DEM modeling of a monowire cutting system

Monowire block cutting machines can be used for natural stone block squaring and slab cutting operations. The plants where the cutting operations are performed demand high product quality with minimum operational costs. The major parameters affecting the economy of the operation are the energy consumed and the wear induced on the diamond beads during the cutting operation. An efficient cutting operation can only be maintained by selecting proper cutting parameters. Therefore, cutting parameters should be clearly understood. Experimental studies and numerical modeling methods are significant in terms of identifying the energy consumption occurring during natural stone cutting with monowire. Experimental studies and numerical modeling using discrete element method were performed on Afyon White Marble. Experimental studies have been performed by using a specially designed, fully automatic monowire cutting machine, and numerical analyses were carried out by commercially available software called three-dimensional particle flow code (PFC3D). A discrete element model for the cutting operation was developed, and various numerical models were performed for different peripheral speeds and cutting speeds, while, at the same time, the actual cutting operations were being carried out in the laboratory. Finally, the data obtained from the experimental works were compared with the data from numerical modeling. A comparison indicates that the frictional energy values obtained by means of numerical modeling are in good agreement with the results of the laboratory measurements. This study clearly put forward the influences of effective parameters on monowire cutting operations in natural stone industry. Furthermore, it filled an important space in the literature about the use of monowire block squaring machines.     

Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation

This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated.     

Cracking Problems in the Segments of Tabriz Metro Tunnel: A 3D Computational Study

This paper presents numerical models of tunnel segments with respect to the steel rebars using a finite element method under TBM thrust jack referring to the Tabriz metro line 2. The analyses have been carried out in five different steps. Numerical modellings have been done from the lowest ratio of reinforcement (the weakest case) to the highest ratio of it (the strongest case), which means that the curved longitudinal rebars were only placed within the concrete in the first step of the analysis, then other types of rebars are added gradually. The results indicated that stress contours in the z-direction (S33) of the model was approximately equal in all the steps. While distributed stress indicated more different behavior than the S33, which the maximum compressive and tensile distributed stresses are just applied on the reinforcement. The higher the reinforcement ratio, distributed stresses are increased step by step. So, it may be noticed that the reinforcement ratio was so crucial to the stresses which are needed to bear. A cracking form of the segment has been analyzed using different types of strains. The curved longitudinal rebars have a remarkable impact on cracking form in the z-direction in step 1, so that the magnitude of plastic strain in the z-direction (PE33) was remarkably decreased when they were placed within the concrete. Although adding the other main rebars has a negligible impact on the cracking form in z-direction whereas they can decline the plastic strain in the x-direction (PE11). The rebars which were added to the model in step 3, decrease the cracks around the shoulders of the segment. Nonetheless, all the rebars have not affected the segment behavior. Finally, in step 5, the cracking mechanism between the loading surfaces has completely changed. Also, displacement contours showed that the magnitude is decreased when the rebars are added to the reinforcement in every step.     

Evaluation of efficiency of different estimation methods for missing climatological data

Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001–2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.