Neural network for bending moment in continuous composite beams considering cracking and time effects in concrete

Pendharkar, Umesh; Chaudhary, Sandeep; Nagpal, A. K.

doi:10.1016/j.engstruct.2006.11.009

Cited by 27 publications

(14 citation statements)

References 25 publications

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“…Pendharkar et al [19] presented a methodology using ANN for predicting the inelastic moments considering instantaneous cracking and time effects for continuous composite beams. Erdem [20] investigated the application of neural networks for predicting the ultimate moment capacity of reinforced concrete (RC) slabs in fire.…”

Section: Structural Engineering Applicationsmentioning

confidence: 99%

Applications of Soft Computing in Civil Engineering: A Review

Chandwani¹,

Agrawal²,

Nagar³

2013

IJCA

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The field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz., Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz., Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.

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Section: Structural Engineering Applicationsmentioning

confidence: 99%

Applications of Soft Computing in Civil Engineering: A Review

Chandwani¹,

Agrawal²,

Nagar³

2013

IJCA

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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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“…Creep and shrinkage result in increase in elastic moments at joints [1][2][3]. The increase in moments at joints is primarily due to shrinkage, the contribution of creep being much smaller [1][2][3]. However, cracking in concrete results in lesser increase in moments as the portion of the concrete undergoing creep and shrinkage reduces [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The time effects of creep and shrinkage in the concrete may lead to further progressive cracking and aging moment redistribution. Creep and shrinkage result in increase in elastic moments at joints [1][2][3]. The increase in moments at joints is primarily due to shrinkage, the contribution of creep being much smaller [1][2][3].…”

Section: Introductionmentioning

confidence: 99%