Conceptual study of X-braced frames with different steel grades using cyclic half-scale tests

In this paper,an experimental and analytical study of two half-scale steel X-braced frames with equal nominal shear strength under cyclic loading is described.In these tests,all members except the braces are similar.The braces are made of various steel grades to monitor the effects of seismic excitation.Internal stiffeners are employed to limit the local buckling and increase the fracture life of the steel bracing.A heavy central core is introduced at the intersection of the braces to decrease their effective length.Recent seismic specifications are considered in the design of the X-braced frame members to verify their efficiency.The failure modes of the X-braced frames are also illustrated.It is observed that the energy dissipation capacity,ultimate load capacity and ductility of the system increase considerably by using lower grade steel and proposed detailing.Analytical modeling of the specimens using nonlinear finite element software supports the experimental findings.     

Application of GA in optimization of pore network models generated by multi-cellular growth algorithms

In pore network modeling, the void space of a rock sample is represented at the microscopic scale by a network of pores connected by throats. Construction of a reasonable representation of the geometry and topology of the pore space will lead to a reliable prediction of the properties of porous media. Recently, the theory of multi-cellular growth (or L-systems) has been used as a flexible tool for generation of pore network models which do not require any special information such as 2D SEM or 3D pore space images. In general, the networks generated by this method are irregular pore network models which are inherently closer to the complicated nature of the porous media rather than regular lattice networks. In this approach, the construction process is controlled only by the production rules that govern the development process of the network. In this study, genetic algorithm has been used to obtain the optimum values of the uncertain parameters of these production rules to build an appropriate irregular lattice network capable of the prediction of both static and hydraulic information of the target porous medium.     

Pollution assessment and source apportionment of arsenic,lead and copper in selected soils of Khuzestan Province,southwestern Iran

Due to the intensified industrial activities and excessive application of agrochemicals and organic waste materials over the last few decades, there is a great concern about the accumulation of potentially toxic elements (PTEs) in soils from north of Khuzestan Province, southwestern Iran. Therefore, a comparative study with a total number of 300 composite soil samples (0–10 cm) from industrial, urban, agricultural, forest, and rangelands; and 26 samples from the major types of soils parent materials was conducted to examine sources, pollution status, and the effects of soil properties, land use types, and the local lithology on the total concentrations of As, Pb, and Cu (measured using atomic absorption spectrometer (AAS) equipped with graphite furnace) in the soils studied. The mean values of Pb and Cu were 12.2?±?4.6 and 13.5?±?7.6 mg kg^?1, respectively, slightly higher than the background values of the study area, but lower than the guideline values of Iranian Environmental Quality Standard for Soils. However, the mean values of As (1.72?±?1.15 mg kg^?1) were lower than both background values and the guideline values of Iranian Environmental Quality Standard for soils. The greatest values of the geo-accumulation index (I _geo), enrichment factor (EF), and the concentrations of Pb, Cu, and As were arranged as industrial > urban > agriculture > rangelands = forest land uses. The results also indicated that concentrations of all PTEs were greater in soils as compared to those in parent materials. Using principal component analysis (PCA), the origin of Cu and Pb with moderate to high enrichments was attributed to the inputs from both natural and anthropogenic sources. However, As was found to be mainly influenced by lithogenic origin.     

Removal of acetaminophen from hospital wastewater using electro-Fenton process

The current work deals with efficient removal of acetaminophen (AC) from hospital wastewater using electro-Fenton (EF) process. The degradation yield of 99.5% was obtained under optimal experimental conditions, namely 5.75 mg L^?1 initial AC concentration, 2.75 pH solution, 3-cm inter-electrode distance, 100 mg L^?1 KCl electrolyte, 122.5 µL L^?1 H₂O₂, 8 mA cm^?2 current density at equilibrium time of 8 min. Analysis of variance (ANOVA) suggested that the effect of mentioned operating parameters was statistically significant on the AC removal. The low probability amount of P value (P < 0.0001), the Fisher’s F-value of 65.91, and correlation coefficient of the model (R² = 0.9545) revealed a satisfactory correlation between the experimental and the predicted values of AC removal. The predicted removal efficiency of 99.4% was in satisfactory agreement with the obtained experimental removal efficiency of 98.7%. The AC degradation during the EF followed a first-order kinetic model with rate constants (K_app) of 0.6718 min^?1. Using the ordinary radical scavengers revealed that main mechanism of AC degradation controlled by the hydroxyl free radicals produced throughout the EF process. The excess amount of iron (II) scavenged the active radicals and diminished the concentration of ^·OH available to react with AC. The optimum molar ratio of H₂O₂ to Fe²⁺ was found to be 2.5. The developed EF process as a promising technique applied for treatment of real samples.     

Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS

This paper presents an urban growth boundary model (UGBM) which utilizes spatial logistic regression (SLR), remote sensing, and GIS to simulate the differentially expanding geometry of a dynamic urban boundary over decadal time periods. SLR is used as the core algorithm in a UGBM quantifying how biophysical factors influence the rate at which all edges of an urban boundary expand over time. Spatial drivers selected from a raster-based environment are used as input predictor variables to the SLR UGBM, the output response variable being the distance between time-separated urban boundary intersections along arcs extending radially from a point centered at the urban core relative to the maximum distance. Percent area match (PAM) quantity and location goodness-of-fit metrics, fit of the predicted distance versus observed distance, and the sensitivity of the SLR UGBM to the contribution of each predictor variable are used to assess the agreement between predicted and observed urban boundaries. The model is built, tested, and validated using satellite images of the city of Las Vegas, United States of America, collected in 1990, 2000, and 2010. We compare urban boundary simulation of full and reduced SLR UGBMs to a null UGBM lacking in specificity of predictor variables. Results indicate that the SLR UGBM has a better goodness of fit compared to a null UGBM using PAM quantity and location goodness-of-fit metrics. Then, we use the SLR UGBM to predict urban boundary expansion between the years 2000 and 2010 and describe how this model can be used to plan ahead for future boundary expansions given what is known about current edge conditions.     

Trends in hydrological and climatic variables affected by four variations of the Mann-Kendall approach in Urmia Lake basin,Iran

ABSTRACT

The trends in hydrological and climatic time series data of Urmia Lake basin in Iran were examined using the four different versions of the Mann-Kendall (MK) approach: (i) the original MK test; (ii) the MK test considering the effect of lag-1 autocorrelation; (iii) the MK test considering the effect of all autocorrelation or sample size; and (iv) the MK test considering the Hurst coefficient. Identification of hydrological and climatic data trends was carried out at monthly and annual time scales for 25 temperature, 35 precipitation and 35 streamflow gauging stations selected from the Urmia Lake basin. Mann-Kendall and Pearson tests were also applied to explore the relationships between temperature, precipitation and streamflow trends. The results show statistically significant upward and downward trends in the annual and monthly hydrological and climatic variables. The upward trends in temperature, unlike streamflow, are much more pronounced than the downward trends, but for precipitation the behaviour of trend is different on monthly and annual time scales. Furthermore, the trend results were affected by the different approaches. Specifically, the number of stations showing trends in hydrological and climatic variables decreased significantly (up to 50%) when the fourth test was considered instead of the first and the absolute value of the Z statistic for most of the time series was reduced. The results of correlations between streamflow and climatic variables showed that the streamflow in Urmia Lake basin is more sensitive to changes in temperature than those of precipitation. The observed decreases in streamflow and increases in temperature in the Urmia Lake basin in recent decades may thus have serious implications for water resources management under the warming climate with the expected population growth and increased freshwater consumption in this region.

Editor Z. W. Kundzewicz; Associate editor Q. Zhang     

Estimating width of the stable channels using multivariable mathematical models

Predicting behavior and the geometry of the channels and alluvial rivers in which the erosion and sediment transport are in equilibrium is one of the most important topics in river engineering. Various researchers have proposed empirical equations to estimate stable river width (W). In this research, empirical equations were examined and tested with a comprehensive available data set consisting of 1644 points collected from 29 stable rivers in various parts of the world. The data set covers a wide range of flow conditions, river geometry, and bed sediments. This data set is classified in two groups (W < 600 m and W ≥ 600 m) for presenting the new models. The new linear and nonlinear multivariable equations were fitted to these two groups, and the best models were selected by preliminary tests and diagnostic determined for each group. The determination coefficient of these models ranged from 0.87 to 0.96. The results show that the models presented in this paper are more accurate with respect to the previously presented models. In the second part, “Artificial neural networks,” perceptron was used and a new methodology for estimating stable channel width was developed. Comparison of the statistical methods presented in this paper and the results of perceptron neural network revealed preferential recent method.     

Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran

Regional flood frequency analysis (RFFA) was carried out on data for 55 hydrometric stations in Namak Lake basin, Iran, for the period 1992–2012. Flood discharge of specific return periods was computed based on the log Pearson Type III distribution, selected as the best regional distribution. Independent variables, including physiographic, meteorological, geological and land-use variables, were derived and, using three strategies – gamma test (GT), GT plus classification and expert opinion – the best input combination was selected. To select the best technique for regionalization, support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and nonlinear regression (NLR) techniques were applied to predict peak flood discharge for 2-, 5-, 10-, 25-, 50- and 100-year return periods. The GT + ANFIS and GT + SVR models gave better performance than the ANN and NLR models in the RFFA. The results of the input variable selection showed that the GT technique improved the model performance.     

Probabilistic seismic loss estimation via endurance time method

Probabilistic Seismic Loss Estimation is a methodology used as a quantitative and explicit expression of the performance of buildings using terms that address the interests of both owners and insurance companies. Applying the ATC 58 approach for seismic loss assessment of buildings requires using Incremental Dynamic Analysis (IDA), which needs hundreds of time-consuming analyses, which in turn hinders its wide application. The Endurance Time Method (ETM) is proposed herein as part of a demand propagation prediction procedure and is shown to be an economical alternative to IDA. Various scenarios were considered to achieve this purpose and their appropriateness has been evaluated using statistical methods. The most precise and efficient scenario was validated through comparison against IDA driven response predictions of 34 code conforming benchmark structures and was proven to be sufficiently precise while offering a great deal of efficiency. The loss values were estimated by replacing IDA with the proposed ETM-based procedure in the ATC 58 procedure and it was found that these values suffer from varying inaccuracies, which were attributed to the discretized nature of damage and loss prediction functions provided by ATC 58.     

Regionalization of watersheds based on the concept of rough set

Natural Hazards - In this study, an algorithm inspired by some concepts of the rough set theory is proposed for regionalization of watersheds. The algorithm includes a clustering step and a...