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

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).

     

Causes and consequences of long-term groundwater overabstraction in Jordan

Hydrogeology Journal - In 2017, a comprehensive review of groundwater resources in Jordan was carried out for the first time since 1995. The change in groundwater levels between 1995 and 2017 was...     

On the effect of compaction on the progression of concentrated leaks in cohesive soils

Acta Geotechnica - The purpose of this study is to conduct hole erosion tests (HETs) to better understand the progression of concentrated leaks in compacted soils. While samples with high levels of...     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

Cluster Analysis of Petrophysical and Geological Parameters for Separating the Electrofacies of a Gas Carbonate Reservoir Sequence

Natural Resources Research - Assessing reservoir properties and knowing the relationship between different reservoir parameters can significantly help to plan for production from a reservoir. In...     

Application of Artificial Intelligence to Estimate Oil Flow Rate in Gas-Lift Wells

Natural Resources Research - Optimization and monitoring schemes for oil well and reservoir system require accurate estimation of production rate. Real-time monitoring is conducted typically using...     

Spatial analysis of water quality and its suitability in farming giant freshwater prawn (Macrobrachium rosenbergii) in Negeri Sembilan region,Peninsular Malaysia

Finding potential sites for resilient prawn production in the tropical environment that also prevents wastage of natural resources is not an easy task. The purpose of this study is to evaluate water quality suitability for prawn farming in Negeri Sembilan of Peninsular Malaysia based on Geographic Information System (GIS). To achieve this goal, numerous criteria including sources of water, water temperature, water pH, sources of pollution, salinity, soil texture and availability of phytoplankton criteria were considered for the modelling process. Analytic Hierarchy Process (AHP) technique was performed to standardize the criteria and the weighting process. The weighted overlay of indicators and results were accomplished by applying the Multi‐Criteria Decision Analysis (MCDA) method in GIS. It was indicated that the Negeri Sembilan area has potential for prawn farming. The results showed that about 25 per cent (163 056.93 ha) of the area was most suitable for prawn farming, about 58 per cent (384 656.88 ha) was considered moderately suitable, while 18 per cent (117 633.49 ha) was regarded as least suitable. The study concluded that the multi‐criteria decision analysis of water quality for prawn farming is vital for regional economic planning in the Negeri Sembilan area and also significant when establishing a model for aquaculture development.     

Socio-political and ecological stresses on traditional pastoral systems: A review

Journal of Geographical Sciences - Pastoralism is a viable socio-economic system-shaped by landless and agro-pastoral communities in many pastoral regions of the world. This system is mainly based...     

Isolation, distribution and evaluation of cytotoxic and antioxidant activity of cultivable actinobacteria from the Oman Sea sediments

Screening bioactive natural products from bacteria is a determinative step in the drug discovery programs. The present study aim to isolate actinobacteria from the Oman Sea sediments for determining the effects of different culture media and treatments on the yield of the isolation process, and measure the DPPH radical scavenging and Artemia cytotoxic activity of culture extracts of the actinobacterial isolates. A total of 290 actinobacterial isolates were collected from 14 sediment samples. Heat treatment(40.68%) and M_4 medium(29.31%) exhibited the maximum isolation rates of actinobacteria. Streptomyces isolates were dominantly distributed in all of the investigated stations according to 16 S rRNA gene sequencing. The distribution pattern of Streptomyces followed a depth-dependent frequency trend, whereas the members of rare genera including Micromonospora, Nocardia Actinoplanes, Nocardiopsis, Saccharopolyspora and Crossiella were distributed in deeper stations. Approximately,25% of the examined isolates could scavenge 90% of 10~(–4) mol/L DPPH solutions at 1 250 μg/mL final concentration of their ethyl acetate culture extracts. Furthermore, the most potent extracts could scavenge DPPH radicals with IC50 ranges from 356.8 to 566.4 μg/mL. Brine shrimp cytotoxicity tests showed that 38.88% of the examined culture extracts exhibited LC_(50) lower than 1 000 μg/mL against the Artemia cells. Moreover, the most potent culture extracts exhibited LC_(50) range from 335.4 to 534.4 μg/mL. Phylogenetic analysis by 16 S rRNA gene sequence revealed that the OS 005, OS 263 and OS 157 closely related to Streptomyces djakartensis, Streptomyces olivaceus and Nocardiopsis dassonvillei respectively. These results suggested the widespread distribution of the antioxidant and cytotoxic producing actinobacteria in the Oman Sea sediments, which could be considered as promising candidates for the discovery of microbial bioactive compounds.     

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

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.