Lifetime Analysis on Centrifugal ID Fan Foundation in Cement Plants

The dynamic behavior of centrifugal ID fan foundation of cement plants to perform the lifetime analysis was investigated in this study. To this end, a three-dimensional (3D) finite-element model (FEM) of this foundation was proposed and the analyses under static and dynamic loads for various modulus of elasticity of concrete (Ec= 20, 25, 28 and 30 GPa) and reinforcement (Es= 200, 250, 300 GPa) and rotor speeds of 400, 800, 1200, and 1800 rounds per minute (rpm) were conducted.The results show that the stress and deflection for a certain ID fan is around 2.5 and 10 times more under dynamic loading compared to static loading. Therefore, the excessive vibrations induced by increased speed of ID fan can result in reduced lifetime and fatigue of the foundation. Comparison of FEM results with laboratory-based evidence verified the accuracy of simulations made by the FEM. The proposed model can thus be applied to determine the critical points in foundation structure in order to obtain optimum results of design parameters

Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN

AbstractThe compressive strength values of cement mortar usually affect by sodium chloride quantities, chemical admixtures and cement grades so that an artificial neural network model was performed to predict the compressive strength of mortar value for different cement grades and sodium chloride (NaCl) percent. A three layer feed forward artificial neural network (ANN) model having four input neurons such as cement grades, various water to cement ratio, sodium chloride solution content, one output neuron and five hidden neurons was developed to predict of mortar each compressive strength.To this aim, twelve different mixes under three sodium chloride solution of 0%, 5% and 10% submerged after 60 days has been adopted to measure compressive strength.Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the sodium chloride percent of cement mortar and compressive strength can be established. Moreover modeling tools has great influence on the different cement grade such as 42.5 and 32.5 MPa

Foundation analyzing of centrifugal ID fans in cement pla

This research was based on a finite-element model (FEM) of large foundations such as induced draft (ID) fans. Three-dimensional (3D) linear analyses were performed under arbitrary static and dynamic loads for various modulus of elasticity of concrete (Ec) (20, 25, 28 and 30 GPa) and reinforcement (Es) (200, 250, 300 GPa). FEM results were compared with the existing ID fan foundations (laboratory-based evidence) to assess the accuracy of simulations made by the FEM. This study validated what constitutes a major departure from current thinking regarding material properties modeling of concrete under various loads to increase foundation for lifetime

An Improved Ant Colony Model for Cost Optimization of Composite Beams

This paper focused on the application and evaluation of an improved ant colony search method for the cost optimization of composite beams. The design was based on the American Institute of Steel Construction load and resistance factor design specifications and plastic design concepts. The objective function for composite beam was the cost function. This function included the cost of concrete, steel beam, and shear connectors. In order to validate the proposed model in optimizing composite beam design, two design examples taken from the literature were studied, and the results were compared to the original ant colony optimization and other meta-heuristic algorithms. The results showed that the improved ant colony method was able to find better solutions and had higher convergence speed than other meta-heuristic algorithms. Moreover, a parametric study was conducted to investigate the effects of beam spans and loadings on the cost optimization of composite beams

Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach

To control tunneling risk, the prediction of the surface settlement rate induced by shield tunneling using earth pressure balance plays a crucial role. To achieve this, ten independent variables were identified that can affect the amount of settlement. The nonlinear relationship between maximum ground surface settlements and ten influential independent variables was considered in artificial neural network (ANN) models. A total of 150 genuine datasets derived from the Southern Development Section of the Tehran Metro Line 6 project were used to train, validate, and test ANN techniques. Hence, the ground surface settlements of the mentioned project were predicted by the most accurate back propagation ANN technique. Ultimately, the importance level of different influential parameters on ground settlement at tunneling is relatively determined based on the results of the optimal neural network. The results used in this paper to evaluate the relative importance of each variable involved in the rate of ground surface settlement demonstrate that the parameters of grout injection and permeability equivalent to the proportions of approximately 16.91% and 5.07% have the highest and lowest impact, successively