Assessing flow paths in a karst aquifer based on multiple dye tracing tests using stochastic simulation and the MODFLOW-CFP code

Karst systems show high spatial variability of hydraulic parameters over small distances and this makes their modeling a difficult task with several uncertainties. Interconnections of fractures have a major role on the transport of groundwater, but many of the stochastic methods in use do not have the capability to reproduce these complex structures. A methodology is presented for the quantification of tortuosity using the single normal equation simulation (SNESIM) algorithm and a groundwater flow model. A training image was produced based on the statistical parameters of fractures and then used in the simulation process. The SNESIM algorithm was used to generate 75 realizations of the four classes of fractures in a karst aquifer in Iran. The results from six dye tracing tests were used to assign hydraulic conductivity values to each class of fractures. In the next step, the MODFLOW-CFP and MODPATH codes were consecutively implemented to compute the groundwater flow paths. The 9,000 flow paths obtained from the MODPATH code were further analyzed to calculate the tortuosity factor. Finally, the hydraulic conductivity values calculated from the dye tracing experiments were refined using the actual flow paths of groundwater. The key outcomes of this research are: (1) a methodology for the quantification of tortuosity; (2) hydraulic conductivities, that are incorrectly estimated (biased low) with empirical equations that assume Darcian (laminar) flow with parallel rather than tortuous streamlines; and (3) an understanding of the scale-dependence and non-normal distributions of tortuosity.     

Biosorption of 4-chlorophenol by dried anaerobic digested sludge: artificial neural network modeling,equilibrium isotherm,and kinetic study

In this study, dried anaerobic digested sludge (DADS) was utilized to remove 4-chlorophenol (4-CP) from aqueous solutions. Batch biosorption experiments were carried out to investigate the effects of physicochemical parameters such as pH, contact time, biosorbent dosage, and initial concentration. Artificial neural network (ANN) was then used to predict the removal efficiency of the process. The comparison between predicted and experimental results provided a high degree of determination coefficient (R² = 0.98), indicating that the model could predict the biosorption efficiency with reasonable accuracy. Biosorption data were successfully described by the Freundlich isotherm and pseudofirst-order model. The Weber–Morris kinetic model indicated that intraparticle diffusion was not the only rate-controlling step, and other mechanisms may be involved in the biosorption process. The optimum pH was detected to be 3 for DADS. By increasing contact time and biosorbent dosage, the removal efficiency of 4-CP increased. Also, a decreasing trend was observed when initial concentrations were increased. The findings suggested that the results predicted by ANN are very close to the experimental values, and DADS as an available adsorbent can efficiently remove 4-CP from aqueous solutions.     

An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios

Remotely sensed image analysis using spectral-spatial information plays a key role in modern remote sensing applications. This article presents a new semi-automatic framework for spectral-spatial classification of hyperspectral images. The proposed framework benefits from a combination of pixel-based and object-based classification scenarios in which the main parameters are adaptively tuned. In order to reduce the complexity of the method, an unsupervised band selection technique is used as well. Meanwhile, the wavelet thresholding is applied in order to smooth the selected bands. The classification results after applying the proposed method to well-known standard hyperspectral datasets are better than those of the most of the other state-of-the-art approaches. As an example, the overall classification accuracy achieved by applying the proposed semi-automatic spectral-spatial classification framework to the Salinas dataset is more than 99% for 10% training samples per class. Moreover, the vital parameters are adaptively set in our approach.     

Geologic hazards along part of Al-sayl–Alkabeir Al-Jammum road in Saudi Arabia

 The geological hazards along part of Al-Sayl Alkabeir Al-Jammum road, western Saudi Arabia, were studied by the use of the sterographic projection to define the types of possible failures and the magnitude of safety factor on each slope face. The studied area consists of granite and granodiorite rocks that were cut by acidic and basic dikes. There are four sets of structural discontinuities which, besides a few strike slip faults, constitute the planes of weakness that lead to a variety of possible mode failures. The stability analysis indicates that sections of the road are unstable as their cut slopes exceed the maximum safe slope angles which range between 65° and 73°. Received: 16 June 1998 · Accepted: 2 March 1999     

