A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

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.     

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.     

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.     

Compounding effects of human activities and climatic changes on surface water availability in Iran

Climatic Change - By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change...     

Sedimentological,Geochemical and Hydrogeochemical Studies of Alluvial Fans for Mineral and Environmental Purposes (Case Study of Southwestern Iran)

Lithology and Mineral Resources - The alluvial-fan sediments play a very important role in mineral reserves and underground water resources, though a comprehensive study on such sediments,...     

Conditioning DRASTIC model to simulate nitrate pollution case study: Hamadan–Bahar plain

One of the major causes of groundwater pollution in Hamadan–Bahar aquifer in western Iran is a non-point source pollution resulting from agricultural activities. Withdrawal of over 88% of drinking water from groundwater resources, adds urgency to the studies leading to a better management of water supplies in this region. In this study, the DRASTIC model was used to construct groundwater vulnerability maps based on the “intrinsic” (natural conditions) and “specific” (including management) concepts. As DRASTIC has drawbacks to simulate specific contaminants, we conditioned the rates on measured nitrate data and optimized the weights of the specific model to obtain a nitrate vulnerability map for the region. The performance of the conditioned DRASTIC model improved significantly (R ² = 0.52) over the intrinsic (R ² = 0.12) and specific (R ² = 0.19) models in predicting the groundwater nitrate concentration. Our study suggests that a locally conditioned DRASTIC model is an effective tool for predicting the region’s vulnerability to nitrate pollution. In addition, comparison of groundwater tables between two periods 30 years apart indicated a drawdown of around 50 m in the central plain of the Hamadan–Bahar region. Our interpretation of the vulnerability maps for the two periods showed a polluted zone developing in the central valley requiring careful evaluation and monitoring.     

Short communication: Engineering properties of wetland plants with application to wave attenuation

This technical note presents empirically-derived values for biophysical attributes of several commonly occurring wetland plant species, including plant stem diameter and tapering, plant clump and stem spacing statistics, biomass, Young's modulus of elasticity, and bending strength. These parameters can be used to more realistically configure plant canopies in numerical and laboratory studies to further our understanding of wave attenuation by wetlands.     

Contribution of tuned liquid column gas dampers to the performance of offshore wind turbines under wind,wave, and seismic excitations

The main intention of the present study is to reduce wind, wave, and seismic induced vibrations of jackettype offshore wind turbines (JOWTs) through a newly developed vibration absorber, called tuned liquid column gas damper (TLCGD). Using a Simulink-based model, an analytical model is developed to simulate global behavior of JOWTs under different dynamic excitations. The study is followed by a parametric study to explore efficiency of the TLCGD in terms of nacelle acceleration reduction under wind, wave, and earthquake loads. Study results indicate that optimum frequency of the TLCGD is rather insensitive to excitation type. In addition, while the gain in vibration control from TLCGDs with higher mass ratios is generally more pronounced, heavy TLCGDs are more sensitive to their tuned frequency such that ill-regulated TLCGD with high mass ratio can lead to destructive results. It is revealed that a well regulated TLCGD has noticeable contribution to the dynamic response of the JOWT under any excitation.