Effects of pulse period of near‐field ground motions on the seismic demands of soil–MDOF structure systems using mathematical pulse models

In this paper, the effects of pulse period associated with near‐field ground motions on the seismic demands of soil–MDOF structure systems are investigated by using mathematical pulse models. Three non‐dimensional parameters are employed as the crucial parameters, which govern the responses of soil–structure systems: (1) non‐dimensional frequency as the structure‐to‐soil stiffness ratio; (2) aspect ratio of the superstructure; and (3) structural target ductility ratio. The soil beneath the superstructure is simulated on the basis of the Cone model concept. The superstructure is modeled as a nonlinear shear building. Interstory drift ratio is selected as the main engineering demand parameter for soil–structure systems. It is demonstrated that the contribution of higher modes to the response of soil–structure system depends on the pulse‐to‐interacting system period ratio instead of pulse‐to‐fixed‐base structure period ratio. Furthermore, results of the MDOF superstructures demonstrate that increasing structural target ductility ratio results in the first‐mode domination for both fixed‐base structure and soil–structure system. Additionally, increasing non‐dimensional frequency and aspect ratio of the superstructure respectively decrease and increase the structural responses. Moreover, comparison of the equivalent soil–SDOF structure system and the soil–MDOF structure system elucidates that higher‐mode effects are more significant, when soil–structure interaction is taken into account. In general, the effects of fling step and forward directivity pulses on activating higher modes of the superstructure are more sever in soil–structure systems, and in addition, the influences of forward directivity pulses are more considerable than fling step ones. Copyright © 2013 John Wiley & Sons, Ltd.     

Probabilistic Analysis of Ground Surface Vibration Due to Train Movement, a Case Study on Tehran Metro Line 4

Ground surface vibration produced by moving train is one of the most important aspects in urban areas. The purpose of this study is the probabilistic analysis of ground surface vibration, which is generated by metro transportations. For this reason, Tehran metro line 4 is considered as a case study. In this paper, at first, a new procedure is used to simulate train dynamic load. In the second step, based on the variation of geomechanical properties and train characteristics in Tehran metro line 4, more than 60 numerical models are simulated. The results of numerical simulations are analyzed by multivariate statistical technique and an equation for prediction of peak particle velocity (PPV) in the ground surface is presented. In the next step, probabilistic analysis is done using Monte Carlo Simulation (MCS). Finally, sensitivity of input data on ground surface vibration is discussed and the impact of geomechanical properties and train characteristics on the surface vibration is considered. Based on the probabilistic analysis, PPV in the surface region of Tehran metro line 4 is <2.76?mm/s with 95?% probability.     

Investigation of lake drying attributed to climate change

In recent decades, climate change has been of great concern due to its effect on water level and its impact on aquatic ecosystems. Urmia Lake, the largest inland wetland in Iran, has been shrinking. There is a great concern whether it will dry up like the Aral Sea. Therefore, a hydrodynamic model has been developed to simulate the condition of Urmia Lake. The model has been validated using the known annual data on precipitation, evaporation, run off, river discharges and water level which are available for the last 35?years. Different hydrological conditions regarding lake input and output data were tested and water depth was calculated using bathymetry to predict water-level fluctuations in the future. The results predict that the water level will decrease continuously. The lake will be dried up in about 10?years if very dry conditions continue in the region. The drought speed cannot be reduced and there is no potential to develop a water-usage program. Besides, the lake water depth decrease is more slightly, applying alternate wet and dry-period conditions. In some hydrological conditions there is a good potential to consider water development projects. The sensitivity analysis of different parameters indicates that the lake is highly sensitive to river discharges, which implies that the water development project plans will disturb the lake ecosystem if implemented up to 2021 and integrated watershed management plan for the lake can change the condition by regulating the dam output.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Input data selection for solar radiation estimation

Model input data selection is a complicated process, especially for non‐linear dynamic systems. The questions on which inputs should be used and how long the training data should be for model development have been hard to solve in practice. Despite the importance of this subject, there have been insufficient reports in the published literature about inter‐comparison between different model input data selection techniques. In this study, several methods (i.e. the Gamma test, entropy theory, AIC (Akaike's information criterion)/BIC (Bayesian information criterion) have been explored with the aid of non‐linear models of LLR (local linear regression) and ANN (artificial neural networks). The methodology is tested in estimation of solar radiation in the Brue Catchment of England. It has been found that the conventional model selection tools such as AIC/BIC failed to demonstrate their functionality. Although the entropy theory is quite powerful and efficient to compute, it failed to pick up the best input combinations. On the other hand, it is very encouraging to find that the new Gamma test was able to choose the best input selection. However, it is surprising to note that the Gamma test significantly underestimated the required training data while the entropy theory did a better job in this aspect. This is the first study to compare the performance of those techniques for model input selections and still there are many unsolved puzzles. Copyright © 2009 John Wiley & Sons, Ltd.     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.     

