A study of differentiation pattern and rare earth elements migration in geochemical and hydrogeochemical environments of Airekan and Cheshmeh Shotori areas (Central Iran)

The Airekan and Cheshmeh Shotori areas are located about 60 km northeast of Khour, in Isfahan province from Central Iran. Research on characteristics and rare earth elements (REE) pattern in hydrogeochemical environments of these areas suggests the same origin for the elements dissolved in groundwater in these areas. Investigation of migration pattern of REE in hydrogeochemical environments shows that the migration and transportation of REE has occurred through chloride complexes. REEs, leached by water/rock interaction from the Airekan granite, are transported by groundwater and then precipitated in the Cheshmeh Shotori area. Study of the Cheshmeh Shotori sediments shows the presence of a sequence of red oxidized and dark layers. Geochemical characteristics of these sediments reveal that their REE characteristics are mainly inherited from the Airekan granite. Changes in the REE pattern of these sediments with depth show that changes in oxidation and reduction process have not played a significant role in controlling their behavior. It is crucial to note that adsorption of REEs dissolved in water by hydrosilicate increases these elements in depth. The REE behavior shows water/rock interaction between the granitic rocks and groundwater as the main factor of solution, migration and precipitation of REEs in the Cheshmeh Shotori area.     

Vertical geochemical variations and arsenic mobilization in the shallow alluvial aquifers of the Chapai-Nawabganj District, northwestern Bangladesh: implication of siderite precipitation

Core sediments from three disturbed boreholes (JOR, GHAT, and RAJ) and two undisturbed boreholes (DW1 and DW2) were collected in the study area of the Chapai-Nawabganj district of northwestern Bangladesh for geochemical analyses. In the study area, groundwater samples from fourteen As-contained private wells and five nested piezometers at both the DW1 and DW2 boreholes were also collected and analyzed. The groundwater arsenic concentrations in the uppermost aquifer (10–40 m of depth) range from 3 to 315 μg/L (mean 47.73 ± 73.41 μg/L), while the arsenic content in sediments range from 2 to 14 mg/kg (mean 4.36 ± 3.34 mg/kg). An environmental scanning electron microscope (ESEM) with an energy dispersive X-ray spectrometer was used to investigate the presence of major and trace elements in the sediments. Groundwaters in the study area are generally the Ca–HCO₃ type with high concentrations of As, but low levels of Fe, Mn, NO₃ ^? and SO ₄ ^?2 . The concentrations of As, Fe, Mn decrease with depth in the groundwater, showing vertical geochemical variations in the study area. Statistical analysis clearly shows that As is closely associated with Fe and Mn in the sediments of the JOR core (r = 0.87, p < 0.05 for Fe and r = 0.78, p < 0.05 for Mn) and GHAT core (r = 0.95, p < 0.05 for Fe and r = 0.93, p < 0.05 for Mn), while As is not correlated with Fe and Mn in groundwater. The comparatively low Fe and Mn concentrations in some groundwater and the ESEM image revealed that siderite precipitated as a secondary mineral on the surface of the sediment particles. The correlations along with results of sequential extraction experiments indicated that reductive dissolution of FeOOH and MnOOH represents a mechanism for releasing arsenic into the groundwater.     

Simple formulas for the dynamic stiffness of pile groups

Simple formulas are derived for the dynamic stiffness of pile group foundations subjected to horizontal and rocking dynamic loads. The formulations are based on the construction of a general model of impedance matrices as the condensation of matrices of mass, damping, and stiffness, and on the identification of the values of these matrices on an extensive database of numerical experiments computed using coupled finite element–boundary element models. The formulations obtained can be readily used for the design of both floating piles on homogeneous half‐space and end‐bearing piles and are applicable for a wide range of mechanical and geometrical parameters of the soil and piles, in particular for large pile groups. For the seismic design of a building, the use of the simple formulas rather than a full computational model is shown to induce little error on the evaluation of the response spectra and time histories. Copyright © 2009 John Wiley & Sons, Ltd.     

