Support vector machine based aerodynamic analysis of cable stayed bridges

Lute, Venkat; Upadhyay, Apoorva; Singh, Khushboo

doi:10.1016/j.advengsoft.2009.01.008

Cited by 31 publications

(8 citation statements)

References 17 publications

(20 reference statements)

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“…For each two-class problem, the original input space is mapped onto a higher dimensional feature space and the optimal classifier that maximizes the generalization ability is determined in the feature space. 22,23 SVM has been applied for predicting the flutter derivatives for any deck size by Lute et al 24 By using the SVM method, the flutter derivatives are predicted with sufficiently good accuracy, and the critical velocity calculated is quite close to the experimental result. Furthermore, the SVM is used to establish a micro-electrical discharge machining process model by Zhang et al 25 The experimental results demonstrate that the method is precise and effective in obtaining Pareto-optimal solutions of parameter settings.…”

Section: Svm For Regressionmentioning

confidence: 94%

Design and calibration method for a novel six-component piezoelectric balance

Jia

Gao

Ren

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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Wind tunnel balance is one of the most important measurement equipments in aerodynamic testing. In this paper, a new six-component piezoelectric balance is developed to measure the dynamic impact loading force in the wind tunnel. The arrangement mode of the triaxial piezoelectric load cells is confirmed based on the theory analysis. Furthermore, the mathematical model is established according to the calibration experimental results. Support vector machine is proposed to develop the piezoelectric balance calibration. It is an effective method to predict the model using small samples and reduce the duration of the calibration. The results of prediction are compared to the conventional calibration and the dynamic step response. The linearity and repeatability of the balance are within 0.2% and 0.5%, respectively, and the interference error has been reduced using the support vector machine method. The experimental results have shown that the four supports arrangement mode can reduce the area of attack and enhance the measuring range of the balance. The dynamic characteristics of the piezoelectric balance performed by the step response test show that the designed balance is feasible to measure the dynamic impact airloads in a wind tunnel.

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Section: Svm For Regressionmentioning

confidence: 94%

Design and calibration method for a novel six-component piezoelectric balance

Jia

Gao

Ren

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…where σ is the parameter of RBF. Therefore, the SVR function with RBF kernel would be introduced as follows 48 :…”

Section: Defining the Predictive Main Model Svrmentioning

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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“…[29]. DVM harf tanımadan [30] sel tahminine [31] kadar birçok farklı alanda etkin olarak kullanılmaktadır.…”

Section: Yapay Sinir Ağlarıunclassified

Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength

Erdal¹

2015

Pamukkale J Eng Sci

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Yüksek performanslı beton (high performance concrete, HPC)'un eksenel basınç dayanımının yüksek doğrulukla tahmini son derece önemli bir konudur. Geçtiğimiz yıllarda, çeşitli gelişmiş modelleme yaklaşımları ve metodolojileri kullanılarak farklı başarı oranları ile HPC basınç dayanımı tahminlemeleri uygulanmıştır. Bu çalışmada farklı karışım oranları kullanılarak HPC'lerin eksenel basınç dayanımının tahmininde uygun bir makine öğrenmesi yöntemi araştırılmıştır. Son yıllarda makine öğrenmesinde oldukça gelişmekte olan Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM)'nin bu tahminde uygulanabilirliği incenmiş ve son derece yüksek tahmin sonuçları elde edilmiştir. Bu çalışmada DVM'lerin tahmin başarısının YSA'lara oranla daha tatmin edici sonuçlar verdiği görülmüştür. DVM yönteminin araştırma laboratuvarları ve beton firmaları tarafından dayanım tahmininde alternatif bir yöntem olarak etkin bir şekilde kullanılabileceği sonucuna varılmıştır.Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC's compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM)'s applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it's obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM's can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength. Anahtar kelimeler: Yüksek performanslı beton, Yapay sinir ağları, Destek vektör makineleri

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Support vector machine based aerodynamic analysis of cable stayed bridges

Cited by 31 publications

References 17 publications

Design and calibration method for a novel six-component piezoelectric balance

Design and calibration method for a novel six-component piezoelectric balance

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

Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength

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