Support vector regression based shear strength modelling of deep beams

Pal, Mahesh; Deswal, Surinder

doi:10.1016/j.compstruc.2011.03.005

Cited by 73 publications

(47 citation statements)

References 44 publications

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“…Comparative studies among some of these global ML techniques have been performed by many researchers (e.g., Ahmad et al., ; Chou et al., ; Pal & Deswal, ; Vu & Hoang, ). Ahmad et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

Luo

Paal

2019

Computer aided Civil Eng

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Drift capacity of reinforced concrete (RC) columns is an important indicator to quantify the seismic vulnerability of RC frame buildings; however, it is challenging to accurately predict this value as the nonlinear behavior can vary greatly by column type. This article proposes a novel, local machine learning (ML) model, called locally weighted least squares support vector machines for regression (LWLS‐SVMR), which integrates LS‐SVMR and locally weighted training criteria to enhance and generalize the prediction of the drift capacity of RC columns, regardless of the type. A database of 160 circular RC columns covering flexure‐, shear‐, and flexure–shear‐critical specimens was developed to train and test the proposed LWLS‐SVMR. The proposed LWLS‐SVMR was validated by comparison with popular existing global and local learning approaches as well as a traditional empirical equation, and the results demonstrated that the proposed LWLS‐SVMR is superior to all other approaches and thus, is a promising artificial intelligence technique for enhancing the prediction of drift capacity, universally across RC flexure‐, shear‐, and flexure–shear‐critical columns. The LWLS‐SVMR exhibits capabilities which may yield it a feasible approach to predict complex, nonlinear behavior in a broad‐spectrum manner.

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“…Comparative studies among some of these global ML techniques have been performed by many researchers (e.g., Ahmad et al., ; Chou et al., ; Pal & Deswal, ; Vu & Hoang, ). Ahmad et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

Luo

Paal

2019

Computer aided Civil Eng

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“…The data specific nature of these models is suggested by several studies. As the design of a structure or a structural component may require an iterative process in which the assumed model behavior converges with the experimental behavior, thus requiring a cost efficient computational technique [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

Ayaz

Kocamaz

Karakoç

2015

Construction and Building Materials

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“…1) [7,[10][11][12]14,15,[17][18][19][20][21][22][23]25,[27][28][29][30][31][32]. Some elements of notation system is presented at Fig.…”

Section: Formulation Of the Problemmentioning

confidence: 99%

About solution of multipoint boundary problem of static analysis of deep beam with the use of combined application of finite element method and discrete-continual finite element method. part 1: formulation of the problem and general principles of approximation

Lyakhovich

Negrozov

2017

MATEC Web Conf.

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Abstract. This paper is devoted to formulation and general principles of approximation of multipoint boundary problem of static analysis of deep beam with the use of combined application of finite element method (FEM) discrete-continual finite element method (DCFEM). The field of application of DCFEM comprises structures with regular physical and geometrical parameters in some dimension ("basic" dimension). DCFEM presupposes finite element approximation for non-basic dimension while in the basic dimension problem remains continual. DCFEM is based on analytical solutions of resulting multipoint boundary problems for systems of ordinary differential equations with piecewise-constant coefficients.

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Support vector regression based shear strength modelling of deep beams

Cited by 73 publications

References 44 publications

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

About solution of multipoint boundary problem of static analysis of deep beam with the use of combined application of finite element method and discrete-continual finite element method. part 1: formulation of the problem and general principles of approximation

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