic and comparing the map of the interpolation method with the result of this study. RMSE values show that satellite imagery could have a high ability to model and estimate composite indices. Classification-KNN-Coarse and Ensemble-Bagged Trees with accuracy of 79.8626 % and 84.9281% are identified as the best machine learning methods for temperature and relative humidity, while the best accuracy to estimate the HT index is 92.8792% for Matern 5/2 GPR. Therefore, it can be said that by changing the methods of database preparation, the modeling results can be changed effectively in order to train models.

ASCE 7-16 has provided a comprehensive platform for the performance-based design of tall buildings. The core of the procedure is based on nonlinear response history analysis of the structure subjected to recorded or simulated ground motions. This study investigates consistency in the ASCE 7-16 requirements regarding the use of different types of ground motions. For this purpose performance of a benchmark tall building subjected to recorded and different types of spectrally matched ground motions is investigated. Application of ASCE 7-16 procedure, which is also adopted by the Los Angeles Tall Building Structural Design Council (LATBSDC) for amplitude scaling on tall buildings, results in unrealistically large scale factors. As expected, this large scale factor leads to a very conservative estimate of local and global demands by scaled recorded ground motions compared with spectrally matched ones. Recorded ground motions intrinsically cause large variation in engineering demand parameters (EDP), which is significantly magnified by large scale factors. The results are, a large ratio of maximum to mean response and control of the design process by maximum EDPs rather than mean values. Interestingly, capacities associated with maximum EDPs are vaguely defined in the code, partially due to the lack of knowledge on the elements actual response. It is also found that estimates of EDPs by different spectrally matched types of ground motions could be significantly different.

Although traditional signal-based structural health monitoring algorithms have been successfully employed for small structures, their application for large and complex bridges has been challenging due to non-stationary signal characteristics with a high level of noise. In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial Neural Network (ANN). First, three adaptive signal processing techniques including Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Hilbert Vibration Decomposition (HVD) are compared. The efficacy of these methods is examined for several numerically simulated signals to find a reliable signal processing tool. Then, three signal features are compared to find the most sensitive feature to damage. In the next step, an ANN ensemble is utilized as a classifier. Traditional statistical features and energy indices are used as the network input and output to make real-time detection of damage possible. The strength of this approach lies with training the network only based on healthy state of the structure. Having a trained ANN, online processing can be made to find a possible damage. Results show that the proposed algorithm has a good capacity as an online output-only damage detection method.