جستجوی مقالات مرتبط با کلیدواژه "settlement" در نشریات گروه "آب و خاک"

Pore water pressure, stress and settlement are the most important geotechnical parameters that must be constantly monitored during the construction of earth dams. Since measuring dam settlement directly at the time of dam construction requires cost and time, the development of artificial intelligence methods can be very effective. Most studies have been done in the field of modeling earth dams during construction with a numerical model; therefore the need for artificial intelligence modeling in this field seems to be necessary. Artificial intelligence models, including neural networks, are used for study and modeling many engineering sciences. Also, with the development of meta-heuristic algorithms, their combination with neural networks has become very wide-spreading due to more accurate results. So, The purpose of this study is to determine the most effective features in modeling settlement in the body of earthen dams at the time of construction as a case study (Kaboud-val dam) using the hybrid algorithm Dragonfly - artificial neural network in different points of the body of earthen dam at the time of construction. Therefore, in this research, new inputs in artificial intelligence modeling have been proposed for this purpose and their importance in different levels of installation has been investigated.

Kaboud-val Dam is located in Golestan province of Gorgan and around the city of Aliabad. This dam is homogeneous and has a filter and inclined drainage. In order to obtain the deformations of the body and the foundation of Kaboud-val dam, settlement plates have been installed in different sections of the body and its foundation during the construction. In this study, instrumental data related to the section of 19 Kaboud-val dam were used. Also, out of 17 pages, 4 pages named M1, M5, M9 and M13 (installed in the body and Kabudwal dam at levels 140, 152, 164 and 180 meter, respectively) have been used for modeling. By analyzing the data of section 19 pages, fill level (F), reservoir level (RL), dam construction time (T), fill rate (FR) and impounding rate (RV) for inlet and landing (P) on terms of (kp), was selected as the output of the hybrid model in the feature selection method. In this study, in order to select the best combination of input features in the artificial neural network, the dragonfly algorithm was used. Feature selection is a method of selecting a subset of related attributes (the best combination of them) that is relevant to a particular goal. The most important principle is to choose stable features and remove extra data. The combination of dragonfly algorithm with artificial neural network as DA-ANN is shown, Therefore, the dragonfly algorithm (DA) models a variety of different combinations of features with an artificial neural network and selects the best least error combination (RMSE) as the optimal artificial neural network model.

By performing a hybrid algorithm, sensitivity analysis and feature selection method, combining the four features on pages M1, M5,M9 and M13 with error values (RMSE) of 0.0023(kPa), 0.0024(kPa), 0.0026(kPa), respectively, and combining the three features on the M13 page with the value Error (RMSE) equal to 0.0035(kPa) was the best input combination. The three features of construction time, fill level and reservoir level as common features in all plates are the most effective features in modeling the settlement on selected plates. On plates mounted at higher levels, the modeling error increases, because during the test period and for plate M13 (with the highest mounting level), according to the statistical indices R^2, SI, NSE and NRMSE are equal to the values of 0.9998, 0.0062, 0.9998 and 0.0062 respectively, have poorer performance than other pages. The effect of reservoir level feature on the plates installed at higher levels due to the high sensitivity coefficient is more than other points and the fill level feature has the least effect on subsidence modeling.

The results are very important considering the cost of installing the measuring equipment and the significance of estimating the actual values in the future. The present study shows that the DA-ANN hybrid model is an important tool in predicting and selecting the best input combination for the intelligent model of the target variable of the settlement at the time of construction of earth dams. However, assessment of this model using the input data studied in different dams is necessary to ensure the application of these models in different conditions.

Generally, the safety of a dam is not only related to its design and construction, but also to the complete characterization of its performance during the first dewatering and operation period, as well as regular service during the life of the dam. In this study, the vertical and horizontal displacements of the Eyvashan Dam were analyzed using actual instrument results in an eight-year period in three steps of construction, first dewatering and operation period. The numerical model of the largest section of the dam was prepared by Geostudio and Plaxis software and examined with respect to the behavioral model of Mohr–Coulomb. According to the results of the instrumentation, the settlement of the dam core has increasing trend and sometimes after the construction, it reaches almost constant. The maximum settlement of the dam core at the end of construction phase was 809 mm which is equal to 1.2% of the dam height at the middle level. The horizontal displacements in the longitudinal direction of dam upstream are completely influenced by the reservoir water level and hydrostatic forces. The maximum horizontal displacement in the upper crust of Eyvashan dam is 75.6 mm. After reservoir filling, the amount of settlement from the lower levels to the higher levels (crest of the dam) have had an increasing trend. In order to adapt the observational and predictive data, multivariate regression and determination coefficient were used which showed a consistency result of about 85 percent between observation and prediction data. This indicates that the maximum settlements and their location are agreed well with the technical specifications, revealing the proper operation of the dam in terms of settlement.

Soil tilth is crucial to seedling emergence, plant growth, and crop yield. Soil tilth of unstable soil is very susceptible to change. Internal forces originating from matric suction can change soil physical properties. A laboratory study was conducted on pots of a surface silty clay loam soil of Khomeinishahr series (fine-loamy, mixed, thermic Typic Haplargids, USDA), located in Research Farm of Isfahan University of Technology. Soil surface subsidence, bulk density, cone index, and tensile strength were measured after first flood irrigation. Results showed that the seedbed (0-20 cm) with a bulk density of 1.2 Mg.m-3 will be changed to a massive soil with high values of bulk density, cone index, and tensile strength after soil wetting. Slaking, slumping and coalescence of the soil caused soil surface to subside about 1.5 cm in 20 cm soil layer. After irrigation, cone index and tensile strength increased abruptly with decreasing of moisture content. It is shown that the dominant source of strength (cone index and tensile strength) gain during drying is the effective stress due to matric suction. In the absence of external loads, physical state (tilth) of the soil returned back to the original state. Therefore, soil slaking and slumping and rearrangement of particles along with the internal forces are the factors leading to soil hardness.