Calibration of the specific barrier model to Iranian plateau earthquakes and development of physically based attenuation relationships for Iran

Earthquake ground-motion relationships for soil and rock sites in Iran have been developed based on the specific barrier model (SBM) used within the context of the stochastic modeling and calibrated against up-to-date Iranian strong-motion data. A total of 171 strong-motion accelerograms recorded at distances of up to 200 km from 24 earthquakes with moment magnitudes ranging from Mw 5.2 to 7.4 are used to determine the region-specific source parameters of this model. Regression analysis was conducted using the “random effects” methodology that considers both earthquake-to-earthquake (inter-event) variability and within-earthquake (intra-event) variability to effectively handle the problem of weighting observations from different earthquakes. The minimization of the error function in each iteration of the “random effects” procedure was performed using the genetic algorithm method. The residuals are examined against available Iranian strong-motion data to confirm that the model predictions are unbiased and that there are no significant residual trends with distance and magnitude. No evidence of self-similarity breakdown is observed between the source radius and its seismic moment. To verify the robustness of the results, tests were performed to confirm that the results are unchanged if the number of observations is changed by removing different randomly selected datasets from the original database. Stochastic simulations, using the derived SBM, are then performed to predict peak ground-motion and response spectra parameters for a wide range of magnitudes and distances. The stochastic SBM predictions agree well with the new empirical regression equations proposed for Iran, Europe and Middle East in the magnitude–distance ranges well represented by the data. It has been shown that the SBM of this study provides unbiased ground-motion estimates over the entire frequency range of most engineering interests (1–10 Hz) for the Iranian earthquakes. Our results are also important for the assessment of hazards in other seismically active environments in the Middle East and Mediterranean regions.     

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

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.     

Application of multivariate statistical methods to indicate the origin and geochemical behavior of potentially hazardous elements in sediment around the Sarcheshmeh copper mine,SE Iran

Multivariate statistical techniques, i.e., correlation coefficient analysis, principal components analysis (PCA), and hierarchical cluster analysis (CA), were applied to the total and water-soluble concentrations of potentially hazardous metals in sediments associated with the Sarcheshmeh mine, one of the largest Oligo-Miocene porphyry copper deposits in the world. The samples were analyzed for hazardous metal concentration levels by inductively coupled plasma mass spectrometry method. Results indicate that the contaminant metals As, Cd, Cu, Mo, S, Sb, Sn, Se, Pb, and Zn were positively correlated with the total concentrations. These hazardous metals also have strong association in the PCA and CA results. Different anthropic versus natural sources of contaminant metals were distinguished by using CA method. Water-soluble fraction of hazardous metals showed that the hydro-geochemical behavior of these metals in sediments is different considerably. Elements such as Cd, Co, Cr, Cu, Fe, Mn, Ni, S, and Zn are readily water soluble from contaminated samples, especially from evaporative mineral phases, while the release of As, Mo, Sb, and Pb into the water is limited by adsorption processes. Results obtained from the application of multivariate techniques on the water-soluble fraction data set show that the hazardous metals are categorized into three groups including (1) Ni, S, Co, Cu, Cr, and Fe; (2) Se, Mn, Cd, and Zn; and (3) Sb, As, Mo, and Sn. This classification describes the hydro-geochemical behavior of hazardous metals in water–sediment environments of the Sarcheshmeh porphyry copper mine and can be used as a basis in remedial and treatment strategies.     

Strong ground motion record selection for the reliable prediction of the mean seismic collapse capacity of a structure group

How to select a limited number of strong ground motion records (SGMRs) is an important challenge for the seismic collapse capacity assessment of structures. The collapse capacity is considered as the ground motion intensity measure corresponding to the drift‐related dynamic instability in the structural system. The goal of this paper is to select, from a general set of SGMRs, a small number of subsets such that each can be used for the reliable prediction of the mean collapse capacity of a particular group of structures, i.e. of single degree‐of‐freedom systems with a typical behaviour range. In order to achieve this goal, multivariate statistical analysis is first applied, to determine what degree of similarity exists between each selected small subset and the general set of SGMRs. Principal Component analysis is applied to identify the best way to group structures, resulting in a minimum number of SGMRs in a proposed subset. The structures were classified into six groups, and for each group a subset of eight SGMRs has been proposed. The methodology has been validated by analysing a first‐mode‐dominated three‐storey‐reinforced concrete structure by means of the proposed subsets, as well as the general set of SGMRs. The results of this analysis show that the mean seismic collapse capacity can be predicted by the proposed subsets with less dispersion than by the recently developed improved approach, which is based on scaling the response spectra of the records to match the conditional mean spectrum. Copyright © 2010 John Wiley & Sons, Ltd.     

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.     

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

     

Standard thermodynamic data for Rhodochrosite from equilibrium decomposition curve

Hydrothermal equilibrium decomposition curve for MnCO₃⇌MnO + CO₂ in the total CO₂ pressure range of 100–1700 bars and temperature range of 500–800°C was studied. The standard thermodynamic data obtained are: ΔH⁰ _f= − 894.382 ± 0.74 kj/mol and ΔG⁰ _f = − 822.170 ± 0.74 kj/mol. These values are more negative than the reported calorimetric data.