Soil bentonite wall protects foundation from thrust faulting: analyses and experiment

When seismic thrust faults emerge on the ground surface,they are particularly damaging to buildings,bridges and lifelines that lie on the rupture path.To protect a structure founded on a rigid raft,a thick diaphragm-type soil bentonite wall(SBW) is installed in front of and near the foundation,at sufficient depth to intercept the propagating fault rupture.Extensive numerical analyses,verified against reduced–scale(1 g) split box physical model tests,reveal that such a wall,thanks to its high deformability and low shear resistance,"absorbs" the compressive thrust of the fault and forces the rupture to deviate upwards along its length.As a consequence,the foundation is left essentially intact.The effectiveness of SBW is demonstrated to depend on the exact location of the emerging fault and the magnitude of the fault offset.When the latter is large,the unprotected foundation experiences intolerable rigid-body rotation even if the foundation structural distress is not substantial.     

Stress/strain variability in fractured media: a fracture geometric study

Geotechnical and Geological Engineering - The presence of natural fractures in fractured media plays a vital role in the in-situ stress state, which is predominantly influenced by tectonic stresses...     

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.     

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.     

Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data

The ability of the extreme learning machine (ELM) is investigated in modelling groundwater level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province, southern Iran. Monthly precipitation, evaporation and previous GWL data were used as model inputs. Developed ELM models were compared with the artificial neural networks (ANN) and radial basis function (RBF) models. The models were also compared with the autoregressive moving average (ARMA), and evaluated using mean square errors, mean absolute error, Nash-Sutcliffe efficiency and determination coefficient statistics. All the data-driven models had better accuracy than the ARMA, and the ELM model’s performance was superior to that of the ANN and RBF models in modelling 1-, 2- and 3-month-ahead GWL. The RMSE accuracy of the ANN model was increased by 37, 34 and 52% using ELM for the 1-, 2- and 3-month-ahead forecasts, respectively. The accuracy of the ELM models was found to be less sensitive to increasing lead time.     

Shear effects in shakedown analysis of offshore structures

In this paper, a formulation for shakedown analysis of elastic-plastic offshore structures under cyclic wave loading is presented. In this formulation, a fast numerical solution method is used, suitable for the Finite Element Method (FEM) analysis of large offshore structures on which shear effects in addition to bending and axial effects are taken into account. The Morison equation is adopted for converting the velocity and acceleration terms into resultant forces and it is extended to consider arbitrary orientations of the structural members. The theoretical methods of the shakedown analysis are discussed in detail and the formulation is applied to an offshore structure to verify the concept employed and its analytical capabilities.     

Seismic response of pile foundations in liquefiable soil: parametric study

The performance of pile foundations in liquefiable soil subjected to earthquake loading is a very complex process. The strength and stiffness of the soil decrease due to the increase in pore pressure. The pile can be seriously destroyed by the soil liquefaction during strong earthquakes. This paper presents the response of vertical piles in liquefiable soil under seismic loads. A finite difference model, known as fast Lagrangian analysis of continua, is used to study the pile behavior considering a nonlinear constitutive model for soil liquefaction and pile?Csoil interaction. The maximum lateral displacement and maximum pile bending moment are obtained for different pile diameters, earthquake predominant frequencies, Arias intensities, and peak accelerations. It is found that the maximum lateral displacement and the maximum pile bending moment increase when the predominant earthquake frequency value decreases for a given peak acceleration value.     

Stability of rubble-mound breakwater using H50 wave height parameter

The prediction of rubble mound breakwaters' stability is one of the most important issues in coastal and maritime engineering. The stability of breakwaters strongly depends on the wave height. Therefore, selection of an appropriate wave height parameter is very vital in the prediction of stability number. In this study, H₅₀, the average of the 50 highest waves that reach the breakwater in its useful life, was used to predict the stability of the armor layer. First, H₅₀ was used instead of the significant wave height in the most recent stability formulas. It was found that this modification yields more accurate results. Then, for further improvement of the results, two formulas were developed using model tree.To develop the new formulas, two experimental data sets of irregular waves were used. Results indicated that the proposed formulas are more accurate than the previous ones for the prediction of the stability parameter. Finally, the proposed formulas were applied to regular waves and a wide range of damage levels and it was seen that the developed formulas are applicable in these cases as well.