Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods

2021 ◽  
pp. 073168442110501
Author(s):  
Yaser Moodi ◽  
Mohammad Ghasemi ◽  
Seyed Roohollah Mousavi
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Support Vector Regression ◽  
Basis Function ◽  
Concrete Columns ◽  
Fiber Reinforced Polymer ◽  
Support Vector ◽  
Reinforced Polymer ◽  
Compressive Strength Of Concrete ◽  
Rectangular Columns

Recently, there has been a tendency to use machine learning (ML)–based methods, such as artificial neural networks (ANNs), for more accurate estimates. This paper investigates the effectiveness of three different machine learning methods including radial basis function neural network (RBNN), multi-layer perceptron (MLP), and support vector regression (SVR), for predicting the ultimate strength of square and rectangular columns confined by various FRP sheets. So far, in the previous study, several experiments have been conducted on concrete columns confined by fiber reinforced polymer (FRP) sheets with the results suggesting that the use of FRP sheets enhances the compressive strength of concrete columns effectively. Also, a wide range of experimental data (including 463 specimens) has been collected in this study for square and rectangular columns, confined by various FRP sheets. The comparison of ML-derived results with the experimental findings, which were in a very good agreement, demonstrated the ability of ML to estimate the compressive strength of concrete confined by FRP; the correlation coefficient (R2) for MLP, RBFNN, and SVR methods was equal to 0.97, 0.97, and 0.90, respectively. Similar accuracy was obtained by MLP and RBFNN, and they provided better estimates for determining the compressive strength of concrete confined by FRP. Also, the results showed that the difference between statistical indicators for training and testing specimens in the RBFNN method was greater than the MLP method, and this difference indicated the poor performance of RBFNN.

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

2020 ◽  
pp. 147592172096715
Author(s):  
Mengyue He ◽  
Yishou Wang ◽  
Karthik Ram Ramakrishnan ◽  
Zhifang Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Polymer Composites ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fiber Reinforced Polymer ◽  
Support Vector ◽  
Fiber Reinforced ◽  
Reinforced Polymer ◽  
Fiber Reinforced Polymer Composites

Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed based on the changes in multiple modes of frequencies to assess the interface, location, and size of delamination in fiber-reinforced polymer composites. Three types of machine learning algorithms including back propagation neural network, extreme learning machine, and support vector machine algorithm were adopted as inverse algorithms for assessment of the delamination parameters, with a special focus on the interface prediction. A theoretical model of fiber-reinforced polymer beam with delamination under vibration was constructed to learn how the frequencies are affected by the delaminations (“forward problem”) and to generate a database of “frequency shifts versus delamination parameters” to be used in machine learning algorithms for delamination prediction (“inverse problem”). Multiple carbon/epoxy fiber-reinforced polymer beam specimens were manufactured and measured by a laser scanning Doppler vibrometer to extract the modal frequencies. Numerical and experimental verification results have shown that support vector machine has the best prediction performance among the three machine learning algorithms, with high prediction accuracy and only requiring a small number of samples. For predicting the interface of delamination which is a discrete variable, support vector machine classification has observed better prediction accuracy and requiring less running time than regression. This study is one of the first to prove the applicability of support vector machine for structural health monitoring of delamination damage in fiber-reinforced polymer composites and has the potential to improve the prediction capability of machine learning algorithms. Another significant outcome of the study is that the interface of delamination has been predicted accurately with support vector machine.

scholarly journals Prediction of Compressive Strength Using Support Vector Regression

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 51-56
Author(s):  
Goutham J Sai ◽  
Vijay Pal Singh
Keyword(s):  
Compressive Strength ◽  
Support Vector Regression ◽  
Design Stage ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Study Support ◽  
Compressive Strength Of Concrete ◽  
Non Destructive

At the design stage of a structure, the members of adequate dimension and strength is provided. But with passage of time, the strength of the members reduces gradually due to exposure to environmental conditions and unexpected loadings other than for which the structure is designed. Non Destructive Testing (NDT) method provides a convenient and rapid method of determination of existing strength of concrete without subjecting the member to any damage.  In the present study, Support Vector Regression (SVR) in Python has been used for the prediction of compressive strength of concrete. Three different NDT techniques have been used as input for the SVR model. A good co-relation between predicted strength and strength determined after crushing the concrete cubes has been achieved. It has also been observed that accuracy in the predicted strength is more in case of inputs from more than one NDT technique is used.

scholarly journals The influence of different type of aggregate, fly ash and fiber-reinforced polymer on splitting resistance in pretensioned concrete railroad ties

2021 ◽  
Vol 27 (4) ◽  
pp. 135-140
Author(s):  
Adrijana Savić ◽  
Robert Peterman
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Fiber Reinforced Polymer ◽  
Concrete Mixture ◽  
Fiber Reinforced ◽  
Reinforced Polymer ◽  
Pretensioned Concrete ◽  
Edge Distance ◽  
Different Types ◽  
Compressive Strength Of Concrete

This research evaluates the influence of the different types of concrete mixture, using a shallow type of indentation of wire, having the different edge distance and compressive strength of concrete on splitting resistance in pretensioned concrete railroad sleepers. The investigated compressive strength of concrete was 4500psi. The research was experimental, and the part of this research was formally adapted in Arema StandardsforRailwayEngineering Chapter 30 section 4.2.4.

