Prediction of Enhanced Soil–Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming     

Abdi  Mahmood Reza  Nakhaei  Pouria  Safdari Seh Gonbad  Mahdi 《Geotechnical and Geological Engineering》2021,39(2):957-972

Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σ_n) and anchorage length (L) and the least effective parameters are average particle size (D₅₀) and anchor base area (A_b).

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APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies     

Seyed Amir Naghibi  Hossein Hashemi  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(6):90-104

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.  相似文献   

Pore-water pressure development in a frozen saline clay under isotropic loading and undrained shearing     

Lyu  Chuangxin  Nishimura  Satoshi  Amiri  Seyed Ali Ghoreishian  Zhu  Feng  Eiksund  Gudmund Reidar  Grimstad  Gustav 《Acta Geotechnica》2021,16(12):3831-3847

Acta Geotechnica - A systematical testing program on frozen Onsøy clay under isotropic loading and undrained shearing at different temperatures...  相似文献   

Geochemical Prospectivity Mapping Through a Feature Extraction–Selection Classification Scheme     

Zekri  Hamid  Cohen  David R.  Mokhtari  Ahmad Reza  Esmaeili  Abbas 《Natural Resources Research》2019,28(3):849-865

Natural Resources Research - Machine learning (ML) schemes can enhance success in geochemical prospectivity mapping. This study has examined the effectiveness of several feature extraction or...  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   

Impact of Reservoir Permeability,Permeability Anisotropy and Designed Injection Rate on CO2 Gas Behavior in the Shallow Saline Aquifer at the CaMI Field Research Station,Brooks, Alberta     

Yu  Xinran  Ahmadinia  Masoud  Shariatipour  Seyed M.  Lawton  Don  Osadetz  Kirk  Saeedfar  Amin 《Natural Resources Research》2020,29(4):2735-2752

Natural Resources Research - Carbon capture and storage is part of Canada’s climate change action plan to reduce greenhouse gas emissions. The Containment and Monitoring Institute Field...  相似文献   

Antenna design and implementation for the future space Ultra-Long wavelength radio telescope     

Linjie Chen  Amin Aminaei  Leonid I. Gurvits  Marc Klein Wolt  Hamid Reza Pourshaghaghi  Yihua Yan  Heino Falcke 《Experimental Astronomy》2018,45(2):231-253

In radio astronomy, the Ultra-Long Wavelengths (ULW) regime of longer than 10 m (frequencies below 30 MHz), remains the last virtually unexplored window of the celestial electromagnetic spectrum. The strength of the science case for extending radio astronomy into the ULW window is growing. However, the opaqueness of the Earth’s ionosphere makes ULW observations by ground-based facilities practically impossible. Furthermore, the ULW spectrum is full of anthropogenic radio frequency interference (RFI). The only radical solution for both problems is in placing an ULW astronomy facility in space. We present a concept of a key element of a space-borne ULW array facility, an antenna that addresses radio astronomical specifications. A tripole–type antenna and amplifier are analysed as a solution for ULW implementation. A receiver system with a low power dissipation is discussed as well. The active antenna is optimized to operate at the noise level defined by the celestial emission in the frequency band 1 ? 30 MHz. Field experiments with a prototype tripole antenna enabled estimates of the system noise temperature. They indicated that the proposed concept meets the requirements of a space-borne ULW array facility.  相似文献   

Prediction of riverine suspended sediment discharge using fuzzy logic algorithms, and some implications for estuarine settings     

Saeed Jafari Mianaei  Ali Reza Keshavarzi 《Geo-Marine Letters》2010,30(1):35-45

The ability of fuzzy logic algorithms to model relationships between stream flow and suspended sediment discharge was investigated using daily measurements of stream flow and suspended sediment discharge for the Escanaba River mouth station, situated on the shore of Lake Michigan and operated by the US Geological Survey. Three different configurations of inputs were applied, whereby the inputs were fuzzified into fuzzy subsets of variables by means of triangular membership functions. The relationships between inputs and suspended sediment discharge (output) were represented by a set of fuzzy rule expressed in IF–THEN format. The weighted average method served for defuzzification. The commonly used sediment rating curve was also applied to the data, and its performance compared with that of the three models by means of statistical analyses. For all three models, suspended sediment discharge predicted by the fuzzy logic algorithm was in satisfactory agreement with observations. Furthermore, the fuzzy logic algorithms performed better than the sediment rating curve, particularly at higher rates of suspended sediment discharge (in this study, more than 50  × 10⁶ g/day). Considered collectively, the use of fuzzy logic algorithms is suggested as a simple and effective approach for better prediction of suspended sediment discharge, also for estuaries.  相似文献   

Numerical Prediction of Induced Pressure and Lift of the Planing Surfaces     

Hassan GHASSEMI  Ahmad Reza KOHANSAL  Mahmoud GHIASSI 《中国海洋工程》2009,23(2):221-232

This paper discusses the numerical prediction of the induced pressure and lift of the planing surfaces in a steady motion based on the potential flow solver as well as the spray drag by use of the practical method.The numerical method for computation of the induced pressure and lift is potential-based boundary element method.Special technique is identified to present upwash geometry and to determine the spray drag.Numerical results of a planing flat plate and planing craft model 4666 are presented.It is shown that the method is robust and efficient and the results agree well with the experimental measurements with various Froude humors.  相似文献   

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques     

Jian Zhou  Yingui Qiu  Danial Jahed Armaghani  Wengang Zhang  Chuanqi Li  Shuangli Zhu  Reza Tarinejad 《地学前缘(英文版)》2021,(3):201-213

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.  相似文献