y. abdi

Khorramabad-Zal Highway is a part of Tehran-Bander Imam Khomeini axis, which is one of the most important roads in the country economically. Therefore, this research has investigated the landslide hazard zonation in Khorram Abad-Zal Highway using geographic information system (GIS). In order to landslide hazard zonation in the present study, the hierarchical analysis method (AHP) was used. Accordingly, according to field studies and observations and geological conditions, the number of 10 factors influencing the occurrence of landslides including lithology, slope, aspect, distance from the fault, land use, climate, altitude, distance from the river, The distance from the stream, and the rainfall are selected and in the GIS environment using the AHP method, after weighting, a map for each parameter has been prepared. Finally, by combining different layers, a landslide hazard zoning map has been prepared for the study area. Based on the prepared map, it was found that 54% of the studied area has a high and very high landslide potential.

The accurate determine of uniaxial compressive strength (UCS) and modulus of elasticity (E) using laboratory methods requires substantial time and cost. To overcome these difficult, development of predictive empirical equations to estimate the UCS and E of rocks is very important in rock engineering. This study deals with the prediction of UCS and E of sandstones from petrographic characteristics using multivariable linear regression analysis (MLR). For this purpose, 20 rock blocks were collected from sandstones in different locations of Upper Red Formation in southwestern Qom. Samples were subjected to petrographic examination, which included the observation of 16 parameters and modal analysis. The specimens were also tested to determine the uniaxial compressive strength, modulus of elasticity, porosity, and dry density. Based on the results of statistical analysis, multiple predictive equations were developed to estimate the mechanical properties using petrographic characteristics. The performance of developed equations was assessed using R, RMSE and VAF. Based on the results, it was observed that the proposed equations have a good performance in predicting the UCS and E.

Depth control autonomous underwater vehicle due to disturbances, noise and non-linear dynamics is very important and difficult in the field of systems control and for the solving it in methods that are robust to the uncertainty of the system is needed. To deal with such uncertainties and disturbances, sliding mode control has been used. Generally, The main advantage of this method is robust to uncertainties and disturbances, But due to the computational delays and restrictions on operators immediately change is not possible and leads to the phenomenon of chattering in control signal and excitation high-frequency dynamics. In this paper, depth control of an autonomous underwater vehicle is considered. To solve the problem chattering in the system under study, We tried to be used the combination of sliding mode control and artificial neural network. The use of the artificial neural network has led to the estimation of the uncertainties of the system and the chattering domain in the control signal is effectively reduced. Simulation show that the sliding mode method problems in depth control of underwater vehicle using artificial neural network capabilities has been fixed and the system properly controlled.