Estimates of changes in design rainfall values for Canada

Annual maximum rainfall data from 51 stations in Canada were analyzed for trends and changes by using the Mann–Kendall trend test and a bootstrap resampling approach, respectively. Rainfall data were analyzed for nine durations ranging from 5 min to 24 h. The data analyzed are typically used in the development of intensity‐duration‐frequency (IDF) curves, which are used for estimating design rainfall values that form an input for the design of critical water infrastructure. The results reveal more increasing than decreasing trends and changes in the data with more increasing changes and larger changes, noted for the longer rainfall durations. The results also indicate that a traditional trend test may not be sufficient when the interest is in identifying changes in design rainfall quantiles. Copyright © 2012 John Wiley & Sons, Ltd.     

Role of construction of large dams on river morphology (case study: the Karkheh dam in Iran)

Unfortunately in developing countries, human activities without attention to the environmental aspects damage to nature. For example, construction of large dams dries wetlands in southwest of Iran. Also, construction of dams is a very important factor for morphological changes. The case study of this research is downstream of the Karkheh dam. This dam is the largest Earth dam in Iran and locates in southwest of Iran. It was constructed in 1999. Small sinuosity coefficient of zone near to dam has been illustrated by satellite images in 2002 and 2014. This subject shows instability of this part of reach; also, the results of Shulits equation illustrate that this part is instable. But, increasing of morphological characteristics (sinuosity coefficient, central angle, and relative radius) show that stability of this reach is increasing. Surveying confirm this matter and shows that severe erosion in upstream and sedimentation in downstream of reach is finishing. Also, it is observed that flow discharge reduced (?56 %) after construction of dam and average width of river reduced (?21 %) from 2002 to 2014. In the early years after the construction of the dam, sediment discharge reduced considerably (?14 %) from Pay-e-Pol to Abdol Khan hydrometric stations. Increasing of morphological characteristics (especially at zones near to dam) and decreasing changes of bed level (sedimentation and erosion) prove that effects of dam are overcoming on effects of other factors, and these effects are permanent in long term.     

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R ²), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R ² values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R ² values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model.     

Smooth particle hydrodynamics studies of wet granular column collapses

Acta Geotechnica - Dry granular materials have been the subject of many investigations, while wet granular materials, which widely exist in many real-world applications, have only received limited...     

An Assessment of Changes in Winter Cold and Warm Spells over Canada

The recent Third Assessment Report (TAR) of the Intergovernmental Panel onClimate Change (IPCC) indicated that observed 20th century changes in severalclimatic extremes are qualitatively consistent with those expected due to increasedgreenhouse gases. However, a lack of adequate data and analyses make conclusiveevidence of changing extremes somewhat difficult, particularly, in a global sense.In Canada, extreme temperature events, especially those during winter, can havemany adverse environmental and economic impacts. In light of the aforementionedIPCC report, the main focus of this analysis is to examine observed trends andvariability in the frequency, duration, and intensity of winter (Jan–Feb–Mar) cold and warm spells over Canada during the second half of the 20th century.Cold spell trends display substantial spatial variability across the country. From1950–1998, western Canada has experienced decreases in the frequency, duration, and intensity of cold spells, while in the east, distinct increases in the frequency and duration have occurred. These increases are likely associated with morefrequent occurrences of the positive phase of the North Atlantic Oscillation (NAO)during the last several decades. With regard to winter warm spells, significantincreases in both the frequency and duration of these episodes were observedacross most of Canada. One exception was found in the extreme northeasternregions, where warm spells are becoming shorter and less frequent. The resultsof this study are discussed within the context of climate warming expectations.     

Incorporation of chitosan and glass substrate for improvement in adsorption,separation, and stability of TiO<Subscript>2</Subscript> photodegradation

It is demonstrated that single titanium dioxide (TiO₂) has high potential for photodegradation of pollutants. However, it is still far from becoming an effective photocatalyst system, due to issues of adsorption process, separation, as well as dissolution. Therefore, this study highlights the high adsorption capacity, simplified separation, and the promising stability of TiO_2(SY) (synthesized via sol–gel method) photocatalyst, fabricated using chitosan–TiO_2(SY) and supported by glass substrate (Cs–TiO_2(SY)/glass substrate) photocatalysts. Chitosan (Cs), with abundant –R–NH and NH₂ groups, promotes the adsorption sites of methyl orange (MO) and OH groups for major attachment to TiO_2(SY). Meanwhile, the glass substrate increases stability and assists separation of the photocatalysts. Initially, nano-TiO_2(SY) has been characterized using high-resolution transmission electron microscope. Cs–TiO_2(SY)/glass substrate was fabricated via dip-coating. The distribution and interface between the photocatalytic components were characterized by Fourier transform infrared absorption spectroscopy, UV–Vis diffuse reflectance spectroscopy, field emission scanning electron microscopy, and energy-dispersive spectrometer. UV–Vis analysis of the multilayer photocatalyst (2, 4, 6, and 8 layers) was further carried out by the adsorption–photodegradation, with MO as model of pollutant. Seventy percent of the total removal of MO via optimized eight layers of photocatalyst was achieved within 1 h of UV irradiation. The adsorption photocatalyst achieved 50 % with no exposure to UV light for 15 min of irradiation. It is concluded that suitable photocatalytic conditions and sample parameters possessing the multilayer photocatalyst of Cs–TiO_2(SY) are beneficial toward the adsorption–photodegradation process in wastewater treatment.     

Correlations between microbial indicators, pathogens, and environmental factors in a subtropical Estuary

The objective of this study was to evaluate whether indicator microbes and physical–chemical parameters were correlated with pathogens within a tidally influenced Estuary. Measurements included the analysis of physical–chemical parameters (pH, salinity, temperature, and turbidity), measurements of bacterial indicators (enterococci, fecal coliform, Escherichia coli, and total coliform), viral indicators (somatic and MS2 coliphage), viral pathogens (enterovirus by culture), and protozoan pathogens (Cryptosporidium and Giardia). All pathogen results were negative with the exception of one sample which tested positive for culturable reovirus (8.5 MPN/100 L). Notable physical–chemical parameters for this sample included low salinity (<1 ppt) and high water temperature (31 °C). Indicator bacteria and indicator virus levels for this sample were within average values typically measured within the study site and were low in comparison with levels observed in other freshwater environments. Overall results suggest that high levels of bacterial and viral indicators were associated with low salinity sites.     

Deriving snow‐cover depletion curves for different spatial scales from remote sensing and snow telemetry data

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow‐covered area (SCA) once snow‐free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate‐resolution imaging spectroradiometer images to produce snow‐cover depletion curves. The snow depletion curves were created for an 80 000 km² domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow‐cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter‐annual consistency. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper (TMD) can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom (MDF) systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.