Behavior of Circular Concrete Columns Repaired with Steel Bars and Wire Mesh-Mortar

2010 ◽  
Vol 133-134 ◽  
pp. 1247-1252 ◽  
Author(s):  
Feng Feng Li ◽  
Xiao Yong Wu ◽  
Yan Zhou ◽  
Xiao Hua Lu ◽  
Guang Jing Xiong
Keyword(s):  
Experimental Study ◽  
Compressive Strength ◽  
Concrete Columns ◽  
Wire Mesh ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Concrete Compressive Strength ◽  
Reinforced Polymer ◽  
Steel Bar ◽  
Steel Bars

In order to increase the strengthening efficiency of steel bar mat-mortar (BM) jacket and wire mesh-mortar (WM) jacket around existed circular concrete columns, an attempt to strengthen the columns with hybrid bar mat-wire mesh-mortar (HBWM) jacket was proposed. A comparatively experimental study on axial compression behaviors of concrete columns wrapped with three different strengthening systems, namely BM, HWBM and carbon fiber reinforced polymer (CFRP) was performed. The experiment results showed that much more cracks appeared in HWBM columns compared with those in BM columns. As a result, on the premise that the concrete compressive strength of the HWBM columns increased 90% compared with that of the BM columns, the ductility of the HWBM columns reached about twice as that of the BM columns. The increase of the concrete compressive strength of CFRP strengthened columns was higher than those of HWBM and BM strengthened columns. The ductility of CFRP strengthened columns, however, was obviously lower than that of HWBW columns.

scholarly journals Modelling and Prediction of Concrete Compressive Strength Using Machine Learning

2021 ◽  
pp. 526-532
Author(s):  
K Sumanth Reddy ◽  
Gaddam Pranith ◽  
Karre Varun ◽  
Thipparthy Surya Sai Teja
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Rational Relation ◽  
Compressive Strength Of Concrete ◽  
And Performance ◽  
Materials Used

The compressive strength of concrete plays an important role in determining the durability and performance of concrete. Due to rapid growth in material engineering finalizing an appropriate proportion for the mix of concrete to obtain the desired compressive strength of concrete has become cumbersome and a laborious task further the problem becomes more complex to obtain a rational relation between the concrete materials used to the strength obtained. The development in computational methods can be used to obtain a rational relation between the materials used and the compressive strength using machine learning techniques which reduces the influence of outliers and all unwanted variables influence in the determination of compressive strength. In this paper basic machine learning technics Multilayer perceptron neural network (MLP), Support Vector Machines (SVM), linear regressions (LR) and Classification and Regression Tree (CART), have been used to develop a model for determining the compressive strength for two different set of data (ingredients). Among all technics used the SVM provides a better results in comparison to other, but comprehensively the SVM cannot be a universal model because many recent literatures have proved that such models need more data and also the dynamicity of the attributes involved play an important role in determining the efficacy of the model.

Residual Strength of the CFRP- Strengthening Small Concrete Columns in Fire Situation

2013 ◽  
Vol 302 ◽  
pp. 481-485
Author(s):  
Clayton Reis de Oliveira ◽  
Armando Lopes Moreno
Keyword(s):  
Compressive Strength ◽  
Room Temperature ◽  
Concrete Columns ◽  
Experimental Results ◽  
Fiber Reinforced Polymer ◽  
Small Scale ◽  
Residual Compressive Strength ◽  
Average Temperature ◽  
Reinforced Polymer ◽  
In Fire

This paper presents the experimental results of a study carried out to investigate the performance of small scale concrete columns retrofitted with unidirectional carbon fiber-reinforced polymer (CFRP) in fire situation. This study aims at the effectiveness of CFRP through the contrast test. In order to observe the residual compressive strength, the specimen was heated to a target temperature and mechanically tested under axial compression. Other similar heated specimen was cooled down to the room temperature and mechanically tested too. This procedure was repeated by temperatures of 200oC, 300oC, 350oC, 400oC, 450oC, 500oC, 600oC, 700oC, 800oC and 1050oC. The heating inside the furnace was controlled in such a way that the average temperature in the furnace followed the standard time-temperature curve, ISO 834 [1]. The experimental results comproved that, due to low temperature resistance, the CFRP systems are not capable of safely and adequately enduring fire for any substantial period of time. This study evaluated a supplementary residual compressive strength of CFRP systems after cooling down to the room temperature. Finally, this study should benefit CFRP-fire research efforts by providing a similitude relationship for the testing of full-scale and small-scale specimens.

scholarly journals Finite Element Modeling of Axially Loaded CFRP-Confined Rectangular Reinforced Concrete Columns

2016 ◽  
Vol 2 (8) ◽  
pp. 414-425 ◽  
Author(s):  
Hamed Akbarpour ◽  
Masoumeh Akbarpour
Keyword(s):  
Compressive Strength ◽  
Ultimate Strength ◽  
Fiber Orientation ◽  
Concrete Strength ◽  
High Strength ◽  
Concrete Columns ◽  
Fiber Reinforced Polymer ◽  
Strength Concrete ◽  
Reinforced Polymer ◽  
Number Of Layers

This paper investigates numerically the behaviour of rectangular RC columns strengthened with carbon fiber reinforced polymer (CFRP) composites under uniaxial loading. For this a reason, a parametric study is conducted and the effects of CFRP layers number, compressive strength of unconfined concrete, and fiber orientation on the behaviour of such columns have been studied. The number of CFRP layers has been changed from one to five layers while the fibers are oriented transversely. Compressive strength of unconfined concrete has been increased from 26 MPa to 45 MPa. In addition, three different fiber orientations are considered. The results show that an increase in the number of CFRP layers would enhance the ultimate strength of specimens. Although increasing the number of layers would not increase the ultimate strength of specimens exponentially, but the rate of strength gain would also decrease. Moreover, it is shown that lateral strains increase as the layer number increases. The effect of unconfined concrete strength on the ultimate strength is less for low strength concrete than high strength concrete. Evaluating the effect of fiber orientation shows that the maximum ultimate strength is obtained from transverse orientation and as the angle of orientation increases, the ultimate strength decreases.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

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