Seismic risk assessment of the 3rd Azerbaijan gas pipeline in Iran

A comprehensive seismic risk assessment has been performed for the existing route of the 3rd Azerbaijan natural gas buried pipeline in Iran. The major active seismic sources along the pipeline were identified and the geometrical parameters as well as the seismicity rates were determined. The seismic hazard assessment of the ground vibrations along the pipeline was performed in the framework of the Probabilistic Seismic Hazard Analysis using the CRISIS 2007 software. All of the components of the gas pipeline along the route were identified and the corresponding fragility functions are established through the methodology described in the HAZUS guideline (HAZUS MH MR4 Technical manual 2007 Department of homeland security emergency. Preparedness and Response Directorate, FEMA). A detailed cost analyses was taken into consideration based on the expert opinions in the National Iranian Gas Company, in order to provide more practical loss model for the pipeline route. Also, a simple method is suggested in order to account for the vent gas in the total loss estimation. The spatial analysis of the hazard function layer in combination with the loss model layer, in Geographical Information System  (GIS) platform, reveal the financial consequences of different earthquake scenarios.     

GeoTxt: A scalable geoparsing system for unstructured text geolocation

In this article we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate.     

Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.     

Effect of seismic waves on the hydro-mechanical properties of fractured rock masses

The transmission of seismic waves in a particular region may influence the hydraulic properties of a rock mass, including permeability, which is one of the most important. To determine the effect of a seismic wave on the hydraulic behavior of a fractured rock mass, systematic numerical modeling was conducted. A number of discrete fracture network(DFN) models with a size of 20 m × 20 m were used as geometrical bases, and a discrete element method(DEM) was employed as a numerical simulation tool. Three different boundary conditions without(Type Ⅰ) and with static(Type Ⅱ) and dynamic(Type Ⅲ) loading were performed on the models, and then their permeability was calculated. The results showed that permeability in Type Ⅲ models was respectively 62.7% and 44.2% higher than in Type I and Type Ⅱ models. This study indicates that seismic waves can affect deep earth, and, according to the results, seismic waves increase the permeability and change the flow rate patterns in a fractured rock mass.     

Post-cyclic Pullout Response of Geogrid in Compacted Sand and Clayey Sand with Emphasis on Pre-cyclic Load Effect

Geotechnical and Geological Engineering - Soil-geosynthetic interface behavior under cyclic loading will differ from that under static loading. To this end, a series of static and multistep tests...     

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.     

Genesis of the Nasirabad manganese occurrence,Fars province,Iran: Geochemical evidences

The Nasirabad manganese occurrence is located to the south of the Neyriz in the Fars province. Structurally and lithologically, this occurrence lies in the southwest part of the Zagros Thrust Belt and was deposited as Mn-nodules and interconnected ore-bearing interlayer's with radiolarite cherts in the vicinity of the Neyriz ophiolites. The present work deals with the geology and geochemistry of the Nasirabad manganese occurrence with a discussion of its genesis. High Mn/Fe (average 18.85) and high Ba (average 28,830 ppm) with low Pb (2.0 ppm) and LREE > HREE, La_n/Nd_n (average 4.5), Dy_n/Yb_n (average 1.2) and negative Eu anomaly suggest distal hydrothermal source. The Co/Zn (average 2.2), Ce/La ratio (average 0.67) and trace element discrimination diagrams indicate hydrothermal–hydrogenous processes. Y/Ho ratio (average 24.85) and strong positive correlation coefficient between major oxides and some high field strength elements (HFSE) like; TiO₂ vs Fe₂O₃ (r = 0.98), Al₂O₃ vs Zr (r = 0.97), Al₂O₃ vs Fe₂O₃ (r = 0.98), Zr vs K₂O (r = 0.98), Nb vs TiO₂ (r = 0.92), Th vs Fe₂O₃ (r = 0.76), Th vs MgO (r = 0.86) reveal the presence of volcaniclastics and (or) terrigenous detritals of mafic composition being deposited into the depositional basin. It seems that intermittent interlayering of mafic detrital material derived from volcanic eruption of the Neyriz island arc directly affected the physicochemical conditions of hydrothermal ore precipitation in this basin and consequently the Nasirabad manganese ores represent hydrothermal–hydrogenous geochemical characteristics. The Nasirabad is an example of non-sulphidic, oxic Mn-mineralization. Similar trend between the enrichment and depletion of some bioessential elements (e.g., Mn, As, Ba, Sr, Co, Ce) might have been resulted from selective sequestering of metal ions by microbial processes and hence hydrogenous characteristics may also be the result of biogenetic processes. Moreover the high dilution of distal hydrothermal exhalations by sea water cannot be ruled out.     

An evolutionary approach to modelling the thermomechanical behaviour of unsaturated soils

A new data‐mining approach is presented for modelling of the stress–strain and volume change behaviour of unsaturated soils considering temperature effects. The proposed approach is based on the evolutionary polynomial regression (EPR), which unlike some other data‐mining techniques, generates a transparent and structured representation of the behaviour of systems directly from raw experimental (or field) data. The proposed methodology can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed models allow the user to gain a clear insight into the behaviour of the system. Unsaturated triaxial test data from the literature were used for development and verification of EPR models. The developed models were also used (in a coupled manner) to produce the entire stress path of triaxial tests. Comparison of the EPR model predictions with the experimental data revealed the robustness and capability of the proposed methodology in capturing and reproducing the constitutive thermomechanical behaviour of unsaturated soils. More importantly, the capability of the developed models in accurately generalizing the predictions to unseen data cases was illustrated. The results of a sensitivity analysis showed that the models developed from data are able to capture and represent the physical aspects of the unsaturated soil behaviour accurately. The merits and advantages of the proposed methodology are also discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

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.