mohammad ali arjomand

This article introduces and uses screw piles in the road construction industry. Considering the continuous expansion of the country's need for various restraints and piles in the construction industry and other industries such as road construction, telecommunications, etc., the use of advanced and modern technologies in this field are very important. It is important to note that, according to studies, a large percentage of the operating costs are spent on the construction of the foundation on loose and dense land. Therefore, the use of foundations that have a higher construction speed, are also economically viable and for different types of soil groups, environmental compatibility, no excessive change of soil layers, easy implementation conditions and no need for specialized personnel, Suitable, recommended.Due to the simple technology and fast implementation of this type of pile in all climatic and regional conditions and also the lower implementation cost compared to other similar methods, the use of this type of pile in the soil of the road construction bed can be very useful.

The aim of this research is to investigate the possibility of repairing and stabilizing clay soils through the use of natural additives. The type of purpose is practical and the method of laboratory performance. Three types of additives including rice husk ash, nanokitin and lime with different weight percentages were added to the primary clay and examined separately. Axial, compressive and adhesion tests were performed and the final results were performed with peripheral scanning electron microscope and X-ray spectroscopy. The results showed that adding different percentages of nanocytin and rice husk ash resulted in much lower reductions in the dough index and improved the dough index and dough characteristics of the problem soils. Also, all the different compounds and different weight percentages of the three substances lime, nanocitin and rice husk ash, have led to a favorable increase in compressive strength of the sample of the first tested soil. When 6% of lime is added to the initial soil sample, the compressive strength increases optimally and effectively, and compared to the results of the initial soil resistance, the increases are 72, 118, 132 and 154%, respectively, during the operation. 7, 28, 42 and 90 day yields were obtained in the resistance of the samples and this increase was desirable and significant. In all specimens, in all periods of processing, adhesion to the primordial soil sample has been on the rise.

This paper presents the effect of geogrid tensile strength by calculating pullout resistance and geogrid-soil interaction mechanism. In order to investigate this interface, a series of pullout tests have been conducted by large scale reformed direct shear test apparatus in both cohesive and granular soils. In numerical, finite difference software FLAC3D has been carried out on experimental tests and the results are compared with findings from laboratory tests and to complete investigation results. The results show tensile strength of geogrids has an important role in interface behavior. Effect of soil type also is discussed. The obtained results show that geogrids with low tensile strength have higher pullout resistance in low normal stress on the surface, this effect reversed as the normal applied stress is increased. Numerical analysis only estimates the pullout strength with good agreement in high normal stress. Obviously, in high normal stresses soils, compacted and passive resistance in front of transverse elements increases. Results also indicate that the failure mechanism of gear pullout is same as foundation bearing capacity, in clays punching shear failure occur on geogrid-soil interaction resistance and sands show hardening behavior. Furthermore, the effective particle size of soil is found to be a little more than the thickness of gear by comparing two sands with different grain size.

Theoretical and empirical equations developed for calculating the magnitude of earthquakes are affected by a lot of parameters. Most of these parameters need to be measured and entered in the equations accurately, while, in many areas, due to the lack of required equipment, these parameters mostly are measured approximately and with low precision or even sometimes assumed. Moreover, these equations usually are exclusive of a specific region or state, so they are not reliable enough for other new regions.
On the other hand, neural networks have been proven to be one of the most practical effects in modelling and forecasting. There are three major advantages of neural networks. First, neural networks are able to learn any complex non-linear mapping. Second, they do not make a priori assumption about the distribution of data. Third, they are very flexible with respect to incomplete, missing and noise data (Vellido et al, 1999). Moreover, neural networks, regardless of the region and country, are a general solution in all areas.
The aim of this paper is to use a kind of neural network system named Multilayer Perceptron (MLP), which is one of the most influential neural network models, to predict the magnitude of the earthquakes.
This method consists of several layers of nodes. It includes an input layer, an output layer, and a hidden layer, each of which contain input node(s), output node(s), and hidden node(s), respectively.
The input nodes are based on some variables. In the current research, six independent variables including three spatial variables, one time variable and two variable related to physical characteristics are defined. The output nodes of neural networks are the prediction outputs or labels.
The seismic data that have been used in the research are got from the whole instrumentally recorded earthquakes occurred in Iran.
From whole data, 85% are used for network training and 10% for network testing and revising. The remained 5% is dedicated to derive the final prediction of the magnitudes of earthquakes. Then, these predictions have been compared with exact values to assess the network prediction ability.
In the hidden layer, as there is no method to decide the optimal number of hidden nodes directly, four different numbers of hidden nodes are chosen, including 8, 12, 16 and 20. Moreover, a well-known concern with neural networks is ‘‘overtraining”. To ease this problem, a set of four different learning epochs are used, including 1, 2, 4 and 8. Moreover, in training part, two different training methods, named Batch and Online, were applicable. In order to reach to more comprehensive results, both of these methods are applied. As a result, we setup 32 different groups of parameters and models.
After all, the results of the study indicate that MLP network has a good capability for predicting the magnitude of earthquakes. The average correct prediction of the models is about 70%.
To conclude, according to the results, the network is a functional device in predicting the magnitude of the earthquake of a region in an arbitrarily considered time.