Stiffness identification of a tall building during construction period using ambient tests

Ambient response measurements were made on an eighteen-storey building at three different stages of construction to detect any changes in the frequencies, mode shapes and stiffness with construction. The first nine frequencies and corresponding mode shapes for each stage of construction are found. A comparison is made among these mode shapes and frequencies and with the mode shapes and frequencies of an analytical model incorporating beams, columns, shear walls, panels and diagonal elements. The added effects, on frequencies and mode shapes, of non-structural elements such as stairs, elevators, claddings and partition walls are studied. Using Improved Statistical Structural Identification, an attempt is made to study the stiffening effect of non-structural elements by updating the stiffness matrix of the building.     

Evaluation,characterization and analytical application of a new composite material for removing metal ions from wastewater

A new organic/inorganic composite based on polyacrylonitrile and stannic molybdophosphate (PAN–SMP) as an adsorbent was synthesized under various conditions. The physicochemical properties of this material were specified by elemental analysis, scanning electron microscopy, infrared spectroscopy and thermogravimetry studies. The synthesized material was found to be stable in demineralized water, in dilute acids, under gamma radiation up to the total radiated of 100 kGy doses and in high temperature up to 500 °C. Ion exchange capacity of the synthesized composite and its distribution coefficient (K _d) for several metal ions were determined. The results showed that PAN–SMP has a great affinity toward some metal ions such as T^l+, Sr²⁺, Ba²⁺, UO₂ ²⁺ and La⁴⁺. Based on the determined K _d values, two binary quantitative separations of metal ions (Cr⁶⁺ from Cu²⁺ and Pb²⁺ from Cu²⁺) have been achieved on columns of this ion exchanger. The ability of PAN–SMP to decontaminate low-level liquid waste was also investigated.     

A hybrid distinct element–boundary element approach for seismic analysis of cracked concrete gravity dam–reservoir systems

This paper proposes a new algorithm for modeling the nonlinear seismic behavior of fractured concrete gravity dams considering dam–reservoir interaction effects. In this algorithm, the cracked concrete gravity dam is modeled by distinct element (DE) method, which has been widely used for the analysis of blocky media. Dynamic response of the reservoir is obtained using boundary element (BE) method. Formulation and various computational aspects of the proposed staggered hybrid approach are thoroughly discussed. To the authors' knowledge, this is the first study of a hybrid DE–BE approach for seismic analysis of cracked gravity dam–reservoir systems. The validity of the algorithm is discussed by developing a two-dimensional computer code and comparing results obtained from the proposed hybrid DE–BE approach with those reported in the literature. For this purpose, a few problems of seismic excitations in frequency- and time-domains, are presented using the proposed approach. Present results agree well with the results from other numerical methods. Furthermore, the cracked Koyna Dam is analyzed, including dam–reservoir interaction effects with focus on the nonlinear behavior due to its top profile crack. Results of the present study are compared to available results in the literature in which the dam–reservoir interaction were simplified by added masses. It is shown that the nonlinear analysis that includes dam–reservoir interaction gives downstream sliding and rocking response patterns that are somehow different from that of the case when the dam–reservoir interaction is approximated employing added masses.     

High-Nb hawaiite–mugearite and high-Mg calc-alkaline lavas from northeastern Iran: Oligo-Miocene melts from modified mantle wedge

Tertiary volcanic rocks in northwestern Firoozeh, Iran (the Meshkan triangular structural unit), constitute vast outcrops (up to 250 km²) of high-Mg basaltic andesites to dacites that are associated with high-Nb hawaiites and mugearites. Whole-rock ⁴⁰Ar/³⁹Ar ages show a restricted range of 24.1 ± 0.4–22.9 ± 0.5 Ma for the volcanic rocks. The initial ratios of ⁸⁷Sr/⁸⁶Sr and ¹⁴³Nd/¹⁴⁴Nd vary from 0.703800 to 0.704256 and 0.512681 to 0.512877, respectively, in the high-Mg basaltic andesites–dacites. High-Th contents (up to 11 ppm) and Sr/Y values (27–100) and the isotopic composition of the subalkaline high-Mg basaltic andesites–dacites indicate derivation from a mantle modified by slab and sediment partial melts. Evidence such as reverse zoning and resorbed textures and high Ni and Cr contents in the evolved samples indicate that magma mixing with mafic melts and concurrent fractional crystallization lead to the compositional evolution of this series. The high-Nb hawaiites and mugearites, by contrast, have a sodic alkaline affinity and are silica undersaturated; they are also enriched in Nb (up to 47 ppm) and a wide range of incompatible trace elements, including LILE, LREE, and HFSE. Geochemistry and Sr–Nd isotopic compositions of the high-Nb hawaiites and mugearites suggest derivation from a mantle source affected by lower degrees of slab melts. Post-orogenic slab break-off is suggested to have prompted the asthenospheric upwelling that triggered partial melting in mantle metasomatized by slab-derived melts.