Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models

Özcan, Gıyasettin; Koçak, Yılmaz; Gülbandılar, Eyyup

doi:10.12989/cac.2017.19.3.275

Cited by 31 publications

(17 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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“…Günümüzde birçok alanda olduğu gibi inşaat mühendisliği uygulamalarında çeşitli problemleri çözmek için, bulanık mantık, yapay sinir ağları, makine öğrenmesi gibi yapay zeka tabanlı uygulamalar yaygın ve etkin olarak kullanılmaktadır [1][2][3][4][5][6][7][8][9][10][11]. Uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) de bu uygulamalardan biri olarak oldukça güvenilir bir yöntem olarak ifade edilmekte ve bir çok araştırmacı tarafından oldukça yaygın olarak kullanılmaktadır.…”

Section: Introductionunclassified

Pomza ve Diatomitin Portland Çimentosunun Basınç Dayanımına Etkilerinin ANFIS ile Tahmini

Güvenç

Koçak

2022

Eskişehir Türk Dünyası Uygulama Ve Araştırma Merkezi Bilişim Dergisi

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Özet: Bu çalışmada, çimento harçlarının basınç dayanımını tahmin etmek için uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) kullanılarak bir tahmin modeli geliştirilmiştir. Bu amaçla yapılan çalışmada, Portland çimentosu (PÇ) ile içerisine pomza (%10-20), diatomit (%10-20) ve pomza + diatomit (%5+5-%10+10) ikame edilmiş olan toplam yedi farklı çimento ile elde edilen harçların 2, 7, 28, 90. hidratasyon günlerindeki basınç dayanımları standart çimento deneyleriyle belirlenmiştir. Deneyler sonucu elde edilen 168 veri eğitim için, bu deney sonuçlarının ortalamaları olan 28 veri de test için kullanılmıştır. ANFIS modelinde eğitim ve test aşamalarında hidratasyon günü, Portland çimento, pomza, diatomit ve su olarak 5 giriş parametresi ve çimento harçlarının basınç dayanımı olmak üzere 1 çıkış parametresi kullanılmıştır. Deneylerden elde edilen ve modelden elde edilen sonuçlarının kıyaslanmasında R 2 , MAPE ve RMSE olmak üzere üç farklı istatiksel yöntem kullanılmıştır. Elde edilen veriler, deney sonuçları ile ANFIS sonuçları arasında uyumun iyi olduğunu ve inşaat mühendisliğindeki uygulamalarda başarıyla uygulanabilirliğini göstermiştir.

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“…The same method was developed for the shear strength [9]. Ozcan et al [10] investigated the effect of waste tire rubber powder and blast furnace slag on the CS of cement mortars using Ada boost and SVM models.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

Moayedi¹,

Mosavi²

2021

Preprint

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Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates artificial neural network (ANN) with a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

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Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models

Cited by 31 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

Pomza ve Diatomitin Portland Çimentosunun Basınç Dayanımına Etkilerinin ANFIS ile Tahmini

Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

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