A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns

Bardhan, Abidhan; Biswas, Rahul; Kardani, Navid; Iqbal, Mudassir; Samui, Pijush; Singh, Manmohan; Asteris, Panagiotis G.

doi:10.1016/j.conbuildmat.2022.127454

Cited by 61 publications

(23 citation statements)

References 57 publications

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“…The performance of the developed GEP models were evaluated using statistical functions, namely, coefficient of correlation (R), root mean square error (RMSE), and mean absolute error (MAE), which are common statistical indices used for the evaluation of AI models in accordance with the previous literature [ 37 , 48 , 49 , 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

et al. 2022

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Reinforced concrete structures are subjected to frequent maintenance and repairs due to steel reinforcement corrosion. Fiber-reinforced polymer (FRP) laminates are widely used for retrofitting beams, columns, joints, and slabs. This study investigated the non-linear capability of artificial intelligence (AI)-based gene expression programming (GEP) modelling to develop a mathematical relationship for estimating the interfacial bond strength (IBS) of FRP laminates on a concrete prism with grooves. The model was based on five input parameters, namely axial stiffness (Eftf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of the groove (hg), and IBS was considered the target variable. Ten trials were conducted based on varying genetic parameters, namely the number of chromosomes, head size, and number of genes. The performance of the models was evaluated using the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The genetic variation revealed that optimum performance was obtained for 30 chromosomes, 11 head sizes, and 4 genes. The values of R, MAE, and RMSE were observed as 0.967, 0.782 kN, and 1.049 kN for training and 0.961, 1.027 kN, and 1.354 kN. The developed model reflected close agreement between experimental and predicted results. This implies that the developed mathematical equation was reliable in estimating IBS based on the available properties of FRPs. The sensitivity and parametric analysis showed that the axial stiffness and width of FRP are the most influential parameters in contributing to IBS.

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Section: Methodsmentioning

confidence: 99%

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

et al. 2022

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“…The accuracy of the ANN model is more comparable to the ACI model rather than GEP, GBT, and RF regression models. This compassion suggests that hybrid ANN models optimized by a variety of Algorithms are capable of further minimizing this prediction error [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

“…The previous researchers recommended the use of a variety of AI models for solving engineering problems [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Modern engineering values numerical [ 42 , 43 , 44 ] and artificial intelligence (AI) models for solving complex and nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models

et al. 2022

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An accurate calculation of the flexural capacity of flexural members is vital for the safe and economical design of FRP reinforced structures. The existing empirical models are not accurately calculating the flexural capacity of beams and columns. This study investigated the estimation of the flexural capacity of beams using non-linear capabilities of two Artificial Intelligence (AI) models, namely Artificial neural network (ANN) and Random Forest (RF) Regression. The models were trained using optimized hyperparameters obtained from the trial-and-error method. The coefficient of correlation (R), Mean Absolute Error, and Root Mean Square Error (RMSE) were observed as 0.99, 5.67 kN-m, and 7.37 kN-m, for ANN, while 0.97, 7.63 kN-m, and 8.02 kN-m for RF regression model, respectively. Both models showed close agreement between experimental and predicted results; however, the ANN model showed superior accuracy and flexural strength performance. The parametric and sensitivity analysis of the ANN models showed that an increase in bottom reinforcement, width and depth of the beam, and increase in compressive strength increased the bending moment capacity of the beam, which shows the predictions by the model are corroborated with the literature. The sensitivity analysis showed that variation in bottom flexural reinforcement is the most influential parameter in yielding flexural capacity, followed by the overall depth and width of the beam. The change in elastic modulus and ultimate strength of FRP manifested the least importance in contributing flexural capacity.

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“…However, there are few studies on cost optimization of concrete, and even fewer studies on multi-objective optimization of UCS and cost. To solve the above problems, this paper proposes a multi-objective optimization the beetle antennae search (MOBAS) algorithm to optimize the UCS and cost of the concrete mixture [ 53 ]. For the optimization of the UCS of concrete, BAS is firstly used to tune the hyperparameters of SVM, RF, and DT, and then the model with the best prediction effect of concrete UCS is found from the above three models.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu

Zhou

et al. 2022

Materials

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Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

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A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns

Cited by 61 publications

References 57 publications

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

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