The influence of surface ROUGHNESS magnitude and direction on dry static friction COEFFICIENT between two similar steel samples has been studied through an experiment. A testing apparatus has been designed and fabricated to measure the friction COEFFICIENT for a few forms of surface asperity. The average, maximum and minimum values of static friction COEFFICIENTs among steel samples have been measured and numerically proposed in terms of some discrete values of the surface ROUGHNESS, base and counter body angles. Correction COEFFICIENT defined as the ratio of maximum to minimum friction COEFFICIENT has been found on the result basis. According to the results of the experiments, dry static friction COEFFICIENT between two similar steel samples is affected by both magnitude and direction of surface ROUGHNESS. Under the condition of static equilibrium, the Colomb - Amonton formula has been used to determine friction COEFFICIENT between samples. The flexibility and mass of the string have been neglected, and the reservoir pendulum-like swinging and the water movement therein have been avoided within the experiments.

ROUGHNESS is one of the geometrical properties of rock joints that can be expressed through various methods. In this paper, eight different parameters were used to estimate the joint ROUGHNESS COEFFICIENT (JRC) of 112 joint ROUGHNESS profiles. The range of variation of these parameters in a given ROUGHNESS class is relatively large. These ROUGHNESS values overlap with their adjacent classes. In order to use two parameters simultaneously to estimate the JRC matrix, the interaction of these parameters on the JRC value were evaluated. The resolution of different ROUGHNESS classes in different scenarios was evaluated using Pearson correlation COEFFICIENT and using engineering judgment. So in this paper, a new method based on the classification of joint ROUGHNESS COEFFICIENT (JRC) by support vector machine (SVM) is purposed. So in this paper, a new method based on the classification of joint ROUGHNESS COEFFICIENT (JRC) by support vector machine (SVM) is purposed. Different joint ROUGHNESS parameters including Z2, RP, Grasselli2D, standard deviation of asperities height (SDH), standard deviation of profiles height variation (SDPHV), standard deviation of asperities angle (SDA), and geostatistical parameters including range (a), sill (C), CA and SRv were evaluated for 112 joint ROUGHNESS profiles. Using these 8 parameters, an 8 by 8 interaction matrix was created which consequently resulted in 28 individual two-dimensional JRC classification scenarios. A graph with SDH and SDA was selected for the Statistical classification of JRC (SCJRC) because of the relatively obvious boundary between JRC classes and easy calculation. Finally, data classification was performed by SVM. The estimation of SCJRC was checked by 20 experimental direct shear test data. A good agreement is observed between SCJRC and experimental results. The results illustrate that SCJRC is an appropriate method for the estimation of JRC.

The Manning ROUGHNESS COEFFICIENT is an important parameter in the design and evaluation of furrow irrigation. Despite its importance, estimation of this parameter for surface irrigation is very difficult, particularly in furrow irrigation. In this study, an EVALUE model was used to estimate the Manning ROUGHNESS COEFFICIENT in furrow irrigation. The model was based on volume balance and developed to estimate the Manning ROUGHNESS COEFFICIENT in surface irrigation. EVALUE also estimates both infiltration parameters of the modified Kostiakov branch function. The main input for EVALUE is depth of flow along the furrow at different times. Evaluation of this model was carried out by comparing simulated advance and resection phases using SIRMOD software based on the estimated parameters and the measured data. The estimated values of the Manning ROUGHNESS COEFFICIENT ranged from 0.02 to 0.102. This model was able to simulate the resection phase, which was more sensitive to the Manning ROUGHNESS COEFFICIENT, closely.

Vegetation ROUGHNESS's COEFFICIENTs are the main parameters used to determine river flow characteristics and are known to depend on the flow condition (depth and velocity) as well as vegetation condition (type and density). Flume experiments were conducted to investigate the vegetation density for submerged vegetation in river bed, banks, and flood plains. Artificial plastic plant (shrub type), were laid on the floor of a 14 meter long and variable slope flume facilitated in the Hydraulics Laboratory of the Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran for this study. The Manning's (n) values were estimated for different slopes, discharges, flow depth and vegetation densities. The results revealed that the Manning ROUGHNESS COEFFICIENT (n) increases as vegetation density increases, while it decreases when the flow depth and velocity increase. The results also showed that the ROUGHNESS COEFFICIENT is not constant and is a function of the flow velocity and depth of water. Significant variation of the Manning's (n) COEFFICIENT with flow and vegetation conditions urges the consideration of the flow and vegetation conditions in estimation of the Manning ROUGHNESS. Three equations are suggested for estimation of ROUGHNESS COEFFICIENTs for rivers and flood plains, with different states of vegetation density, flow depth and velocity.

Estimating the ROUGHNESS of erodible open channels plays an important role in their hydraulic design. This parameter is important for the development of numerical models and hydraulic design of these erodible channels. For this reason, several empirical methods have been presented so far to estimate the ROUGHNESS COEFFICIENT while the previous study shows that these methods are not sufficiently accurate. These methods usually are based on empirical activities which are too time-consuming and expensive. Therefore, in this paper, the so-called Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) methods as soft computing methods are used to estimate the ROUGHNESS COEFFICIENT in erodible open channels. To achieve this firstly, it is attempted effective parameter on the COEFFICIENT are extracted based on empirical methods and a dimensional analysis. Then effectiveness of the parameters on the COEFFICIENT is investigated via a sensitivity analysis versus the error of estimation. Following to the development of the models, they are implemented to estimate the COEFFICIENT. Based on the method none-dimensional water depth, Sheilds number, shear Reynolds number and none dimensional falling velocity are determined as input parameters of soft-computing models. Final results show that the employed methods are more accurate than empirical methods to estimate the parameter and these methods can be used as an alternative method for the estimation of parameters. In addition, the effectiveness of some other parameters such as shear Reynolds number and none dimensional water depth over the event are extracted by the sensitivity analysis.

