Prediction of Buckling Load of Columns Using Artificial Neural Networks

Mukherjee, Abhijit; Deshpande, J. M.; Anmala, Jagadeesh

doi:10.1061/(asce)0733-9445(1996)122:11(1385)

Cited by 57 publications

(23 citation statements)

References 4 publications

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“…ANN has been helpful in solving structural engineering [30] and construction engineering related problems [21]. This includes structural analysis and design [31], [32], prediction of load-deflection of CFRF strengthened RC slabs [15]; prediction of shear capacity of concrete beam [27], [33]- [36], and shear strength of steel-fiber reinforced high strength concrete deep beams [24], prediction of moment capacity of ferrocement members [19], detection of structural damage [37]- [38], identification of structural system identification [39]- [40], modeling of material behavior, structural optimization [41] and structural dynamics and control [42].…”

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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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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“…Vanluchene and Sun demonstrated the application of ANN for three structural engineering problems, the first problem of pattern recognition of bending under various loading conditions; second, concrete beam design problem, and lastly, a rectangular plate analysis. Structural stability for compression member using ANN was discussed by Mukherjee et al Janusz et al applied ANN‐fuzzy model for predicting the strength properties of high‐performance concrete (HPC) mixes. Rao and Datta developed an ANN‐based control scheme for reduction of the seismic response of the multistory building.…”

Section: Introductionmentioning

confidence: 99%

Dynamic wind response of tall buildings using artificial neural network

Nikose

Sonparote

2019

Structural Design Tall Build

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Summary This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.

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“…Hence, the processing elements in ANN are also called artificial neurons (Rafiq et al, 2001). Artificial neural network described in this chapter is mostly applied to solve many civil engineering applications such as structural analysis and design (Cladera & Mar, 2004a, 2004bHajela & Berke, 1991;Sanad & Saka, 2001), structural damage assessment (Feng & Bahng, 1999;Mukherjee et al, 1996), structural dynamics and control (Chen et al, 1995;Feng & Kim, 1998) and pavement condition-rating modeling (Eldin & Senouuci, 1995).…”

Section: Introductionmentioning

confidence: 99%

Neural Network and Adaptive Neuro-Fuzzy Inference System Applied to Civil Engineering Problems

Mashrei¹

2012

Fuzzy Inference System - Theory and Applications

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Prediction of Buckling Load of Columns Using Artificial Neural Networks

Cited by 57 publications

References 4 publications

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

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

Dynamic wind response of tall buildings using artificial neural network

Neural Network and Adaptive Neuro-Fuzzy Inference System Applied to Civil Engineering Problems

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