A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

Engineering with Computers - Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant...     

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Neural Computing and Applications - Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research,...     

Creep and Fatigue Failure in Single- and Double Hot Arm MEMS Thermal Actuators

Determination of the failure mechanisms of mechanical devices is the key to the design of reliable products. This paper reports an investigation on creep and fatigue failure of microelectromechanical (MEMS) thermal actuators. Finite element modeling is used to predict thermomechanical behavior of actuators under low to moderate voltage differences. The modeling results are compared with experimental results to evaluate the models. Two probable failure modes associated with thermal actuators, that is, fatigue and creep, are investigated, and it is found that creep is the dominant failure mechanism. The creep behaviors of several U-shape and double hot arm thermal MEMS actuators are examined, and their deformation-time curves are obtained numerically and experimentally. The curves follow a typical three-stage creep curve usually observed in metals. The creep life cycles of the devices are compared on the basis of their stress and temperature distributions. This study shows that actuators with the maximum temperature occurring at the location where the high stress is induced have shorter life spans than those experiencing the high stress away from the maximum temperature location. It is concluded that the double hot arm actuators with equal length have longer creep life than the U-shape (single hot arm) actuators.     

Microstructure and fatigue behavior of cold spray coated Al5052

The effect of cold spray coating in inducing residual stresses in the substrate and its effect on delaying crack initiation under cyclic loading have been studied on Al5052 alloy specimens. Different sets of Al5052 specimens have been coated with pure Al and Al7075 feedstock powder, using a low-pressure cold spray coating technique. Some sets of specimens were grit blasted (GB) before coating. The microstructural evolution of the substrate after coating and the fatigue behavior of the coated structure have been studied. In order to obtain the fatigue S–N diagram for each set, as-received and coated specimens with and without preceding GB treatment have been tested in a load-controlled condition. X-ray diffraction has been used to measure the residual stresses both in the deposited materials and the substrates. The results are discussed to highlight the effect of this emerging surface treatment on the characteristics of the treated material. Compressive residual stresses, which led to appreciable increase in the fatigue life, have been observed in all the coated sets. The results indicate that the fatigue strength was significantly improved up to 30% in the case of Al7075 coatings. The results show a strong dependency of the fatigue strength on the deposited material and the spray parameters.     

Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Engineering with Computers - Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce...     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R²) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R² of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.