Compressive strength prediction of CFRP confined concrete using data mining techniques

Camões, Aires; Martins, Francisco F.

doi:10.12989/cac.2017.19.3.233

Cited by 5 publications

(3 citation statements)

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“…Linear function:

K ((),, x_{i}, x) goodbreak= x_{i} x

Polynomial function:

K ((),, x_{i}, x) goodbreak= {(x_{i} (x + 1))}^{d}

Radial‐based function:

K ((),, x_{i}, x) goodbreak= \exp ([], goodbreak- \frac{((), x_{i} goodbreak- x) ((), x_{i} goodbreak- x)}{2 σ^{2}})

Sigmoid function:

K ((),, x_{i}, x) goodbreak= \tanh ((), x_{i} ((), x goodbreak+ 1))

where x i and x , are the training and test inputs, respectively, σ is the Gaussian kernel function, and d is the kernel function's polynomial degree. Earlier, SVR has been used in various structural engineering applications, for example, for modeling concrete strength, deterioration, and other subject areas 46–52 . A review report summarizing the research conducted in structural engineering using support vector machines was carried out by Çevik et al 53 …”

Section: Overview On Support Vector Machinesmentioning

confidence: 99%

A machine‐learning‐based constitutive bond‐slip model for anchored CFRP strips externally bonded on concrete members

Kurtoğlu

Anıl

Çevik

2022

Structural Concrete

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Strengthening is often required for reinforced concrete, steel, and masonry structures or structural elements when they possess insufficient performance against external loads such as earthquakes. Recently, the use of carbon fiber-reinforced polymers (CFRP) has been considered a viable strengthening technique alternative to traditional methods. The major concern is premature debonding failure hindering the efficient use of CFRP systems. FRP anchor systems have been used to avoid this phenomenon. This paper employs a machine learning (ML)-based algorithm (support vector regression) to propose predictive models to simulate the bond-slip behavior of anchored CFRP strips externally bonded to the concrete surface. A comprehensive database was constructed using the previous reports on the bond-slip behavior of FRP-to-concrete joints anchored with CFRP strips. Afterwards, the collected database was used to train and validate the proposed models. The input parameters cover all possible factors, that is, compressive strength of concrete, width of concrete block, anchor hole diameter, anchor hole depth, number of anchors at one row, number of anchors at one column; elastic modulus, width, bonding length, and thickness of CFRP strip. The output parameters are maximum shear capacity, residual shear capacity, displacement values at peak shear, and residual shear. Results imply that the proposed models have high prediction accuracies with low error rates. Proposed models are also presented in code format to be easily incorporated into analysis software for practical use.

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“…Linear function:

K ((),, x_{i}, x) goodbreak= x_{i} x

Polynomial function:

K ((),, x_{i}, x) goodbreak= {(x_{i} (x + 1))}^{d}

Radial‐based function:

K ((),, x_{i}, x) goodbreak= \exp ([], goodbreak- \frac{((), x_{i} goodbreak- x) ((), x_{i} goodbreak- x)}{2 σ^{2}})

Sigmoid function:

K ((),, x_{i}, x) goodbreak= \tanh ((), x_{i} ((), x goodbreak+ 1))

Section: Overview On Support Vector Machinesmentioning

confidence: 99%

A machine‐learning‐based constitutive bond‐slip model for anchored CFRP strips externally bonded on concrete members

Kurtoğlu

Anıl

Çevik

2022

Structural Concrete

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show abstract

“…Moreover, Ghanizadeh et al [ 27 ], Khademi et al [ 28 ], and Reddy [ 29 ] utilized NN for predicting the ultimate strength of concrete. Other researchers have utilized NN models to forecast the strength capacity of confined concrete columns and cylinders laminated with FRP, such as Mansouri et al [ 30 ] and [ 31 , 32 , 33 , 34 , 35 ]. Several empirical models of confined concrete for extreme scenarios have been suggested, such as forecasting FRP confined concrete behavior by incorporating typical neutral networks techniques based on limited database, bond strength behavior between FRP composite and concrete, and stress–strain behavior of FRP composites based on ANN [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

et al. 2021

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This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.

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“…Consequently, the outcome would be regarded as a great combination of experimental studies with numerical methods. Furthermore, such approaches have been successfully applied to many civil engineering prediction problems …”

Section: Introductionmentioning

confidence: 99%

Numerical formulation of confined compressive strength and strain of circular reinforced concrete columns using gene expression programming approach

Sadrossadat

Ghorbani

Hamooni

et al. 2017

Structural Concrete

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In reinforced concrete (RC), concrete's relatively low tensile strength and ductility are counteracted by reinforcement of materials having higher tensile strength or ductility, such as steel reinforcing bars. A remarkable concern in design of RC columns confined with transverse reinforcement is to achieve an acceptable estimation of confined compressive strength and strain factors when they are subjected to compressive and lateral loading. To cope with this issue, various models have been proposed by researchers due to the costly procedure of experimental studies and lack of high-capacity testing equipment. Despite this fact, there still exists the necessity to develop more robust estimation models. This paper explores the capability of gene expression programming for the prediction of confined compressive strength and strain of RC columns with circular cross section. A reliable database is used to develop two new models which can be used via hand calculations for design purposes. In order to verify and validate the proposed models, several analyses are conducted and the results are compared with those provided by other researchers. Consequently, the results explicitly represent that the proposed models accurately estimate the confined compressive strength and corresponding strain of circular concrete columns and reach a notably better prediction performance than the traditional models.

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Compressive strength prediction of CFRP confined concrete using data mining techniques

Cited by 5 publications

References 0 publications

A machine‐learning‐based constitutive bond‐slip model for anchored CFRP strips externally bonded on concrete members

A machine‐learning‐based constitutive bond‐slip model for anchored CFRP strips externally bonded on concrete members

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

Numerical formulation of confined compressive strength and strain of circular reinforced concrete columns using gene expression programming approach

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