Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches

Mansouri, Iman; Kisi, Özgür

doi:10.1016/j.compositesb.2014.11.023

Cited by 69 publications

(27 citation statements)

References 66 publications

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“…Starting from this assumption, several formulations have been proposed so far for estimating the debonding force in concrete [12,40,[69][70][71][72][73]. An overview of the existing bond strength models mainly proposed for concrete, but which can also be used for masonry substrates, is presented in [74,75]; the most common models with the relative references are summarized in Table 1.…”

Section: Bond Strength Modelsmentioning

confidence: 99%

A review on the bond behavior of FRP composites applied on masonry substrates

Bellini

Mazzotti

2017

RILEM Tech Lett

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The need of repairing and retrofitting masonry buildings, typical of Italian and European built heritage, determined a growing interest towards the development of effective and cost efficient innovative strengthening solutions. Among the different techniques currently available, the adoption of composite materials, such as Fiber Reinforced Polymers (FRPs), proved to be one of the most viable solutions for the strengthening of masonry structures. If not properly fixed, a common failure mode of this system is the early debonding of the reinforcement from the substrate. For this reason, several experimental tests and numerical analyses are currently available in the literature, clarifying and predicting the bond behavior of FRP glued to masonry substrates. The purpose of this paper is to provide an overview of the results obtained by the different experimental works, of the numerical analyses performed both for evaluating and validating experimental results and of the simplified analytical models currently available for the prediction of the maximum capacity of the strengthening systems. Results of extended experimental campaigns, carried out also within different Round Robin Test (RRT) series, with several laboratories involved, and recent model calibrations will be discussed together with the approach proposed by the actual guidelines.

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Section: Bond Strength Modelsmentioning

confidence: 99%

A review on the bond behavior of FRP composites applied on masonry substrates

Bellini

Mazzotti

2017

RILEM Tech Lett

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“…NN are mathematical structures enabling the processing of signals by other elements due to the use of certain models which perform input operations. Nowadays we can observe a significant role played by artificial intelligence methods in predicting material parameters or investigating strength of composite materials or failure behavior analyses [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The research on stiffened, buckling-susceptible composite elements used in the aircraft industry, oriented at reducing structure weight, by the finite element method in the Abaqus program and neural networks, was conducted among others by a research team from the Department of Aerospace Science and Technology, Politecnico di Milano [21].…”

Section: Motivationmentioning

confidence: 99%

Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

Golewski

Gajewski

Sadowski

2017

Archives of Metallurgy and Materials

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Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure's geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure's weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the "Bryza" aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM -ANN system with respect to design optimization.

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“…Research has shown the artificial intelligence methods such as ANFIS and ANN to be very successful in civil engineering and especially in water resources analysis applications [7][8][9][10][11][12][13][14][15][16]. ANFIS, for example, was successfully used by Terzi et al [13] to analyze the daily meteorology data of the Lake Egirdir in Turkey.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

Kişi

Mansouri

2017

Advances in Meteorology

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Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.

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Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches

Cited by 69 publications

References 66 publications

A review on the bond behavior of FRP composites applied on masonry substrates

A review on the bond behavior of FRP composites applied on masonry substrates

Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

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