The Compressive Strength Prediction for FRP‐Confined Concrete in Circular Columns by Applying the Normalized AlexNet‐ELM and the Advanced Red Fox Optimization Algorithm

Cui, Wenming; Zhao, Lianheng; Xu, Yi‐Peng; Mamlooki, Mina

doi:10.1002/adts.202100410

Cited by 6 publications

(3 citation statements)

References 44 publications

(46 reference statements)

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“…Cui et al [68] used the normalized AlexNet-ELM and the sophisticated Red Fox optimization method to forecast the compressive strength of FRP-confined concrete in circular columns. The validity of the predictions was demonstrated through a parametric study, and the precision was assessed by an empirical versus theoretical comparison.…”

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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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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“…For example, high flexural strength, high fluidity, high abrasion resistance, high modulus of elasticity, low permeability, high durability, etc 2,3 . These improvements in mechanical features have led HPC to be employed widely in constructing applications, essentially in road construction, high‐rise towers, tunnels, and long bridges 4–6 . An important technique in the production of HPC is to make HPC densely 7,8 .…”

Section: Introductionmentioning

confidence: 99%

“…2,3 These improvements in mechanical features have led HPC to be employed widely in constructing applications, essentially in road construction, high-rise towers, tunnels, and long bridges. [4][5][6] An important technique in the production of HPC is to make HPC densely. 7,8 Therefore, the crucial norms which distinguish producing HPC from other concrete types are: (a) utilizing smaller aggregate particles, (b) mixing additional cement-based matters such as silica-fume (SF), and (c) using superplasticizer as water-reducing agents to reduce ratio of water/binder.…”

Section: Introductionmentioning

confidence: 99%

Developing a support vector regression model via optimization algorithms to appraise the hardness properties of high‐performance concrete

Yan

2022

Structural Concrete

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High-performance concrete (HPC) as a highly sophisticated aggregate in constructional projects has made modeling given mechanical properties a very complex problem. Declaring by many studies, mechanical features of HPC are not only characterized by the maximum size of coarse aggregate and water amount since influencing by the other components. Using fly-ash and silica fume as the key constituents can simultaneously increase the hardness aspects and the environmental effects. Considering the compressive strength and slump flow of concrete should be investigated before performing any practical practices. Artificial intelligence approaches with precise and low-cost methods can replace the costly experimental ways. Therefore, the present paper has aimed to link a prediction model with optimization algorithms to accurately appraise the hardness properties of HPC samples rarely found in literature like this way. In this regard, a machine learning approach of Support Vector Regression using two kernels of Gaussian and radial basis function is coupled with matheuristic algorithms to optimize the modeling process of compressive strength and slump flow of HPC samples. The internal settings of SVR would be tuned at optimal rate by optimizers to function efficiently. To investigate the performance of hybrid frameworks developed in this research, several indicators evaluated the results of hybrid models. Therefore, the R 2 of the models was calculated averagely at 0.91 with a maximum difference rate of 11% for the testing phase. While the RMSE index assessed the models with higher values of 16.56 mm for slump and 12.86 MPa for compressive strength. Generally, using smart approaches with high-accuracy performance has been proposed to be used instead of physical procedures increasing the productivity of concrete compressive strength in terms of time, energy, and cost criteria.

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Auto‐regulated radial base function structure implementation in the hybrid and ensemble hybrid domains to assess the hardened properties of novel mixture high performance concrete

Lei

2024

Structural Concrete

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The mechanical properties of concrete, such as compressive strength and slump flow rates, are very nonlinear. For academics, it is crucial to forecast these qualities while creating new building methods. Such capabilities should be developed to lower the cost of expensive tests and increase the precision of the measurements. The goal of this study is to create an radial basis function neural network to describe the characteristics of hardness in high‐performance concrete (HPC) mix. Metaheuristic techniques were used to enhance the RBFNN's functionality. A dataset of 181 HPC mixes comprising ecologically beneficial ingredients, such as fly ash and silica fume, was used for training and evaluating the capabilities of the proposed hybrid models. According to the modeling process based on sensitivity analysis of input parameters and the results of hybrid models, the model combined with the multiverse optimization algorithm (MVO) had a higher correlation between the predicted and observed CS and slump values than the model combined with three optimization algorithms in terms of the R2 index being the maximum value of 0.984 in the tasting phase of CS and SL estimation. While evaluating two mechanical aspects of HPC samples, the of the model coupled with the MVO algorithm reconfirmed its accuracy being 3.59.

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The Compressive Strength Prediction for FRP‐Confined Concrete in Circular Columns by Applying the Normalized AlexNet‐ELM and the Advanced Red Fox Optimization Algorithm

Cited by 6 publications

References 44 publications

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

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

Developing a support vector regression model via optimization algorithms to appraise the hardness properties of high‐performance concrete

Auto‐regulated radial base function structure implementation in the hybrid and ensemble hybrid domains to assess the hardened properties of novel mixture high performance concrete

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