Prediction of the sawing quality of Marmarit stones using the capability of artificial neural network

The sawing rate is one of the most significant and effective parameters in extracting building stones via diamond wire sawing. This parameter designates the capability of diamond wire sawing for sawing different stones; in addition, the parameter gives rise to economical considerations for quarry designers. In this study, the existent relations between stone geotechnical parameters and the sawing rate of stones via diamond wire sawing were analyzed using regression and correlation coefficient as well as the collected data from Marmarit stone quarries. Moreover, we estimated the sawing rate of Marmarit using the dimensional stone rock mass rating (DSRMR); upon comparison of the data obtained from DSRMR our pre‐collected data on quarries, we did not gain satisfactory results from DSRMR, hence we used artificial neural network (ANN). The results showed that the percentage of Silica, the coefficient of water absorption, the uniaxial compressive strength (UCS), and abrasive hardness are the proper parameters for creating the ANN. Discontinuities have the least effects possible on diamond wire sawing. Having given the training possibility of the ANN, and its ability to evaluate relations among input parameters, the ANN, which was being trained with Marmarit's traits, was an accurate network for estimating diamond wire sawing in Marmarit quarries, although it could not generalize this network for other stones such as Chini and Crystal. Copyright © 2011 John Wiley & Sons, Ltd.     

Investigating effects of land use change scenarios on soil erosion using CLUE-s and RUSLE models

Predicting soil erosion change is an important strategy in watershed management. The objective of this research was to evaluate land use change effects on soil erosion in the north of Iran using five land use scenarios. Three land use maps were created for a period of 25 years (1986–2010) to investigate land use transition and to simulate land use for the year 2030. Additionally, the RUSLE model was used to estimate erosion and the effect of land use change. The results showed that CLUE-s is suitable for modeling future land use transition using ROC curve. The median soil loss in the basis period was 104.52 t ha^?1 years^?1. Results indicate that the range of soil loss change is 2–32% in simulated period and soil loss value was higher than basis period in all scenarios. Thirty percent decrease in demand scenario has the lowest soil loss in simulated period, and the soil loss value under this scenario will be only 2% more than the basis period. Thus, the soil conversion effects resulted from the demand of each land use.     

Parameters affecting the occurrence of quicksand and the drying up of large diameter wells that gain water from the bottom: a case study from Iran

Large diameter fully cased wells that gain water from the bottom are often dug in sandy and collapsible aquifers. They have cylindrical vertical walls lined with brick or concrete. The well bottom is partially filled with aquifer material through which the flow is vertically upward. When the vertical hydraulic gradient reaches a critical value, quicksand occurs and the well structure can be destroyed. Another difficulty encountered is drawdown in the wellbore and the drying up of the well. To overcome these problems, the flow around and beneath these wells is numerically simulated. The simulation results are used to investigate the effect of well and aquifer parameters on quicksand and drawdown. For practical purposes, the dimensionless drawdown-time and dimensionless vertical gradient-time curves are developed. It was found that the ratio of filling material thickness to well radius affects the shape of these type curves. The type curves may be used to predict the time after pumping commences when quicksand occurs and the well dries up. They are also useful to design the safe pumping rate and duration as well as the optimum well radius. These are demonstrated by analyzing the pumping test data from a case study in the arid Chah Kutah region, southern Iran.     

Effect of wellbore storage and finite thickness skin on flow to a partially penetrating well in a phreatic aquifer

An analytical model is presented for the analysis of constant flux tests conducted in a phreatic aquifer having a partially penetrating well with a finite thickness skin. The solution is derived in the Laplace transform domain for the drawdown in the pumping well, skin and formation regions. The time-domain solution in terms of the aquifer drawdown is then obtained from the numerical inversion of the Laplace transform and presented as dimensionless drawdown–time curves. The derived solution is used to investigate the effects of the hydraulic conductivity contrast between the skin and formation, in addition to wellbore storage, skin thickness, delayed yield, partial penetration and distance to the observation well. The results of the developed solution were compared with those from an existing solution for the case of an infinitesimally thin skin. The latter solution can never approximate that for the developed finite skin. Dimensionless drawdown–time curves were compared with the other published results for a confined aquifer. Positive skin effects are reflected in the early time and disappear in the intermediate and late time aquifer responses. But in the case of negative skin this is reversed and the negative skin also tends to disguise the wellbore storage effect. A thick negative skin lowers the overall drawdown in the aquifer and leads to more persistent delayed drainage. Partial penetration increases the drawdown in the case of a positive skin; however its effect is masked by the negative skin. The influence of a negative skin is pronounced over a broad range of radial distances. At distant observation points the influence of a positive skin is too small to be reflected in early and intermediate time pumping test data and consequently the type curve takes its asymptotic form.     