Effect of inertia nonlinearity on dynamic response of an asymmetric building equipped with tuned mass dampers

This study investigates the effect of nonlinear inertia on the dynamic response of an asymmetric building equipped with Tuned Mass Dampers (TMDs). In the field of structural engineering, many researchers have developed models to study the behavior of nonlinear TMDs, but the effect of nonlinear inertia has not received as much attention for asymmetric buildings. To consider nonlinear inertia, the equations of motion are derived in a local rotary coordinates system. The displacements and rotations of the modeled building and TMDs are defined by five-degree-of-freedom (5-DOFs). The equations of motion are derived by using the Lagrangian method. Also in the proposed nonlinear model, the equations of motion are different from a conventional linear model. In order to compare the response of the proposed nonlinear model and a conventional linear model, numerical examples are presented and the response of the modeled buildings are derived under harmonic and earthquake excitations. It is shown that if the nonlinear inertia is considered, the response of the modeled structures changes and the conventional linear approach cannot adequately model the dynamic behavior of the asymmetric buildings which are equipped with TMDs.     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

A practical approach for seismic risk assessment of underground structures: A case study of Iranian subway tunnels

Active geological and young faulted zones have made Iran’s territory one of the most seismological active areas in the world according to recent historical earthquakes. Some of the deadliest earthquakes such as Gilan 1990 and Kermanshah 2018 caused tens of thousands fatalities. If such violent earthquakes affect strategical structures of a country, indirect losses would be more concerning than direct losses. Nowadays there is no doubt about the vital role of tunnels and underground structures in urban areas. These facilities serve as nonstop functional structures for human transportation, water and sewage systems and underground pedestrian ways. Any external hazard subjected to underground spaces, such as earthquake could directly affect passenger’s lives and significantly decrease whole system reliability of public transportation. Commonly two earthquake levels of intensities, Maximum Design Earthquake (MDE) and Operating Design Earthquake (ODE) were used in seismic design of underground structures. However, uncertain nature of earthquakes in terms of frequency content, duration of strong ground motion, and level of intensity indicate that only the two levels of earthquake (ODE and MDE) cannot cover the all range of possible seismic responses of structures during a probable earthquake. It is important to evaluate the behavior of tunnel under a wide range of earthquake intensities. For this purpose, a practical risk-based approach which is obtained using the total probability rule was used. This study illustrates a framework for evaluation seismic stability of tunnels. Urban railway tunnels of Tehran, Shiraz, Ahwaz, Mashhad, Isfahan and Tabriz were considered as study cases. Nominal value of seismic risk for three main damage states, including minor, moderate and major were calculated.     

Simulation of debris flow on an instrumented test slope using an updated Lagrangian continuum particle method

We present an updated Lagrangian continuum particle method based on smoothed particle hydrodynamics (SPH) for simulating debris flow on an instrumented test slope. The site is a deforested area near the village of Ruedlingen, a community in the canton of Schaffhausen in Switzerland. Artificial rainfall experiments were conducted on the slope that led to failure of the sediment in the form of a debris flow. We develop a 3D mechanistic model for this test slope and conduct numerical simulations of the flow kinematics using an SPH formulation that captures large deformation, material nonlinearity, and the complex post-failure movement of the sediment. Two main simulations explore the impact of changes in the mechanical properties of the sediment on the ensuing kinematics of the flow. The first simulation models the sediment as a granular homogeneous material, while the second simulation models the sediment as a heterogeneous material with spatially varying cohesion. The variable cohesion is meant to represent the effects of root reinforcement from vegetation. By comparing the numerical solutions with the observed failure surfaces and final free-surface geometries of the debris deposit, as well as with the observed flow velocity, flow duration, and hot spots of strain concentration, we provide insights into the accuracy and robustness of the SPH framework for modeling debris flows.

     

Evaluation of the effects of uncertainty on the predictions of landslide occurrences using the Shannon entropy theory and Dempster–Shafer theory

Natural Hazards - One of the requirements for planning and decision-making to develop the infrastructures is to prepare the landslide occurrence hazard map. For this purpose, in this article, the...