Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

Mansouri, Iman; Gholampour, Aliakbar; Kisi, Özgür; Ozbakkaloglu, Togay

doi:10.1007/s00521-016-2492-4

Cited by 83 publications

(32 citation statements)

References 43 publications

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“…From Table 1, the statistical measurements for the training and testing datasets show good agreement, meaning that both of them represent almost similar distributions. Before the models simulation, the input and output parameters are normalized by scaling them between 0.2 and 0.8 using (13) to eliminate their dimension effects and to ensure that all variables receive equal attention during training; moreover, it gives the models more flexibility to estimate beyond the training range [23].…”

Section: Case Studymentioning

confidence: 99%

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Kaloop

Elbeltagi

2017

Shock and Vibration

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This study investigates predicting the pullout capacity of small ground anchors using nonlinear computing techniques. The input-output prediction model for the nonlinear Hammerstein-Wiener (NHW) and delay inputs for the adaptive neurofuzzy inference system (DANFIS) are developed and utilized to predict the pullout capacity. The results of the developed models are compared with previous studies that used artificial neural networks and least square support vector machine techniques for the same case study. The in situ data collection and statistical performances are used to evaluate the models performance. Results show that the developed models enhance the precision of predicting the pullout capacity when compared with previous studies. Also, the DANFIS model performance is proven to be better than other models used to detect the pullout capacity of ground anchors.

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Section: Case Studymentioning

confidence: 99%

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Kaloop

Elbeltagi

2017

Shock and Vibration

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“…Moreover, similar methods such as fuzzy logic and genetic algorithms have also been used for modelling the compressive strength of concrete [24][25][26][27][28][29][30][31][32]. A detailed state-of-the-art report can be found in earlier literatures [33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Masonry Compressive Strength Prediction Using Artificial Neural Networks

Argyropoulos

Cavaleri²,

Rodrigues

et al. 2019

Communications in Computer and Information Science

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The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.

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“…Today, soft computing (SC) has many applications in engineering problems [7][8][9][10]. There are numerous articles on the use of SC in civil engineering such as earthquakes [11,12] dams [13], concrete [14] and structural control [15]. Also, these methods are considered to estimate the capacity of structural elements [16,17] instead of finite element analysis which is a time-consuming approach [18,19].…”

Section: Introductionmentioning

confidence: 99%

Moment capacity estimation of spirally reinforced concrete columns using ANFIS

Naderpour

Mirrashid

2019

Complex Intell. Syst.

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This paper presents a predictive model based on adaptive neuro-fuzzy inference system namely ANFIS to determine the moment capacity of spiral-reinforced concrete columns. For this purpose, five input parameters including the longitudinal reinforcement index, transverse reinforcement index, axial force, diameter to length ratio and also shear force were considered to estimate the moment capacity. A collection of experimental database was applied to train and test the proposed system. This database includes 82 spiral-reinforced concrete columns (with flexure failure) which were reported in the literature and modified by PEER as a uniform database of cantilever columns. The model is created by fuzzy C-means algorithm with four cluster and Gaussian membership functions, also trained and tested by 70 and 12 datasets, respectively. It was concluded that the model of this study with high accuracy could be able to estimate the moment capacity.

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Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

Cited by 83 publications

References 43 publications

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Masonry Compressive Strength Prediction Using Artificial Neural Networks

Moment capacity estimation of spirally reinforced concrete columns using ANFIS

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