Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques

Mansouri, Iman; Ozbakkaloglu, Togay; Kisi, Özgür; Xie, Tianyu

doi:10.1617/s11527-015-0790-4

Cited by 108 publications

(31 citation statements)

References 47 publications

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“…MARS simulates the model by the use of basic functions (BFs). BFs are defined in the form of pairs based on a knot to define an inflection region [20]. MARS generates a linear combination of BFs, which are shown below [19, 21]:…”

Section: Methodsmentioning

confidence: 99%

Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India

Srivastava

Tiwari

Giri

2019

Heliyon

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Solar radiation is a critical requirement for all solar power plants. As it is a time-varying quantity, the power output of any solar power plant is also time variant in nature. Hence, for the prediction of probable electricity generation for a few days in advance, for any solar power plant, forecasting solar radiation a few days into the future is vital. Hourly forecasting for a few days in advance may help a utility or ISO in the bidding process. In this study, 1-day-ahead to 6-day-ahead hourly solar radiation forecasting was been performed using the MARS, CART, M5 and random forest models. The data required for the forecasting were collected from a solar radiation resource setup, commissioned by an autonomous body of the Government of India in Gorakhpur, India. From the results, it was determined that, for the present study, the random forest model provided the best results, whereas the CART model presented the worst results among all four models considered.

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Section: Methodsmentioning

confidence: 99%

Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India

Srivastava

Tiwari

Giri

2019

Heliyon

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“…They concluded that this model performed better than some others. Mansouri et al 17 compared the performance of M5Tree, ANN, ANFIS, and multivariate adaptive regression splines (MARS) in analyzing the axial compression of fiber-reinforced polymer and found that ANN predicted an accurate result. Mostafa and El-Masry 18 used gene expression programming (GEP), ANN, and ARIMA to forecast the evolution of oil prices between January 1986 and June 2016.…”

Section: Comparative Performance Of Ann and Anfismentioning

confidence: 99%

A comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs

Okwu

Adetunji

2018

International Journal of Engineering Business Management

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Most deterministic optimization models use average values of nondeterministic variables as their inputs. It is, therefore, expected that a model that can accept the distribution of a random variable, while this may involve some more computational complexity, would likely produce better results than the model using the average value. Artificial neural network (ANN) is a standard technique for solving complex stochastic problems. In this research, ANN and adaptive neuro-fuzzy inference system (ANFIS) have been implemented for modeling and optimizing product distribution in a multiechelon transshipment system. Two inputs parameters, product demand and unit cost of shipment, are considered nondeterministic in this problem. The solutions of ANFIS and ANN were compared to that of the classical transshipment model. The optimal total cost of distribution using the classical model within the period of investigation was 6,332,304.00. In the search for a better solution, an ANN model was trained, tested, and validated. This approach reduced the cost to 4,170,500.00. ANFIS approach reduced the cost to 4,053,661. This implies that 34% of the current operational cost was saved using the ANN model, while 36% was saved using the ANFIS model. This suggests that the result obtained from the ANFIS model also seems marginally better than that of the ANN. Also, the ANFIS model is capable of adjusting the values of input and output variables and parameters to obtain a more robust solution.

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“…Prediction of bending strength of the SLC from the mix ingredients and fresh properties is a particularly complex question. And literature review shows that previous approaches for the prediction of SLC strength properties do not include sufficient and detailed investigations [22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

Yıldızel¹,

Uzun²,

Çöğürcü³

2019

ijisae

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Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.

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Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques

Cited by 108 publications

References 47 publications

Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India

Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India

A comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

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