Optimization of Gravity Concrete Dams Using the Grasshopper Algorithm (Case Study: Koyna Dam)

Geotechnical and Geological Engineering - The aim of this study is to optimize the geometric dimensions of the Koyna concrete weight dam with and without seismic forces using the grasshopper...     

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.     

A tectono-stratigraphic record of an extensional basin: the Lower Jurassic Ab-Haji Formation of east-central Iran

The Lower Jurassic Ab-Haji Formation consists of siliciclastic strata which are widespread and superbly exposed across the Tabas and Lut blocks of east-central Iran. The formation records the geodynamic history of central Iran during the Early Jurassic in the aftermath of the main Cimmerian event (near the Triassic–Jurassic boundary) through its sedimentary facies and stratigraphic architecture and allows palaeogeographic and palaeoenvironmental reconstructions. We measured and studied three well-exposed outcrop sections and identified lithofacies and facies associations (fluvial plain, delta plain, delta front, prodelta, and shallow-marine siliciclastic shelf). The integration of all geological, stratigraphic, and sedimentological data shows a west-to-east continental-to-marine gradient within the Ab-Haji Formation. Based on thickness variations, lateral facies changes, palaeocurrent patterns, and changes in the nature of the basal contact of the Ab-Haji Formation on the Tabas and Lut blocks, we locate the fault-bounded Yazd Block in the west and the Shotori Swell at the eastern edge of the Tabas Block as provenance regions. The pattern of thickness variations, rapid east–west facies changes, and provenance is best explained by a tectonic model invoking large tilted fault blocks in an extensional basin. The basal unit shows distinct increase in grain size at the base of the Ab-Haji Formation, similar to the Shemshak Group of the Alborz Mountains (the base of the Alasht Formation) and the non-marine time-equivalent succession of the Binalud Mountains of northeastern Iran. This grain size pattern may have been caused by rapid source area uplift due to slab break-off of the subducted Iran plate in the course of the Cimmerian collision in east-central Iran.     

Pressuremeter Modulus and Limit Pressure of Clayey Soils Using <Emphasis Type="Italic">GMDH</Emphasis>-Type Neural Network and Genetic Algorithms

Pressuremeter modulus (\(E_{M}\)) and limit pressure (\(P_{L}\)) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the \(E_{M}\) and \(P_{L}\) to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the \(E_{M}\) and \(P_{L}\) of clayey soils. The \(E_{M}\) and \(P_{L}\) were modeled as a function of three variables including the moisture content (\(\omega\)), plasticity index and corrected SPT blow counts (\(N_{60}\)). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the \(N_{60}\) is the most influential parameter on the PMT parameters.     

Developing the Rule of Thumb for Evaluating Penetration Rate of TBM,Using Binary Classification

Geotechnical and Geological Engineering - Using the tunnel boring machine (TBM) in tunneling projects contributes significantly to increased efficiency and reducing the time of project...     

Identification of a hydrodynamic threshold in karst rocks from the Biscayne Aquifer, south Florida, USA

A hydrodynamic threshold between Darcian and non-Darcian flow conditions was found to occur in cubes of Key Largo Limestone from Florida, USA (one cube measuring 0.2 m on each side, the other 0.3 m) at an effective porosity of 33% and a hydraulic conductivity of 10 m/day. Below these values, flow was laminar and could be described as Darcian. Above these values, hydraulic conductivity increased greatly and flow was non-laminar. Reynolds numbers (Re) for these experiments ranged from <0.1 to 7. Non-laminar flow conditions observed in the hydraulic conductivity tests were observed at Re close to 1. Hydraulic conductivity was measured on all three axes in a permeameter designed specifically for samples of these sizes. Positive identification of vertical and horizontal axes as well as 100% recovery for each sample was achieved. Total porosity was determined by a drying and weighing method, while effective porosity was determined by a submersion method. Bulk density, total porosity and effective porosity of the Key Largo Limestone cubes averaged 1.5 g/cm³, 40 and 30%, respectively. Two regions of anisotropy were observed, one close to the ground surface, where vertical flow dominated, and the other associated with a dense-laminar layer, below which horizontal flow dominated.     

Assessment of bioclimatic comfort conditions based on Physiologically Equivalent Temperature (PET) using the RayMan Model in Iran

In this study thermal comfort conditions are analyzed to determine possible thermal perceptions during different months in Iran through the Physiologically Equivalent Temperature (PET). The monthly PET values produced using the RayMan Model ranged from ?7.6°C to 46.8°C. Over the winter months the thermal comfort condition (18–23°C) were concentrated in southern coastal areas along the Persian Gulf and Oman Sea. Most of the country experienced comfort conditions during the spring months, in particular in April, while during the summer months of July and August no thermal comfort conditions were observed. In November coastal areas of the Caspian Sea had the same physiological stress level of thermal comfort as April. The map produced showing mean annual PET conditions demonstrated the greatest spatial distribution of comfortable levels in the elevation range from 1000 to 2000 meter a.s.l., with annual temperatures of 12–20°C and annual precipitation of under 200 mm. The statistical relationship between PET conditions and each controlling parameter revealed a significant correlation in areas above 2000 meter, annual temperature over 20°C and annual precipitation of 200–400 mm with a correlation coefficient (R ²) of 0.91, 0.97 and 0.96, respectively.     

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.     

Spatial rules that generate urban patterns: Emergence of the power law in the distribution of axial line length

This paper studies emergence/generation of power law in rank-order distribution of axial line length, which is a global pattern observed in real cities, due to interaction of a set of seven simple spatial rules at a local scale. These rules and their interactions form a model expected to simulate the morphological structure of free spaces in unplanned organic pedestrian small cities. Effects of each of the seven rules are discussed through repeated simulations of eight possible combinations of the rules, using a bottom-up process. The results show that the rules generate environments with statistically stable rank-order distribution of axial line length that follows the power law. It means that the axial maps of the simulated environments have a scale-free hierarchical structure such that their distributions lean toward short axial lines. It also represents dominance of local spatial structure, as the model renders a faster rate of growth at a local scale while allowing a steady growth at a global scale.     

Prediction of drought in dry lands through feedforward artificial neural network abilities

Drought is one of the most important natural hazards in Iran. It is especially more prevalent in arid and hyper arid regions where there are serious limitations in regard to providing sufficient water resources. On the other hand, drought modeling and particularly its prediction can play important role in water resources management under conditions of lack of sufficient water resources. Therefore, in this study, drought prediction in a hyper arid location of Iran (Ardakan region) has been surveyed based on the abilities of artificial neural. Standardized Precipitation Index (SPI) in different time scales (3, 6, 9, 12, and 24 monthly time series) computed based on the data gathered from four rain gauge stations. After evaluation and testing of different artificial neural networks (ANN) structures, gradient descent back propagation (traingd) network showed higher abilities than others. Then, the predictions of SPI time series with different monthly lag times (1:12 months) were tested. Generally, drought prediction by ANNs in the Ardakan region has shown considerable results with the correlation coefficient (R) more than 0.79 and in the most cases and it rises more than 0.90, which indicates the ANN’s ability of drought prediction.