Vegetation as one of the main effective factors in the flow resistance directly influence the flow velocity by increasing the surface ROUGHNESS. The calculation of flow velocity in hydraulic, hydrology science and all water projects, particularly river engineering, flood control, also for hydraulic and hydrologic modeling is necessary. One of the methods used for determing the velocity of flow is using the manning equation. In this research, some field experiments have been carried out to simulate runoff flow on two slops in order to determine the effects of vegetation on manning ROUGHNESS COEFFICIENT on the hillslope of Aghghala in Golestan province. These experiments have been done on two rectangular plots, with a width of 2 meters, length of 18 meters, on two different slops i.e.4.1, 12.9 percent. The runoff was generated by pumping water out of a reservoir. The experiments were done firstly with vegetation and then the vegetation was removed from the plots and the experiments were repeated. Totally, these experiments were repeated 20 times. The slope, vegetation percentage, flow discharge rate for the hillslope were measured. Then, in each repetition, the flow velocity was calculated and also the hydraulic radius in 6 sections were determined. The manning ROUGHNESS COEFFICIENT, with vegetation and without vegetation, determined using the manning equation for the two hillslopes was 0.0557, 0.0510 for low slope hill and 0.0652, 0.0564 for the high slope hill. After the calculation of manning COEFFICIENT of all repetitions, theT test results showed that the vegetation can significantly increase the ROUGHNESS COEFFICIENT. The structure of the field experiments as well as the fact that sheet flow runoff and unsubmerged vegetation being the focus of the study were new in this research and there is no enough knowledge on them.

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This study aimed to estimation of the Manning ROUGHNESS COEFFICIENT (n) in different phases and events of irrigation using empirical relations. For this purpose, six inflow rates in two flow categories, low and high, three consecutive irrigation events, advance and storage phases, two irrigation intervals and two types of soil texture were investigated. Next, the correlation between ROUGHNESS and these parameters was investigated using Pearson and Kendall statistical tests. Then, using its results, regression equations were developed to estimate Manning’s n in different irrigation phases. The results indicated that the advance time and the size of clods before irrigation had a high correlation and the slope, initial soil moisture and the size of clods after irrigation had a low correlation with the Manning’s n data in the whole irrigation event. The ROUGHNESS COEFFICIENT of the advance phase also had the highest correlation with the advance time. The highest and lowest correlation COEFFICIENTs between the parameters and ROUGHNESS COEFFICIENT of the storage phase were related to advance time and inflow rate with values of 0.65 and -0.31, respectively, which shows high correlation and direct relationship between advance time and ROUGHNESS and weak correlation and inverse relationship between flow rate and ROUGHNESS. The average values of R2, RMSE, and NRMSE indices in the provided relationships were 0.87, 0.014, and 26.97%, respectively, which indicated the appropriate accuracy of these relationships. Finally, it was suggested to conduct similar studies in different field and hydraulic conditions so that the presented relations are more comprehensive and can be recommended in other fields since the development of such relations can increase the speed of ROUGHNESS estimation in different phases and the ease of using it.

Flow resistance equations are a classical component of river hydraulic analysis, required for such applications as flood routing, prediction of flow depths and velocities in the design of floods conveyance structures, channel flood capacity estimation and the indirect estimation of flood discharges by the slope-area technique. In this research the effect of particle shape and bed slope of channel on Manning’s ROUGHNESS COEFFICIENT have been investigated experimentally. To achieve this aim, two types of gravels (natural and crushed shapes) with three average gravel sizes (3.8, 4.66 and 6.53 cm), four bed slopes (0.004, 0.006, 0.008 and 0.01) were used under different hydraulic conditions. The results showed that as the gravel size and bed slope increases or the relative submergence decreases, the Manning’s ROUGHNESS COEFFICIENT (n) increases.Moreover, the results revealed that the average value of Manning’s ROUGHNESS COEFFICIENT (n) of crushed gravels were by 2.7, 3.7, 3.8 and 5.9 percent more than natural gravels in the bed slopes of 0.004, 0.006, 0.008 and 0.01, respectively. Also, the difference between the values of Manning’s ROUGHNESS COEFFICIENTs (n) for crushed and natural particles increased by decreasing relative submergence and increasing bed slope. In other words, the effect of particle shape on Manning’s ROUGHNESS COEFFICIENT is applicable in steep slopes and low relative submergences (large-scale ROUGHNESS).

Manning ROUGHNESS COEFFICIENT is one of the most important parameters in designing water conveyance structures. Unsuitable selection of this COEFFICIENT brings up some mistakes. This research aims to present a method to determine the Manning ROUGHNESS COEFFICIENT based on a combination of optimization algorithm of simulated annealing (SA) with gradually varied flow equations. Therefore, in a lab rectangular flume of 12 m, 60 cm and 65 cm in length, width and height with fixed channel bed slope of 0.0002, nine series of water level profiles were carried out. Then, an objective function based on observed and calculated water level gradient was defined to decide on manning ROUGHNESS COEFFICIENT while it was minimized with simulated annealing optimization method. The values of objective function parameters were discussed by sensitivity analysis and the most optimal objective function was obtained. To measure the accuracy of COEFFICIENT obtained, Statistics indices of R2, Root mean square error (RMSE), Mean bias error (MBE), d were used. The results showed that manning ROUGHNESS COEFFICIENT has a great accuracy.