Shahid Chamran University of AhvazJournal of Hydraulic Structures2345-413X7320211101Estimation of the Local Scour from a Cylindrical Bridge Pier Using a Compilation Wavelet Model and Artificial Neural Network1221713710.22055/jhs.2021.38300.1187ENMehranSeifollahiFaculty of Civil Engineering, University of Tabriz, Tabriz, Iran.0000-0002-8842-7118Mohammad AliLotfollahi-YaghinFaculty of Civil Engineering, University of Tabriz, Tabriz, Iran.0000-0003-2628-4083FarhoudKalatehFaculty of Civil Engineering, University of Tabriz, Tabriz, Iran.0000-0001-5192-9408RasoulDaneshfarazDepartment of Civil Engineering, University of Maragheh, Maragheh, Iran.0000-0003-1012-8342SalimAbbasiDepartment of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.0000-0001-7571-2780JohnAbrahamSchool of Engineering, University of St. Thomas, St Paul, MN, USA.0000-0002-3818-8681Journal Article20210903In the present study, an artificial neural network and its combination with wavelet theory are used as the computational tool to predict the depth of local scouring from the bridge pier. The five variables measured are the pier diameter of the bridge, the critical and the average velocities, the average diameter of the bed aggregates, and the flow depth. In this study, the neural wavelet method is used as a preprocessor. The data was passed through the wavelet filter and then passed to the artificial neural network. Among the various wavelet functions used for preprocessing, the dmey function results in the highest correlation coefficient and the lowest RMSE and is more efficient than other functions. In the wavelet-neural network compilation method, the neural network activator function was replaced by different wavelet functions. The results show that the neural network method with the Polywog4 wavelet activator function with a correlation coefficient of 87% is an improvement of 8.75% compared to the normal neural network model. By performing data filtering by wavelet and using the resulting coefficients in the neural network, the resulting correlation coefficient is 82%, only a 2.5% improvement compared to the normal neural network. By analyzing the results obtained from neural network methods, the wavelet-neural network predicted errors compared to experimental observations were 8.26, 1.56, and 1.24%, respectively. According to the evaluation criteria, combination of the best effective hydraulic parameters, the combination of wavelet function and neural network, and the number of neural network neurons achieved the best results.https://jhs.scu.ac.ir/article_17137_706f6ef99e08815ec4f180162f1ba8d6.pdfShahid Chamran University of AhvazJournal of Hydraulic Structures2345-413X7320211101Examination the effect of soil parameters on earth dam slope stability in ABAQUS software23321714310.22055/jhs.2021.37701.1178ENAhmadrezaMazaheriFaculty of Engineering, Ayatollah Borujerdi University, Borujerd, Iran.MasoudPaknahadFaculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran.RasoulAlipourFaculty of Engineering, Shahrekord University, Shahrekord, Iran.0000-0001-9425-1369Journal Article20210612The stability of soil slopes and the determination of safety factors have always been the subject of study for engineers and researchers. The safety factor of slopes can be determined by using the methods of limit equilibrium method (L.E.M.), limit analysis, and strength reduction method (S.R.M.). The equilibrium method determines the slope safety factor based on the equilibrium of the inter-slice force and without the analysis of tension and strain. In the strength reduction method, based on the tension-strain analysis, the strength of various points of the slope is reduced until it reaches the critical state, and by connecting all of the critical points, the critical rupture level will be obtained. Finite element software and finite difference software determine the safety factors in soil slopes by using the concepts of the strength reduction method. In this paper, the safety factors of soil slopes are determined by using ABAQUS software, and using the concept of strength reduction method. There is no option in ABAQUS for the determination of safety factors and it should be obtained by defining the concepts of strength reduction. The purpose of this study is to implement a strength reduction method in a finite element program to calculate the safety factor of slopes. The results of this research indicate that the changes in friction angle affect the safety factor changes more than variations in cohesion. Also, slope angle and its changes affect the safety factor changes more than other factors.https://jhs.scu.ac.ir/article_17143_4a26d22d3051a6724c5cb83c9fc1e509.pdfShahid Chamran University of AhvazJournal of Hydraulic Structures2345-413X7320211101Introduce an approach to computing mean velocity and discharge using entropy velocity concept and a data-driven technique and only one single measured value of mean velocity33411714810.22055/jhs.2021.38330.1188ENAmir FarbodAbdolvandiDepartment of Water Science and Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.Ali NaghiZiaeiDepartment of Water Science and Engineering, College of Agriculture, Ferdowsi University of Mashhad,
Mashhad, Iran.Journal Article20210823Discharges in hydrometric stations are estimated by converting the stage values to the discharge using a stage-discharge relationship or by multiplying mean velocity with ﬂow cross-sectional area. Estimation of mean velocity in hydrometric stations, especially during flood events, is not easily possible. Therefore, the method of estimating mean velocity by converting the maximum velocity to mean velocity using a conversion factor is a desirable method. The velocity convert factor estimation in stations without enough valuable measured discharge data is a challenging issue. Present study develops a method for determining the conversion factor by combining the entropy velocity profile and a data-driven technique (genetic programming) by knowing only one mean velocity value, and thus develops a method to determine discharge at the weak gauging sites. The advantage of the method introduced in this study is the simplicity of application and the use of parameters that can be easily measured to estimate the mean velocity values. The performance of the method was evaluated by comparing the computed and the observed mean velocity values and the Root Mean Square Error and the Mean Absolute Error were found to be 0.05 m s-1 and 0.04 m s-1, respectively. The results showed that the introduced method estimates a suitable conversion factor compared to similar methods and is applicable for stations without measurement.