Simulation of torsional shear test results with neuro-fuzzy control system

In the first part of this study, a series of stress-controlled hollow cylinder cyclic torsional triaxial shear tests were conducted on loose to medium dense saturated samples of clean Toyoura sand to investigate its liquefaction behavior. A uniform cyclic sinusoidal loading at a 0.1 Hz frequency was applied to air-pluviated samples where confining pressure and relative density was varied. Cyclic shear stress–strain changes, the number of cycles to reach liquefaction and pore pressure variations were recorded. Results indicate that the liquefaction resistances of uniform sands are significantly affected by the method of sample preparation and initial conditions.     

A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass

Physico-mechanical properties of rocks have great significance in all operational parts in mining activities, from exploration to final dispatch of material. Compressional wave velocity (p-wave velocity) and anisotropic behaviour of rocks are two such properties which help to understand the rock response under varying stress conditions. They also influence the breakage mechanism of rock. There are different methods to determine thep-wave velocity and anisotropyin situ and in the laboratory. These methods are cumbersome and time consuming. Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed and compared two different models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial neural network systems, for the prediction of compressional wave velocity.     

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development.The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation.Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection.     

Comparison between neuro-fuzzy and fractal models for permeability prediction

We have used different techniques for permeability prediction using porosity core data from one well at the Maracaibo Lake, Venezuela. One of these techniques is statistical and uses neuro-fuzzy concepts. Another has been developed by Pape et al. (Geophysics 64(5):1447–1460, 1999), based on fractal theory and the Kozeny–Carman equations. We have also calculated permeability values using the empirical model obtained in 1949 by Tixier and a simple linear regression between the logarithms of permeability and porosity. We have used 100% of the permeability–porosity data to obtain the predictor equations in each case. The best fit, in terms of the root mean-square error, was obtained with the statistical approach. The results obtained from the fractal model, the Tixier equation or the linear approach do not improve the neuro-fuzzy results. We have also randomly taken 25% of the porosity data to obtain the predictor equations. The increase of the input data density for the neuro-fuzzy approach improves the results, as is expected for a statistical analysis. On the contrary, for the physical model based on the fractal theory, the decrease in the data density could allow reaching the ideal theoretical Kozeny–Carman model, on which are based the fractal equations, and hence, the permeability prediction using these expressions is improved.     

Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system

The rock engineering classification system is based on six parameters defined by Bieniawski [5], who employed parallel sets of linguistic and numerical criteria that were acknowledged to influence the behaviour of rock masses and the stability of rock structures. Consequently, experts frequently relate rock joints and discontinuities as well as ground water conditions in linguistic terms, with rough calculations. Recently, intelligence system approaches such as artificial neural network (ANN) and neuro-fuzzy methods have been used successfully for time series modelling. Using neuro-fuzzy approaches, which enable the information that is stored in trained networks to be expressed in the form of a fuzzy rule base, would help to overcome this issue. This paper presents the results of a study of the application of neuro-fuzzy methods to predict rock mass rating. We note that the proposed weights technique was applied in this process. We show that neuro-fuzzy methods give better predictions than conventional modelling approaches.     

Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater

The ocean wave system in nature is very complicated and physical model studies on floating breakwaters are expensive and time consuming. Till now, there has not been available a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. This is due to complexity and vagueness associated with many of the governing variables and their effects on the performance of breakwater. In the present paper, Adaptive Neuro-Fuzzy Inference System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network-like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from experimental wave transmission of horizontally interlaced multilayer moored floating pipe breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology Karnataka, Surathkal, India. Computer simulations conducted on this data shows the effectiveness of the approach in terms of statistical measures, such as correlation coefficient, root-mean-square error and scatter index. Influence of input parameters is assessed using the principal component analysis. Also results of ANFIS models are compared with that of artificial neural network models.     

Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system

Neuro-fuzzy inference systems have been used in many areas in civil engineering applications. This study was conducted to estimate low strain dynamic properties of composite media from easily measurable physical properties using the adaptive neuro-fuzzy inference system (ANFIS). The inference system was employed to predict the shear modulus and the damping coefficient of the sand samples as an alternative to lengthy laboratory testing. ANFIS was trained using low strain dynamic test results of samples of sand reinforced with particulate rubber inclusions from a resonant column device. The training was performed with an improved hybrid method, which was found to deliver better results than classical back-propagation method such as multi-layer perceptron (MLP) and multiple regression analysis method (MRM). Using the new approach, the optimal precise value of a parameter could be estimated within the constraints of the experimental design. The ANFIS model has appeared very effective in modeling complex soil properties such as shear modulus and damping coefficient, and performs better than MLP and MRM.     

Neuro-fuzzy control strategy for earthquake-excited nonlinear magnetorheological structures

Magnetorheological (MR) damper is a prominent semi-active control device for earthquake responses mitigation of structures. The most important topic for the intelligent MR structures is choosing the control current of MR dampers quickly and accurately. The typical control strategy is on–off control strategy, i.e. bi-state control strategy, while inherent time-delay and coarse control precision lie in this strategy. This paper proposes neuro-fuzzy control strategy, in which the neural-network technique is adopted to solve time-delay problem and the fuzzy controller is used to determine the control current of MR dampers quickly and accurately. Through comparison between the bi-state control and the neuro-fuzzy control strategies and a numerical example about a three-story reinforced concrete structure, it can be concluded that the control strategy is very important for semi-active control, the neuro-fuzzy control strategy can determine currents of MR dampers quickly and accurately, and the control effect of the neuro-fuzzy control strategy is better than that of the bi-state control strategy.