Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models

Khan, Sadaqat Ullah; Ayub, Tehmina; Rafeeqi, Sahibzada Farooq Ahmad

doi:10.12691/ajcea-1-1-2

Cited by 26 publications

(12 citation statements)

References 24 publications

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“…Artificial neural network (ANN) has been used to predict fresh and hardened properties of high performance concrete (Khan et al 2013) and LWA concrete (Alshihri et al 2009;Abdeen and Hodhod 2010). The results of these studies have generally confirmed ANN to be a powerful method for such applications.…”

Section: Related Work In Modeling and Predicting Concrete Propertiesmentioning

confidence: 98%

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Omran

Chen

Jin

2016

J. Comput. Civ. Eng.

131

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With its growing emphasis on sustainability, the construction industry is increasingly interested in environmentally friendly concrete produced using alternative and/or recycled waste materials. However, the wide application of such concrete is hindered by lack of understanding of the impacts of these materials on concrete properties. This research investigates and compares the performance of nine data mining models in predicting the compressive strength of a new type of concrete containing three alternative materials as fly ash, Haydite® lightweight aggregate, and Portland limestone cement. These models include three advanced predictive models (multilayer perceptron, support vector machines, and Gaussian processes regression), four regression tree models (M5P, REPTree, M5-Rules, and decision stump), and two ensemble methods (additive regression and bagging) with each of the seven individual models used as base classifier. The analytical results show that with appropriate parameter settings all of these models except for decision stump achieved acceptable prediction performance. The ensemble methods improved the prediction accuracy of the four regression tree models, but had less success on the other three advance predictive models. The individual Gaussian processes regression model as well as its related ensemble models reached the highest prediction accuracy in comparison groups. The results of this paper offer valuable insights to improving the use of these models for property prediction of concrete.

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Section: Related Work In Modeling and Predicting Concrete Propertiesmentioning

confidence: 98%

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Omran

Chen

Jin

2016

J. Comput. Civ. Eng.

131

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“…The topic of using neural networks for predicting optimal concrete mix proportions has been explored many times in the past few decades. The research work [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] included investigating the compressive strength and workability of plain concrete or concrete with different aggregates and additions. Probably the largest contribution in this area was given by Dr I.-C. Yeh which created the rst database containing 1030 sets of data about properties of concrete specimens (mix proportions, age, compressive strength).…”

Section: Applications Of Annsmentioning

confidence: 99%

“…11,14 Some investigations have been directed toward specic types of concrete such as high-performance, 9,10 green, 16,17,22 self-compacting, 18 and lightweight concrete. Khan et al 15 estimated the compressive strength of plain concrete using MATLAB ANN simulation and aer comparison of the mathematical results and 55 experimental results, concluded that multilayer feedforward neural network gives satisfactory results. Although the multilayer feed-forward approach is mostly used, 9,10,14,17,21,24 researchers oen use other techniques for predicting the compressive strength of concrete, such as fuzzy logic, decision tree or support vector machine.…”

Section: Applications Of Annsmentioning

confidence: 99%

“…Machine learning has been used in the eld of concrete technology in the past twenty years. Various neural networks were made [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] in order to predict primarily the compressive strength of concrete with different additions. Nonetheless, ANNs may be used also for predicting electrical conductivity of smart concrete, similar to the model used for conductive nanomodied polymer materials.…”

Section: Introductionmentioning

confidence: 99%

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Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Kekez

Kubica

2020

RSC Adv.

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“…In another study, Khan et al (2013) [59] have predicted the compressive strength of plain concrete confined with ferrocement using ANN, with 8 inputs, namely the cylinder and core dimensions, number of mesh layers, yield strength, wire diameter, wire spacing, unconfined compressive strength and experimental confined compressive strength, and one output, the theoretical confined compressive strength with 16 neurons as hidden variables. Out of 55 experimental results, 19 were selected for training of multi-layer feed forward neural network.…”

Section: Ann Prediction and Modeling Studiesmentioning

confidence: 99%

Modeling and Prediction of Flexural Strength of Hybrid Mesh and Fiber Reinforced Cement-Based Composites Using Artificial Neural Network (Ann)

Sakthivel¹

2016

geomate

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ABSTRACT:In this paper, Artificial Neural Network (ANN) has been used to predict the equivalent flexural strength of hybrid mesh and fiber reinforced cement-based composites (HMFRCBC). Three ANN models (Models 1, 2 and 3) were developed for predicting the flexural strength of cement-based composites. Model 1 used 48 data of the previously published data of the present authors and Model 2 used 48 data (out of the 75 ANN validated data) from previous studies related to mesh reinforced cement-based composites. Model 3 with 98 data (combined data sets of Model 1 and Model 2) employed seven input parameters, namely the width and depth of slab, cylinder compressive strength, mesh ultimate strength, mesh volume fraction, fiber volume fraction, fiber ultimate strength, and an output, the ultimate flexural strength of mesh and fiber reinforced cement based composites. Hidden layer was fixed based on 5 trial runs for Models 1 and 2, and 10 trial runs for Model 3. All the three models (Models 1, 2 and 3) were trained with 80% of the data, and tested with balance 20% of the data. For Models 1, 2 and 3, the Lowest Individual Error (LIE) of 11.18%, 6.95% and 11.56% (respectively) is achieved in Trial Run No.1.4 (with I-H-O, Input-Hidden Neurons-Output of 5-6-1), Trial Run No.2.2 (5-4-1) and Trial Run No.3.7 (7-10-1) respectively. Also, the lowest absolute average deviation (AAD%) of 4.81%, 3.51% and 4.58% (respectively); lowest Root Mean Square Error (RMSE) of 0.86, 0.76 and 0.99 (respectively); and highest R 2 of 0.933, 0.988 and 0.975 (respectively) are seen for these trials 1.4, 2.2, 3.7 in Models 1, 2 and 3 respectively. All the three ANN models were found to be in good agreement with actual results, and these three ANN models can serve as simple but reliable predictive tools in determination of flexural strength of HMFRCBC.

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Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models

Cited by 26 publications

References 24 publications

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Modeling and Prediction of Flexural Strength of Hybrid Mesh and Fiber Reinforced Cement-Based Composites Using Artificial Neural Network (Ann)

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