(235)U, (238)U, (232)Th, (40)K and (137)Cs activity concentrations in marine sediments along the northern coast of Oman Sea using high-resolution gamma-ray spectrometry

The natural radioactivity levels in sediment samples of the northern coast of Oman Sea, covering the coastal strip from Hormoz canyon to Goatr seaport, as the first time has been determined. The results of measurements will serve as background reference level for Oman Sea coastlines. Sediments from 36 coastal and near shore locations were collected for analysis. Analysis on the collected samples were carried out to determine (235)U, (238)U, (232)Th, (40)K and (137)Cs using two high purity germanium detectors with 38.5% and 55% relative efficiencies. The concentration of (235)U, (238)U, (232)Th, (40)K and (137)Cs in sediment samples ranged between 1.01 and 2.87Bq/kg, 11.83 and 22.68Bq/kg, 10.7 and 25.02Bq/kg, 222.89 and 535.07Bq/kg and 0.14 and 2.8Bq/kg, respectively. The radium equivalent activity was well below the defined limit of 370Bq/kg. The external hazard indices were found to be less than 1, indicating a low dose.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

A hybrid clustering-fusion methodology for land subsidence estimation

In many parts of Canada, limited data are available for hydrodynamic model inputs, and the ability to generate quality flood grids through 1D, 2D or 3D methods is nonviable. In this paper, the capability of simplified flood models, which rely solely on digital terrain models (DTMs), was explored to assess the quality and speed of their results. Results were validated against historic floods in two locations. Three non-physics-based simplified conceptual flood models were tested: (1) planar method, (2) inclined plane and (3) height above nearest drainage network (HAND) model. The accuracy and performance were evaluated using three criteria: inundation extent, water depth and computation time. Findings show that the HAND model is the best predictor of inundation extent, with Probability of Detection and Critical Success Index being higher than 0.90 in both study areas. Though the preprocessing time for the HAND model is lengthy, once completed, the time to simulate flooding at a variety of water levels is rapid, making this model the most suitable choice for web-based, on-demand flood inundation mapping. Knowledge of the fit of these flood models and associated uncertainty can be helpful to emergency managers such that they can better understand exposure and vulnerability while preparing flood response plans.     

GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.     

TISS: a decision framework for tailing impoundment site selection

Tailing dam is one of the most important mining operations interface with surrounding environment even as long as many years after ore reserve exhausted. Therefore, appropriate design and management respect to future limitations of environmental regulations is required. Recently, effect of multiple criteria on tailing impoundment site selection makes it complex as the conventional procedures unable to answer. The systematic approach of multi attribute decision-making helps decision-makers select the most preferable decision and provide the basis of a decision support system. This paper developed new strategy based on fuzzy multi attribute group decision-making methods including: technique for order preference by similarity to ideal solution and analytical hierarchy process in fuzzy group environment. A hypothetical case is processed to demonstrate the strategy’s efficiency and results are compared and ranked so that the most preferable option is identified.     

Efficiency of employing fiber-based finite-length plastic hinges in simulating the cyclic and seismic behavior of steel hollow columns compared with other common modelling approaches

The accuracy and efficiency of the modelling techniques utilized to model the nonlinear behavior of structural components is a significant issue in earthquake engineering. In this study, the sufficiency of three different modelling techniques that can be employed to simulate the structural behavior of columns is investigated. A fiber-based finite length plastic hinge (FB-FLPH) model is calibrated in this study. In order to calibrate the FB-FLPH model, a novel database of the cyclic behavior of hollow steel columns under simultaneous axial and lateral loading cycles with varying amplitudes is used. By employing the FB-FLPH model calibrated in this study, the interaction of the axial force and the bending moment in columns is directly taken into account, and the deterioration in the cyclic behavior of these members is implicitly considered. The superiority of the calibrated FB-FLPH modelling approach is examined compared with the cases in which conventional fiber-based distributed plasticity and concentrated plasticity models are utilized. The efficiency of the enumerated modelling techniques is probed when they are implemented to model the columns of a typical special moment frame in order to prove the advantage of the FB-FLPH modelling approach.

     

The effect of soil–structure interaction on the seismic risk to buildings

This paper studies the effect of soil–structure interaction (SSI) on the seismic risk estimates of buildings. Risk, in this context, denotes the probability distribution of seismic monetary loss due to structural and nonstructural damage. The risk analysis here uncovers the probability that SSI is beneficial, detrimental, or uninfluential on seismic losses. The analyses are conducted for a wide range of buildings with different structural systems, numbers of stories, and foundation sizes on various soil types. A probabilistic approach is employed to account for prevailing sources of uncertainty, i.e., those in ground motion and in the properties of the soil–structure system. In this approach, probabilistic models are employed to predict the response, damage, and repair cost of buildings. To properly account for the ground motion uncertainty, a suite of nearly 7000 accelerograms recorded on soil is employed. It is concluded that structures on very soft soils are extremely likely to incur smaller losses due to SSI, which is in line with the common belief that SSI is a favorable effect for such systems. However, the results for buildings on moderately soft soils reveal a considerable probability, up to 0.4, that SSI has an adverse effect on the structure and increases the seismic losses.