Assessing the load carrying capacity of concrete anchor bolts using non-destructive tests and artificial multilayer neural network

2020 ◽  
Vol 30 ◽  
pp. 101260 ◽  
Author(s):  
Muhammad Saleem
Keyword(s):  
Neural Network ◽  
Carrying Capacity ◽  
Load Carrying Capacity ◽  
Load Carrying ◽  
Anchor Bolts ◽  
Non Destructive

scholarly journals Knowledge-Based Prediction of Load-Carrying Capacity of RC Flat Slab through Neural Network and FEM

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Afaq Ahmad ◽  
Muhammad Usman Arshid ◽  
Toqeer Mahmood ◽  
Naveed Ahmad ◽  
Abdul Waheed ◽  
...  
Keyword(s):  
Neural Network ◽  
Carrying Capacity ◽  
Research Work ◽  
Compressive Force ◽  
Critical Parameters ◽  
Load Carrying Capacity ◽  
Concentrated Load ◽  
Flat Slab ◽  
Load Carrying ◽  
Input Parameters

The present research work aims to compare the results for predicting the ultimate response of Reinforced Concrete (RC) members using Current Design Codes (CDCs), an alternative method based on the Compressive Force Path (CFP) method, and Artificial Neural Network (ANN). For this purpose, the database of 145 samples of RC Flat Slab with the simple supported condition under concentrated load is developed from the latest published work. All the cases studied were Square Concrete Slabs (SCS). The critical parameters used as input for the study were column dimension, cs, depth of the slab, ds, shear span ratio, a v s / d , longitudinal percentage steel ratio, ρls, yield strength of longitudinal steel, fyls, the compressive strength of concrete, fcs, and ultimate load-carrying capacity, Vus. Seven ANN models were trained using different combinations of input parameters and different points of hidden neurons with different activation functions. The results exhibited that SCS-4 was the most optimized ANN model, having the maximum value of R (89%) with the least values of MSE (0.62%) and MAE (6.2%). It did not only reduce the error but also predicted accurate results with the least quantity of input parameters. The predictions obtained from the studied models (i.e., CDCs, CFP, and ANN) exhibited that results obtained using the ANNs model correlated well with the experimental data. Furthermore, the FEM results for the selected cases show the closer result to the ANN predictions.

Determining maximum load-carrying capacity of robots using adaptive robust neural controller

Robotica ◽  
2010 ◽  
Vol 28 (7) ◽  
pp. 1083-1093 ◽  
Author(s):  
M. H. Korayem ◽  
A. Alamdari ◽  
R. Haghighi ◽  
A. H. Korayem
Keyword(s):  
Neural Network ◽  
Carrying Capacity ◽  
Industrial Robot ◽  
Maximum Load ◽  
Load Carrying Capacity ◽  
Back Propagation Algorithm ◽  
Robust Controller ◽  
Linearization Methods ◽  
Load Carrying ◽  
Articulated Robots

SUMMARYIn this paper, the combination of neural network (NN), proportional derivative (PD), and robust controller are used for determining the maximum load-carrying capacity (MLCC) of articulated robots, subject to both actuator and end-effector deflection constraints. The proposed technique is then applied to articulated robots, and MLCC is obtained for a given trajectory. In the practical simulations, it's impossible to determine the parameters of robot model exactly, so the trajectory tracking performance of the proportional integral derivative (PID) and computed torque methods significantly decrease. The PD control of robot has major problem, it cannot guarantee zero steady state error. For this reason, the NN controller with PD and robust controller are used. The multilayer neural network is also used to compensate gravity and friction effects. By using Lyapunov Direct Method it is shown that the stability of closed loop system would be guaranteed, if the weights of multilayer had certain learning rules. Standard back propagation algorithm is used as a learning algorithm to update the connection weights of the NN controller. The simulation results of the proposed adaptive robust neural network (ARNN) controller are compred with sliding mode and feedback linearization methods for flexible joint robot, and compared with open loop controller for 3D industrial robot. The obtained results assured the robustness and improvement in MLCC in the presence of uncertainties in dynamic model of the robot arm and external disturbances. In fact, adaptive robust NN controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.

scholarly journals Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
E. Khalilzadeh Vahidi ◽  
M. M. Roshani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carrying Capacity ◽  
Simulated Data ◽  
Maximum Load ◽  
Shear Wall ◽  
Load Carrying Capacity ◽  
Ann Model ◽  
Load Carrying ◽  
Artificial Neural

The effects of different parameters on steel plate shear wall (SPSW) are investigated. The studied parameters are thickness of plate, location of the opening, thickness of diagonal stiffeners, and thickness of circular stiffener. Load-carrying capacity of the SPSW is studied under static load using nonlinear geometrical and material analysis in ABAQUS and the obtained simulation results are verified. An artificial neural network (ANN) is proposed to model the effects of these parameters. According to the results the circular stiffener has more effect compared with the diagonal stiffeners. However, the thickness of the plate has the most significant effect on the SPSW behavior. The results show that the best place for the opening location is the center of SPSW. Multilayer perceptron (MLP) neural network was used to predict the maximum load in SPSW with opening. The predicted maximum load values using the proposed MLP model were compared with the simulated validated data. The obtained results show that the proposed ANN model has achieved good agreement with the validated simulated data, with correlation coefficient of more than 0.9975. Therefore, the proposed model is useful, reliable, fast, and cheap tools to predict the maximum load in SPSW.

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