مقالات رزومه دکتر پیمان بیرانوند

In this research, an MLP neural network, which has widely been used in geotechnical engineering problems, is selected and trained by defining the stability factor of an earth dam. To training the network, we first specify the effect of parameters on the earth dam stability, including dam height (H), dam width (B), dam slope (θ), internal friction angle (φ), specific gravity of soil (γ) and cohesion of soil (C) by the Plaxis finite element program. Then, a database of 240 earth dam models is created and used to train the network. Subsequently, we train a single-layer and a two-layer MLP neural network with LM method and compare them. The results show that the single-layer network exhibits better performance in processing time and training quality. Then, the results are compared with the results of the ANFIS network and it is shown that the ANFIS network has a lower capability in defining earth dam stability factor than the MLP network.

Sometimes, suddenly or gradually structural or nonstructural damages are created in structure’s members due to natural or unnatural reasons like earthquake or corrosion. One of the methods used from the early in last decade to recognize damage is wavelet transform method. In this method, instead of examination of natural frequencies and the rate of its changes, vibration response or static response of a structure in various points of a structure is recorded at the same time, which in fact is a place-domain signal. In this paper, some factors have been investigated on a plate such as the effect of crack depth reduction, effect of changing support conditions, and the effect of approaching two rectangular cracks; and the responses obtained from finite element analysis, have been studied and the following results are gained: the ability of wavelet method to recognize the locality and severity of cracks, identification of tiny cracks up to 0.5mm depth and increasing the intensification of cracks’ effect on plate by reducing their distance from each other.

Concrete-filled steel tubular columns have been extensively used in structures, owing to that they utilize the most favorable properties of both constituent materials, ductility, large energy-absorption capacity, and good structural fire behavior. Concrete inside the steel tube enhances the stability of the steel tube, and the steel tube, in turn, provides effective lateral confinement to the concrete. Furthermore, the fire resistance of (CFT) columns is higher than that of hollow steel tubular columns, external protection being not needed in most cases. During a fire, the steel tube acts as a radiation shield to the concrete core and a steam layer in the steel- concrete boundary appears. This paper presents to investigate the effect of the parameters compressive on behavior of concrete-filled tubular (CFT) columns under fire by numerical simulations using ABAQUS software. Three different diameters to thickness ration of 54, 32 and 20 are considered in this study with two concrete's compressive strengths of 44 and 60 MPa. The measured compressive axial capacity is compared to their corresponding theoretical values predicted by four different international codes and standards. The results indicate that the effect of diameter to thickness ratio on the compressive behavior of the sections is greater than the effect of the other factors. Also, the axial capacity calculated by most of these codes reduces as the diameter to thickness ratio increases as verified by experimental results.

Predicting the river discharge is one of the important subjects in water resources engineering. This subject is of utmost importance in terms of planning, management, and policy of water resources with the aim of economic and environmental development, especially in a country like Iran with limited water resources. Awareness of the relation between rainfall and runoff of basins is an inseparable past of water design studies. Lack of sufficient data on rainfall-runoff due to the absence of appropriate hydrometric stations reveals the importance of using indirect methods and heuristic algorithms for estimating the basins' runoff more than before. In the present research, the genetic programming model has been employed to simulate the rainfall-runoff process of Khorramabad River basin, and in order to introduce the patterns and identify the best pattern dominating the nature of flow, all statistical data were divided into two groups of training and experiment (52 percent training and 48 percent experiment) and the program was implemented for 1000 replications using fitting functions and going through replication and developmental processes so as to find the optimal replication. Moreover, in order to evaluate the relations obtained from the simulator model, Root Mean Square Error (RMSE) and Mean Squared Error (MSE) indexes and Coefficient of Determination (R2) have been used. The investigations demonstrate that the employed equation 3 has the greatest relevance with the observational data. Therefore, it is recommended that the said equation be used for the rainfall-runoff studies of the abovementioned basin. Based on the results, the genetic programming model is an accurate direct method for predicting the discharge of Khorramabad River basin.