Prediction of Rock Abrasivity Index (RAI) and Uniaxial Compressive Strength (UCS) of Granite Building Stones Using Nondestructive Tests     

Farhadian  Ali  Ghasemi  Ebrahim  Hoseinie  Seyed Hadi  Bagherpour  Raheb 《Geotechnical and Geological Engineering》2022,40(6):3343-3356

Geotechnical and Geological Engineering - Rock abrasivity index (RAI) and uniaxial compressive strength (UCS) are two key parameters for assessing abrasivity and durability of building stones,...  相似文献   

Determination and Assessment of Parameters Influencing Rock Mass Cavability in Block Caving Mines Using the Probabilistic Rock Engineering System   总被引：1，自引：0，他引：1  

Ramin Rafiee  Mohammad Ataei  Reza Khalokakaie  Seyed Mohammad Esmaeil Jalali  Farhang Sereshki 《Rock Mechanics and Rock Engineering》2015,48(3):1207-1220

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Trend analysis of the rainfall erosivity index at different time scales in Iran   总被引：2，自引：0，他引：2  

Seyed Hamid Reza Sadeghi  Zeinab Hazbavi 《Natural Hazards》2015,77(1):383-404

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Dispersive thermohaline convection near salt domes: a case at Napoleonville Dome,southeast Louisiana,USA     

Zahra Jamshidzadeh  Frank T. -C. Tsai  Hasan Ghasemzadeh  Seyed Ahmad Mirbagheri  Majid Tavangari Barzi  Jeffrey S. Hanor 《Hydrogeology Journal》2015,23(5):983-998

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Probabilistic analysis of extreme regional meteorological droughts by L-moments in a semi-arid environment   总被引：1，自引：1，他引：0  

Javad Abolverdi  Davar Khalili 《Theoretical and Applied Climatology》2010,102(3-4):351-366

Regional extreme value analyses of drought characteristics provide information on probabilistic nature of drought occurrence, viewed as an essential tool in drought mitigation and planning. In this paper, L-moments are used to investigate the regional characteristics and probabilistic behavior of drought severity levels, represented by the Standardized Precipitation Index (SPI) annual minima (the minimum monthly SPI value). Rainfall data of 3, 6, 12, and 24 month time scales are investigated. A regional watershed in southwestern Iran is used as a case study area. The semi-arid nature of the study area requires appropriate selection of rainfall data. The boxplot approach is used to select those months with adequate data time series for the SPI analysis. Appropriateness of the suggested data time series is discussed in the context of the research by Wu et al. (2007). Based on the results, all of the suggested time scales are found appropriate for SPI investigations. For each time scale of interest regional homogeneity is evaluated and the best regional/sub-regional probability distribution function is selected. Regional quantiles are estimated for different time scales and their variability with respect to return period is discussed.  相似文献   

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran     

Qihao Weng  Mohammad Karimi Firozjaei  Amir Sedighi  Seyed Kazem Alavipanah 《地理信息系统科学与遥感》2019,56(4):576-604

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.  相似文献   

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features   总被引：1，自引：1，他引：0  

Seyed Amir Naghibi  Mostafa Moradi Dashtpagerdi 《Hydrogeology Journal》2017,25(1):169-189

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.  相似文献   

The effect of different meteorological parameters on the temporal variations of reference evapotranspiration     

Neda Khanmohammadi  Hossein Rezaie  Majid Montaseri  Javad Behmanesh 《Environmental Earth Sciences》2017,76(15):540

Temporal changes of meteorological variables can affect reference evapotranspiration (ET₀). The goal of the present research is to analyze the changes of ET₀ and identify the impact of effective meteorological parameters to the changes of ET₀. For this purpose, daily meteorological data recorded in 30 synoptic stations of Iran during 1960–2014 were used. The annual and seasonal values of ET₀ were calculated by the recorded data. To calculate ET₀, FAO56 Penman–Monteith method (standard method) was used. The annual and seasonal trends of ET₀ and its eight effective parameters were analyzed. Then the contributions of effective parameters changes on ET₀ were determined. To analyze ET₀ trend at annual and seasonal scales, two common methods, Spearman’s Rho and Mann–Kendall tests, were used. The R ² = 0.99 showed that the results of the mentioned methods were similar and on the basis of T-statistic <0.057, their difference was not significant (95% confidence level). Therefore, only one method’s results (Spearman’s Rho) were reported. On the basis of Spearman’s Rho results, the annual and seasonal values of ET₀ had negative trend in most of arid and semi-arid stations while the trend of this parameter was positive in humid and very humid stations. At annual and seasonal scales, decreasing in wind speed (W), temperature (T), sunshine hours (n), minimum temperature (TN), dew point temperature (TD), maximum temperature (TX), saturation vapor pressure deficit (SVPD) and solar radiation (RS) was observed in 58, 54, 39, 43, 56, 65, 65 and 37% studied stations, respectively. In many scales, the results showed that TX and W were the most effective meteorological variables on ET₀ changes and then SVPD was located in second step in arid and semi-arid stations. In humid and very humid stations, W was the first effective parameter at all scales, except autumn.  相似文献   

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters     

Mohammad Isazadeh  Seyed Mostafa Biazar  Afshin Ashrafzadeh 《Environmental Earth Sciences》2017,76(17):610

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.  相似文献   

Implementation of online model updating in hybrid simulation          下载免费PDF全文

M. Javad Hashemi  Armin Masroor  Gilberto Mosqueda 《地震工程与结构动力学》2014,43(3):395-412

Hybrid simulation combines numerical and experimental methods for cost‐effective, large‐scale testing of structures under simulated earthquake loading. Structural system level response can be obtained by expressing the equation of motion for the combined experimental and numerical substructures, and solved using time‐stepping integration similar to pure numerical simulations. It is often assumed that a reliable model exists for the numerical substructures while the experimental substructures correspond to parts of the structure that are difficult to model. A wealth of data becomes available during the simulation from the measured experiment response that can be used to improve upon the numerical models, particularly if a component with similar structural configuration and material properties is being tested and subjected to a comparable load pattern. To take advantage of experimental measurements, a new hybrid test framework is proposed with an updating scheme to update the initial modeling parameters of the numerical model based on the instantaneously‐measured response of the experimental substructures as the test progresses. Numerical simulations are first conducted to evaluate key algorithms for the selection and calibration of modeling parameters that can be updated. The framework is then expanded to conduct actual hybrid simulations of a structural frame model including a physical substructure in the laboratory and a numerical substructure that is updated during the tests. The effectiveness of the proposed framework is demonstrated for a simple frame structure but is extendable to more complex structural behavior and models. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献