Drought and Immethodical ground water withdrawal in recent years has caused numerous problems such as subsidence due to falling of subsurface water table, the reduction of water quality, etc. in cities across the world. This research as a case study deals with harmful effects of subsurface water withdrawal in the city of Kerman and practical monitoring of the subsidence and makes prediction of land subsidence. The artificial neural network has been used for modeling the monitored results and prediction of future subsidence. A surveying network with more than 500 installed benchmarks in an area of 334 square kilometer has been used to measure the subsidence of the city area. Benchmarks were installed in the beginning of 2004 and were monitored at the end of 2004, 2006, and 2007. For modeling, extra data were obtained from Iranian Surveying Organization for the years before 2004. The resulting model showed that, the subsidence varies between zero and 15cm per year in different parts of the City, which depends on the subsurface-layered soils, their compressibility, and the manner of subsurface water withdrawal
In the present study a Genetic Programing model (GP) proposed for the prediction of relative crest settlement of concrete faced rock fill dams. To this end information of 30 large dams constructed in seven countries across the world is gathered with their reported settlements. The results showed that the GP model is able to estimate the dam settlement properly based on four properties, void ratio of dam's body (e), height (H), vertical deformation modulus (E v ) and shape factor (Sc) of the dam. For verification of the model applicability, obtained results compared with other research methods such as Clements' formula and the finite element model. The comparison showed that in all cases the GP model led to be more accurate than those of performed in literature. Also a proper compatibility between the GP model and the finite element model was perceived.
A new combined method for the inverse modelling of leakage from the body and foundation of earth dams considering a transient flow model is introduced in this paper. Reaching a unique result, an objective function that simultaneously employs the time series of hydraulic heads and observations of flow rates has been defined. An inclusive finite-element model that considers all the construction stages of an earth dam has been created and then orthogonal design, back-propagation artificial neural networks and a genetic algorithm have been used to do inverse modelling. The proposed method has been employed for the inverse modelling of leakage in Baft dam in Kerman, Iran. Hydraulic conductivities of different parts of the dam have been investigated for two distinct predefined cases, and in both cases satisfactory results have been obtained. The fitting results show the applicability of the suggested method in inverse modelling of real large-scale problems, which not only decreases the computation cost but also increases reliability and efficiency in such problems.
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