Spatial estimation of water-table depth by artificial neural networks in light of ancillary data

Proper estimation of the spatial distribution of water-table depth is highly important in most groundwater studies. Groundwater depth is measured at specific and limited points and it is estimated for other parts using spatial estimation methods. In this study, two multivariate methods, artificial neural network (ANN) and multiple linear regression (MLR), are examined to estimate water-table depth in an unconfined aquifer located in Shibkooh, Iran. The different ancillary data, including spatial coordinates, digital elevation model (DEM), aquifer bed elevation, specific resistivity and aquifer thickness were used to improve estimates based on these methods. It was proved that performance of the ANN surpasses that of the MLR method. Using the spatial coordinates, the aquifer bed elevation and aquifer thickness resulted in the optimum spatial estimation of the water-table depth. These parameters, directly or indirectly, affect the water-table depth estimation through techniques such as ANN capable of modelling of nonlinear relationships.     

Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran

Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.

     

Seismic behaviour of pile groups by hybrid experiments

Non-linear seismic soil-pile interaction was studied with a hybrid procedure that used a pseudo-dynamic testing (PDT) method modified to account for frequency dependence and developed for foundation-soil systems. The numerical scheme used in the conventional PDT was improved by the introduction of a time-dependent pseudo-forcing function derived from the frequency-dependent dynamic characteristics of the system by Hilbert transformation in the frequency domain. Single, 2-, 3- and 9-pile group foundation models were used, their mechanical characteristics later being determined from static and forced vibration dynamic tests. Amplitude scaling was used for three recorded accelerograms. Data recorded during an earthquake at the site of the experiments revealed that the proposed methodology predicts well seismic nonlinear interaction and accounts for frequency dependence and non-linearity in the time domain.     

Hybrid experiments on non-linear earthquake-induced soil-structure interaction

Non-linear seismic soil-structure interaction is studied through a hybrid procedure using the pseudo-dynamic testing (PDT) method which is modified to take into account frequency dependence and developed for foundation-soil systems. The numerical scheme used in conventional PDT is improved by introduction of a time-dependent pseudo-forcing function which is derived from frequency-dependent dynamic characteristics of the system by means of Hilbert transformation in the frequency domain. Surface, shallow and caisson foundation models that differed in size and depth of embedment were used. The mechanical characteristics of the systems were determined from static and forced vibration dynamic tests. An amplitude scaling technique was used for three recorded accelerograms.     

Determination of sand dune characteristics through geomorphometry and wind data analysis in central Iran (Kashan Erg)

The combination of wind measurements and remotely sensed geomorphometry indices provides a valuable resource in the study of desert landforms, because arduous desert environments are difficult to access. In this research, we couple wind data and geomorphometry to separate and classify different sand dunes in Kashan Erg in central Iran. Additionally, the effect of sand-fixing projects on sand dune morphology was assessed using geomorphometry indices (roughness, curvature, surface area, dune spacing and dune height). Results showed that a Digital Elevation Model of the National Cartographic Center of Iran (NCC DEM) with 10-m resolution and accuracy of 54% could discriminate geomorphometry parameters better than the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 30-m resolution and Shuttle Radar Topography Mission (SRTM) data with 90-m resolution and 45.2 and 1.6% accuracy, respectively. Low classification of SRTM DEM was associated with too many non-value points found in the DEM. Accuracy assessment of comparison ground control points revealed that ASTER DEM (RMSE = 4.25) has higher accuracy than SRTM and NCC DEMs in this region. Study of curvature showed that transverse and linear sand dunes were formed in concave topography rather than convex. Reduced slopes in fixed sand dunes were established due to wind erosion control projects. Measurements of dune height and spacing show that there is significant correlation in compound dunes (R ² = 0.546), linear dunes (R ² = 0.228) and fixed dunes (R ² = 0.129). In general, the height of dunes in Kashan Erg increases from the margin of the field to the center of the field with a maximum height of 120 m in star dunes. Analysis of wind data showed that sand drift potential is in low-medium class in Kashan Erg. Linear sand dunes in Kashan Erg show that they are following a global trend in forming of these. Finally, established of geomorphometry method in dune classification will help researchers to identify priority of land management and performance assessment of sand dunes fixing projects in arid arduous environment.