Crack width in concrete using artificial neural networks

Elshafey, Ahmed A.; Dawood, Nabil; Marzouk, H.; Haddara, Moutaz

doi:10.1016/j.engstruct.2013.03.020

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…The ANN technique has been extensively and effectively employed in civil engineering applications and is slowly gaining importance in other prominent engineering areas as well. In the recent past, ANNs have been used extensively by researchers for structural engineering applications such as failure prediction, crack detection, delamination identification and quantification of magnitude, predicting the size and position of cutouts [28,29], mechanical characteristics [30,31], etc. Recently, Zenzen et al [32] adopted a transmissibility damage indicator and an ANN to predict damage location and size.…”

Section: Introductionmentioning

confidence: 99%

“…The ANN model was developed using the Levenberg-Marquardt backpropagation algorithm to predict the natural frequency and buckling loads of composite plates. Elshafey et al [29] developed an effective ANN model for crack width prediction of thick and thin concrete members using the feed-forward backpropagation method. It was reported that the predicted average crack width results were more accurate than the results obtained using the rules in existing building codes.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

et al. 2021

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The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

et al. 2021

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“…Out of these approaches, artificial intelligence (AI) approaches have been broadly used by researchers in the fields of civil engineering in the last two decades [20,21,22]. Such data-driven methods, mainly artificial neural network (ANN) and fuzzy logic (FL), have become popular because of their prediction ability in many engineering applications [23,24], especially in terms of mechanical strength of GPC [25,26,27,28,29,30,31]. As an example, Topçu et al [32] developed both ANN and FL models to predict the 7-, 28-, and 90-day compressive strength of fly ash-based concrete.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

et al. 2019

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Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

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“…Al-Rahmani et al [112] used ANN to predict the most probable cracking pattern in bridge girders. Elshafey et al [113] estimated the crack width in thick concrete elements using RBF neural network.…”

Section: Prediction Applicationsmentioning

confidence: 99%

Neurocomputing in Civil Infrastructure

et al. 2016

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Abstract. This article presents a review of the recent applications of Arti cial Neural Networks (ANN) for civil infrastructure including structural system identi cation, structural health monitoring, structural vibration control, structural design and optimization, prediction applications, construction engineering, and geotechnical engineering. The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural networks. Deep machine learning techniques are among the newest techniques to nd applications in civil infrastructure systems.

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Crack width in concrete using artificial neural networks

Cited by 31 publications

References 16 publications

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

Neurocomputing in Civil Infrastructure

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