The use of neural networks in concrete compressive strength estimation

Bilgehan, Mahmut; Turğut, Paki

doi:10.12989/cac.2010.7.3.271

Cited by 54 publications

(17 citation statements)

References 18 publications

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“…The higher the weights, the higher the impact of the input node. It is used for modeling on prediction or estimation of strength of capacity of structures [23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Artificial Neural Network In Structural Engineering and Matementioning

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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Section: Artificial Neural Network In Structural Engineering and Matementioning

confidence: 99%

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

2018

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“…In general, the neural network processing consists of two distinct phases: training phase and testing phase. ANNs have the ability of achieving a favourable level of generalization from the patterns on which they have been trained [23]. Training incorporates processing the neural network with a set of known input-output data.…”

Section: Back-propagation Algorithmmentioning

confidence: 99%

Prediction of properties of recycled aggregate concrete

Miličević¹,

Šipoš²

2017

JCE

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“…Three neuron models namely, 'tansig', 'logsig' and 'purelin', have been used in the architecture of the network with the back propagation algorithm implemented in originally developed MATLAB routines. In the back propagation algorithm, the feed-forward (FFBP), cascade-forward (CFBP) and Elman back propagation (EBP) type network were considered [3,11,12,16,18,23]. Each input is weighted with an appropriate weight and the sum of the weighted inputs and the bias forms the input to the transfer function.…”

Section: Neural Network Modelmentioning

confidence: 99%

“…Analysis of the accumulated test data employing the neural network technique has been performed in order to develop a new procedure for predicting the effective strength of the slabcolumn joint. A neural network has the capability of realizing a greater variety of nonlinear relationship of considerable complexity [3,23]. In neural networking the data is presented to the network in the form of input and output parameters, and the optimum nonlinear relationship is found by minimizing a penalized likelihood.…”

Section: Introductionmentioning

confidence: 99%

Estimation of RC slab-column joints effective strength using neural networks

Shah

Ribakov

2011

Lat. Am. j. solids struct.

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The nominal strength of slab-column joints made of highstrength concrete (HSC) columns and normal strength concrete (NSC) slabs is of great importance in structural design and construction of concrete buildings. This topic has been intensively studied during the last decades. Different types of column-slab joints have been investigated experimentally providing a basis for developing design provisions. However, available data does not cover all classes of concretes, reinforcements, and possible loading cases for the proper calculation of joint stresses necessary for design purposes. New numerical methods based on modern software seem to be effective and may allow reliable prediction of column-slab joint strength. The current research is focused on analysis of available experimental data on different slab-to-column joints with the aim of predicting the nominal strength of slabcolumn joint. Neural networks technique is proposed herein using MATLAB routines developed to analyze available experimental data. The obtained results allow prediction of the effective strength of column-slab joints with accuracy and good correlation coefficients when compared to regression based models. The proposed method enables the user to predict the effective design of column-slab joints without the need for conservative safety coefficients generally promoted and used by most construction codes.

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The use of neural networks in concrete compressive strength estimation

Cited by 54 publications

References 18 publications

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

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

Prediction of properties of recycled aggregate concrete

Estimation of RC slab-column joints effective strength using neural networks

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