نشریه مهندسی عمران و محیط زیست دانشگاه تبریز
سال سی و نهم شماره 2 (پیاپی 58، تابستان 1388)

Tuned liquid damper (TLD) is one of the passive control methods in structures which have been used since mid 1980s for the control of structural vibrations in civil engineering. TLD system consists of one or more tanks containing a liquid that mainly water. These tanks are installed on top of the structure and water sloshing in these containers is used to reduce the vibrations of the structure. In this paper, manufacturing method of the shaking table containing TLD system, and experimental test results under various excitation frequencies is discussed. The results of this investigation reveals that when the frequency ratio(ratio of induction frequency to natural frequency of the tank) is close to 1, TLD system can have the best performance in damping the dynamic input energy and increasing damping of the structure. Also, the results in these series of experiments are consistent and in close agreement with Housner linear system behavior theory.

In this paper, hydrodynamic pressure due to dam-reservoir interaction for an incompressible, irrotational and inviscid fluid with unbounded domain, is analyzed by using finite element approach. In the derivation of boundary condition, it is assumed that dam is vibrating in the normal direction of dam-reservoir interface and this interface is vertical. Moreover, bottom of reservoir is assumed to be rigid and horizontal. The governing equation with related boundary conditions is implemented in the finite element code. The weighted residual standard Galerkin method with 8-node elements is used for developing finite element model. Both sommerfeld boundary condition and perfect damping boundary condition are developed for truncating surface of unboundedfluid domain and the results of two cases are compared with analytical results.

One of the main problems in valuation of the operation of floating breakwaters is estimation of thetransmission and reflection coefficients and because of absence the universal theoretical principles in order to obtain these coefficients in any region, investigations have been focused on physical models. In this research program, A.N.Ns (Artificial Neural Networks) has been used as a computational tool in order to estimate the transmission and reflection coefficients of the P.F.Bs (pontoon floating breakwaters). The mathematical A.N.N model used in this study, is a MLP (Multi Layer Perception) kind and BP (Back Propagation) algorithm has been used for learning the network. The data from physical model studies of P.F.B are partly used for learning the network and the remained are used to verify the learned network. The different parameters of neural networksincluding the learning rule, transfer function, number of hidden layers and number of nodes in each layer have been investigated and finally, the optimum network, have been determined.

Steel plate shear walls have been noticed in recently three decades to considering lateral loads of earthquake and wind, in high rise buildings behavior of this system resembles a steel plate girder used in a vertical cantilever application. The columns fulfill the function of the flanges of the vertical girder, while the horizontal floor beams act as web stiffeners. Before acceptance of the idea to utilizing post-buckling strength of infill steel plate, stiffened walls with heavy stiffeners were used to preventing infill steel plate buckling but after acceptance of this idea, unstiffened walls replaced with stiffened walls. However in seismic rehabilitation of existing buildings and retrofitting the structures, which are more in attention recently or in place which wall has an opening using stiffeners has a high importance. In this research influence of X, K, V, inverse V and V-H shapes of the stiffeners on steel plate shear walls have been studied. Results of finite element analysis show that X shape stiffener in compare with other diagonal stiffeners including K, inverse V and V shape especially V-H shape stiffener, have more effective influence in increasing yield and ultimate loads and energy absorption capacity of panel. Also unlike plate girders which for doing economic design stiffener is used in steel plat shear wall systems, it is better using unstiffened panel.

Nowadays the drinking water industry has an increased focus on the effectiveness of drinking water treatment plants in removing waterborne pathogens such as Giardia and Cryptosporidium. For water quality assessment, use of methods to control size and numbers of particles in filtration effluent will be necessary. Because of high resistance of these kinds of microorganisms against disinfection and also insignificant impacts them on turbidity of effluents of treatment plants. In this study the effects of some operational parameters on direct filtration effectiveness, like numbers and size of particles in treated water, have been examined. Tests were performed by utilizing a pilot in laboratory scale, that consists of: raw water supply unit, coagulation, flocculation units and a 3layers filtration unit. The results show that the particle counting makes comprehensive monitoring of filtrationunit possible and acts more sensitive to present variations of turbidity.

In this study, Artificial Neural Network (ANN) as a black box model was used in order to evaluate the temperature and water discharge effects on suspended sediment load of Ligvanchay River. For this purpose, the hydrological data such as mean river flow discharge, mean daily temperature and mean daily suspended sediment load of Ligvanchay at the outlet hydromerey station were collected and divided to seven categories. These data were then entered to the ANN and the model with Levenberg-Marquardt (LM) of back propagation training algorithm was executed about 2000 times to determine the optimum structure of ANN model includes numbers of hidden layers and neurons in each layer. After determine the optimum structure, other back propagation training algorithms were tested for doing more comparison among the algorithms; in this manner the results of training and verifying as graphs and tables were presented. Furthermore, the results of the ANN model were compared with the results of some other classical black box models such as multi and single linear regression, auto regressive (AR) time series models. The obtained results of the study showed better performanceof the ANN model in comparison of the others when only one neuron was considered as model input. However when more than one neuron was used as input data, MLR and AR (2) showed a hit better results. This may be because of noise propagation in ANN non-linear model in comparison with other linear models. According the results, it can be obviously seen that the suspended sediment load of Ligvanchay has only a little sensitivity to the environment temperature.