Modeling ultimate deformation capacity of RC columns using artificial neural networks

İnel, Mehmet

doi:10.1016/j.engstruct.2006.05.001

Cited by 43 publications

(14 citation statements)

References 21 publications

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“…Ghaboussi et al [22]), structural analysis and design (e.g. Consolazio [23], Inel [20]), damage detection (e.g. Suresh et al [24], Saridakis et al [25]), structural dynamics and control (e.g.…”

Section: Introductionmentioning

confidence: 98%

“…Much of the success of ANN is due to its nonlinear and parallel processing characteristics. This technique has been applied in many disciplines including business, engineering, medicine and science [20]. The growing interest in the field is demonstrated by the ever-increasing applications in various areas extending from image and pattern recognition to identification and control applications [21].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of ANFIS and NN models—With a study in critical buckling load estimation

Bilgehan

2011

Applied Soft Computing

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Comparison of ANFIS and NN models—With a study in critical buckling load estimation

Bilgehan

2011

Applied Soft Computing

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“…Nehdi et al [13] applied ANN approach in estimating the shear capacity of FRP reinforced concrete beams. ANN also has been successfully used to model other properties of concrete such as creep [14], deformation due to flexural failure [15], and inelastic moments [16].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based prediction of shear strength of concrete beams without shear reinforcement

Jung¹,

Kim²

2008

Engineering Structures

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“…It can be classified as compression, flexure and shear where crack formations form depending on its failure mode. To investigate different structural members and its mode of failure, ANN were used in studies that showed good agreement with experimental tests [54][55][56][57][58][59][60][61][62][63].…”

Section: Damage Assessment In Reinforced Concretementioning

confidence: 99%

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

2018

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An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch-catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson's Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content.

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Modeling ultimate deformation capacity of RC columns using artificial neural networks

Cited by 43 publications

References 21 publications

Comparison of ANFIS and NN models—With a study in critical buckling load estimation

Comparison of ANFIS and NN models—With a study in critical buckling load estimation

Knowledge-based prediction of shear strength of concrete beams without shear reinforcement

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

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