Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods

Jamali, Faride; Mousavi, Seyed Roohollah; Peyma, Abdolhamid Bahr; Moodi, Yaser

doi:10.1177/07316844211068116

Cited by 11 publications

(6 citation statements)

References 125 publications

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“…In this section, the evaluation between the developed ML models and analytical models is discussed to define the reliability of the proposed models. In addition, the accuracy and reliability of the developed ML models has been verified by a recently developed ML model, which was given by Jamali et al [43]. Based on the results of the performance metrics of all the analytical as well as ML models, it can be concluded that the optimized GPR model has the highest precision, as indicated in Table 6.…”

Section: Discussionmentioning

confidence: 71%

“…Furthermore, the MAE, MAPE, and RMSE values of the model of Benzaid et al [48] are 119.08%, 138.20%, and 74.77% higher than those of the SVM model, whereas the R-value and NS value of the SVM model are 15.32% and 41.14% greater than those of the model of Benzaid et al [48]. Additionally, the R-value of the optimized GPR model is 1.11% higher than that of the model of Jamali et al [43], while the MAPE and RMSE values of the model of Jamali et al [43] are 25.69% and 116.18% greater than those of the optimized GPR model, respectively.…”

Section: Discussionmentioning

confidence: 81%

“…Prediction of the CS of the FRP-confined concrete cylinders was undertaken by Jamali et al [43]. A total of 1066 samples of cylinders were collected from the literature and ML models such as the multi-layer perceptron (MLP), SVR, and a combination of ANFIS with particle swarm algorithm (PSO) were used to evaluate the CS.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

et al. 2023

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The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.

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Section: Discussionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 81%

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Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

et al. 2023

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“…The compressive strength of FRP-confined concrete was investigated and determined by Jamali et al [73]. A large database of 1066 confined concrete cylinders with FRP sheets was collected.…”

Section: Compressive Strength Of Frp-confined Concretementioning

confidence: 99%

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Alhusban,

Alkhawaldeh

2023

Sustainability

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Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.

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“…Especially in bridges, building reinforcement, and marine construction, pultruded GFRP has emerged as the optimal alternative to steel due to its excellent mechanical performance and environmental resistance [ 2 ]. These materials are typically employed as reinforcement (bars, sheets, and laminates) and pultruded load-bearing components (profiles) in combination with concrete [ 3 , 4 , 5 , 6 ]. The market share of FRP (fiber-reinforced polymer) profiles has increased rapidly in the last decade to reach USD 15.3 billion, which is 6.4% of the construction market [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

The Material Heterogeneity Effect on the Local Resistance of Pultruded GFRP Columns

Zhu,

Gribniak,

Ding

et al. 2023

Materials

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Pultruded GFRP (glass fiber-reinforced polymer) materials are widely used in structural engineering because of their lightweight, corrosion immunity, and electromagnetic transparency. However, the design of load-bearing components facing substantial compressive stresses, e.g., columns, must be more stringent than steel structures due to excessive deformability, material heterogeneity, and vulnerability to stress concentration. This manuscript investigates the failure performance of locally produced GFRP materials, focusing on the material heterogeneity effect on the mechanical resistance of a support joint of a pultruded tubular GFRP column. This experimental campaign employs relatively short rectangular profile fragments to isolate the support behavior and verify a simplified numerical finite element model, which neglects the nonlinearity of GFRP material. This work determines the material failure mechanisms behind the mechanical performance of pultruded profiles subjected to longitudinal compression for various column lengths.

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Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods

Cited by 11 publications

References 125 publications

Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

The Material Heterogeneity Effect on the Local Resistance of Pultruded GFRP Columns

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