Laboratory Estimation of Rock Joint Stiffness and Frictional Parameters

Numerical modeling of complex rock engineering problems involves the use of various input parameters which control usefulness of the output results. Hence, it is of utmost importance to select the right range of input physical and mechanical parameters based on laboratory or field estimation, and engineering judgment. Joint normal and shear stiffnesses are two popular input parameters to describe discontinuities in rock, which do not have specific guidelines for their estimation in literature. This study attempts to provide simple methods to estimate joint normal and shear stiffnesses in the laboratory using the uniaxial compression and small-scale direct shear tests. Samples have been prepared using rocks procured from different depths, geographical locations and formations. The study uses a mixture of relatively smooth natural joints and saw-cut joints in the various rock samples tested. The results indicate acceptable levels of uncertainty in the calculation of the stiffness parameters and provide a database of good first estimates and empirical relations which can be used for calculating values for joint stiffnesses when laboratory estimation is not possible. Joint basic friction angles have also been estimated as by-products in the small scale direct shear tests.     

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.     

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

Stochastic Environmental Research and Risk Assessment - Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely...     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Dynamics of hourly sea level at Hillarys Boat Harbour,Western Australia: a chaos theory perspective

Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed: low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another, the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual insights provided by each model.     

On the role of transition region on the Alfvén wave phase mixing in solar spicules

Alfvénic waves are thought to play an important role in coronal heating and solar wind acceleration. Here we investigate the dissipation of standing Alfvén waves due to phase mixing at the presence of steady flow and sheared magnetic field in the stratified atmosphere of solar spicules. The transition region between chromosphere and corona has also been considered. The initial flow is assumed to be directed along spicule axis, and the equilibrium magnetic field is taken 2-dimensional and divergence-free. It is determined that in contrast to propagating Alfvén waves, standing Alfvén waves dissipate in time rather than in space. Density gradients and sheared magnetic fields can enhance damping due to phase mixing. Damping times deduced from our numerical calculations are in good agreement with spicule lifetimes. Since spicules are short living and transient structures, such a fast dissipation mechanism is needed to transport their energy to the corona.     

Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation

Rainfall distributions in Iran are spatially and temporally heterogeneous, a fact probably linked to the mostly arid and semi-arid climate of the country. On the other hand, water demand is increasing with increasing population and improving life style. At present, the optimal utilization of water resources and irrigation dams is the primary concern of water resource managers. The Eleviyan dam (with a capacity of 60 hm³) was constructed to meet the irrigation and municipal water needs of the Maraghan region (Northwestern Iran). In this study, the efficiency of the Eleviyan irrigation dam system was investigated in three phases by setting up the optimization model that maximized the water release for irrigation purposes after municipal water need were met. In the first phase, the inflows measured in the 21 years prior to the construction of the reservoir, and in the second, the inflows generated by the Monte Carlo simulation method, and in the third phase, the inflows after the construction of the reservoir were used. The results demonstrate that the capacity determined during the preliminary studies was accurate and the operation carried out in the recent periods of operation life was up to a satisfactory standard.     

The best available technology for small electroplating plants applying analytical hierarchy process

In this study analytical hierarchy process approach which uses expert’s knowledge, was applied for selection of the best wastewater treatment alternative for electroplating workshops, located in Tehran province, Iran. This method can be applied for complicated multi-criteria decision making to obtain scientific and reasonable results. Four wastewater treatment alternatives including reverse osmosis, nano filtration, ion exchange, and chemical precipitation were evaluated and ranked based on economical, technical, environmental/ social aspects. Each criterion includes different indices such as land area, capital cost, sludge disposal issues, operating and maintenance, local suppliers and warranty, system flexibility, required skilled and non skilled man power safety, and etc. The results showed that reverse osmosis achieved the maximum general profits and can be the best choice. Sensitivity analysis can shows the effect of inputs parameters change on the results. Sensitivity analysis was applied for each criterion. The results illustrated that for economic and technical criterion, chemical precipitation and for environment criterion, reverse osmosis were qualified.     

Beginning of melting and composition of first melts in the system Qz-Ab-Or-H2O-CO2

Solidus temperatures have been determined for minimum melt compositions in the system Qz(SiO₂)-Ab(NaAlSi₃O₈)-Or(KAlSi₃O₈) at P(fluid)=2,5 and 10 kbar and at various water activities. The dry solidus was investigated in a dry argon environment. Water activities (aH₂O) between 0.0 and 1.0 were obtained by using H₂O-CO₂ mixtures. The Or/Ab+Or ratio of first melts increases considerably with decreasing water activity. At 10 kbar it is 0.28 in the water-saturated system and 0.56 at water activity 0.1. The Qz-content does not change with changing water activities. The Ab-content of minimum melts formed at high pressures and low aH₂O may remain almost constant in ascending magmas that are cooling and crystallizing. Qz-content increases at the expense of the Or-component. Solidus temperatures decrease considerably when aH₂O increases slightly from zero. At 10 kbar, the temperature difference between dry melting and the solidus for aH₂O=0.1 is 120°C. The influence of pure CO₂ on the solidus is very limited in the investigated P-T range. The solidus is approximatively 760°C at aH₂O=0.5 between 2 and 10 kbar and approximatively 830°C at aH₂O=0.3. This means that melting of quartz-feldspar assemblages may induce dehydration reactions at P-T conditions of the granulite facies.