• Structural Engineering and Mechanics
• /
• v.83 no.5
• /
• pp.671-680
• /
• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• Computers and Concrete
• /
• v.29 no.6
• /
• pp.433-444
• /
• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• Structural Engineering and Mechanics
• /
• v.84 no.5
• /
• pp.665-673
• /
• 2022

Additive manufacturing is an emerging method to manufacture objects with complex shapes and intricate geometry, such as cellular structures. The cellular structures can widely be used in lightweight application as it provides a high strength-to-load ratio. Under the various testing condition, each topology shows different mechanical properties. This study investigates the structural response of various types of cellular structures in compression loading, both experimentally and numerically. For that purpose, honeycomb, modified honeycomb, and spiral-type topology were selected to investigate. Besides, structural properties change by changing the cell size for each topology is also investigated. The specimens were subjected to a compression test by a universal testing machine to determine the absorbed energy and other mechanical properties. An implicit numerical study was also conducted to determine cellular structure's mechanical characteristics. The experimental and numerical results show that the honeycomb structure absorbs the maximum energy compared to the other structures. The experimentally and numerically calculated absorbed energy for the 4.8 mm honeycomb structure was 32.2J and 30.63J, respectively. The results also show that the increase of cell size for a particular cellular structure reduces the energy-absorbing ability of that structure.

• Geomechanics and Engineering
• /
• v.31 no.5
• /
• pp.453-460
• /
• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• Advances in Computational Design
• /
• v.8 no.3
• /
• pp.237-253
• /
• 2023

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

• Geomechanics and Engineering
• /
• v.32 no.3
• /
• pp.271-291
• /
• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.

• Advances in nano research
• /
• v.14 no.2
• /
• pp.117-126
• /
• 2023

This experimental study investigated friction stir welding (FSW) of the acrylonitrile-butadiene-styrene (ABS) T-joint in the presence of various nano-silica levels. This study aim to handle the drawbacks of the friction stir welding (FSW) of an ABS T-joint with various quantity of nanoparticles and assess the performance of nanoparticles in the welded joint. Moreover, the relationship between the nanoparticle quantity and FSW was analyzed using response surface methodology (RSM) Box-Behnken design. The input parameters were the tool rotation speed (400, 600, 800 rpm), the transverse speed (20, 30, 40 mm/min), and the nano-silica level (0.8, 1.6, 2.4 g). The tensile strength of the prepared specimens was determined by the universal testing machine. Silica nanoparticles were used to improve the mechanical properties (the tensile strength) of ABS and investigate the effect of various FSW parameters on the ABS T-joint. The results of Box-Behnken RSM revealed that sound joints with desired characteristics and efficiency are fabricated at tool rotation speed 755 rpm, transverse speed 20 mm/min, and nano-silica level 2.4 g. The scanning electron microscope (SEM) images revealed the crucial role of silica nanoparticles in reinforcing the ABS T-joint. The SEM images also indicated a decrease in the nanoparticle size by the tool rotation, leading to the filling and improvement of seams formed during FSW of the ABS T-joint.

• Geomechanics and Engineering
• /
• v.29 no.5
• /
• pp.523-533
• /
• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• Steel and Composite Structures
• /
• v.47 no.3
• /
• pp.395-403
• /
• 2023

The main objective of this article is to investigate the response of different fiber metal laminates subjected to low velocity impact experimentally and numerically via finite element method (FEM). Hence, two different fiber metal laminate (FML) samples (GLARE/CARALL) are made of 7075-T6 aluminum sheets and polymeric composites reinforced by E-glass/carbon fibers. In order to study the responses to the low velocity impacts, samples are tested by drop weight machine. The projectiles are released from 1- and 1.5-meters height were the speed reaches to 4.42 and5.42 meter per second and the impact energies are measured as 6.7 and 10 Joules. In addition to experimental study, finite element simulation is done and results are compared. Finally, a detailed study on the maximum deflection, delamination and damages in laminates and geometry's effect of projectiles on the laminate response is done. Results show that maximum deflection caused by spherical projectile for GLARE samples is more apparent in comparison with the CARALL samples. Moreover, the maximum deflection of GLARE samples subjected to spherical projectile with 6.7 Joules impact energy, 127% increases in comparison with the CARALL samples in spite of different total thickness.

• Wind and Structures
• /
• v.36 no.6
• /
• pp.367-377
• /
• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.