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.

     

Development of a model for analysis of slope stability for circular mode failure using genetic algorithm

Slope stability estimation is an engineering problem that involves several parameters. The interactions between factors that affect slope instability are complex and multi-factorial, so often it is difficult to describe the slope stability mathematically. This paper, proposes the use of a genetic algorithm (GA) as a heuristic search method to find a regression model for analyzing the slope stability. For this purpose, an evolutionary algorithm based on GA was used to develop a regression model for prediction of factor of safety (FS) for circular mode failure. The proposed GA uses the root mean squared error as the fitness function and searches among a large number of possible regression models to choose the best for estimation of FS from six geotechnical and geometrical parameters. For validation of the model and checking its efficiency, a validation dataset was used to evaluate FS using the proposed model and a previously developed mathematical GA based model in the literature. Results have shown that the presented model in this study was capable of evaluating FS at a higher level of confidence regarding the other model (R = 0.89 for presented model in this study comparing R = 0.78 for the other model) and can be efficient enough to be used as a simple mathematical tool for evaluation of factor of safety for circular mode failure especially in preliminary stages of the designing phase.     

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.     

Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques

Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. It has been found that ANN and ANFIS techniques have much better performances than the empirical formulas (for the test data set, ANN R² = 0.97, ANFIS R² = 0.92 and Marciano R² = 0.54). Between ANN and ANFIS, ANN model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process is quite complicated. In this research, the Gamma test (GT) has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration. More studies are needed to gain wider experience about this data selection tool and how it could be used in assessing the validation data.     

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.     

Contribution of tuned liquid column gas dampers to the performance of offshore wind turbines under wind,wave, and seismic excitations

The main intention of the present study is to reduce wind, wave, and seismic induced vibrations of jackettype offshore wind turbines (JOWTs) through a newly developed vibration absorber, called tuned liquid column gas damper (TLCGD). Using a Simulink-based model, an analytical model is developed to simulate global behavior of JOWTs under different dynamic excitations. The study is followed by a parametric study to explore efficiency of the TLCGD in terms of nacelle acceleration reduction under wind, wave, and earthquake loads. Study results indicate that optimum frequency of the TLCGD is rather insensitive to excitation type. In addition, while the gain in vibration control from TLCGDs with higher mass ratios is generally more pronounced, heavy TLCGDs are more sensitive to their tuned frequency such that ill-regulated TLCGD with high mass ratio can lead to destructive results. It is revealed that a well regulated TLCGD has noticeable contribution to the dynamic response of the JOWT under any excitation.     

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.