Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks

2013 ◽  
Vol 119 ◽  
pp. 1-11 ◽  
Author(s):  
Oğuzhan Hasançebi ◽  
Taha Dumlupınar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Nonlinear Model ◽  
Model Updating ◽  
Linear And Nonlinear ◽  
Artificial Neural ◽  
T Beam

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

2020 ◽  
Vol 234 (21) ◽  
pp. 4288-4304
Author(s):  
Ali Mardanshahi ◽  
Masoud Mardanshahi ◽  
Ahmad Izadi
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Finite Element Model ◽  
Model Updating ◽  
Sandwich Structures ◽  
Element Model ◽  
Foam Core ◽  
Damage Quantification ◽  
Artificial Neural

The main idea of this paper is to propose a nondestructive evaluation (NDE) system for two types of damages, core cracking and skin/core debonding, in fiberglass/foam core sandwich structures based on the inverse eigensensitivity-based finite element model updating using the modal test results, and the artificial neural networks. First, the modal testing was conducted on the fabricated fiberglass/foam core sandwich specimens, in the intact and damaged states, and the natural frequencies were extracted. Finite element modeling and inverse eigensensitivity-based model updating of the intact and damaged sandwich structures were conducted and the parameters of the models were identified. Afterward, the updated finite element models were employed to generate a large dataset of the first five harmonic frequencies of the damaged sandwich structures with different damage sizes and locations. This dataset was adopted to train the machine learning models for detection, localization, and size estimation of the core cracking and skin/core debonding damages. A multilayer perceptron neural network classification model was used for detection of types of damages and also a multilayer perceptron neural network regression model was fitted to the dataset for automatically estimation of the locations and dimensions of damages. This intelligent system of damage quantification was also used to make predictions on real damaged specimens not seen by the system. The results indicated that the extracted natural frequencies from the accurate finite element model, in coordination with the experimental data, and using the artificial neural networks can provide an effective system for nondestructive evaluation of foam core sandwich structures.

Model Updating of a Steel Truss Based on Artificial Neural Networks

2011 ◽  
Vol 121-126 ◽  
pp. 1363-1366
Author(s):  
Shi Lei Zhang ◽  
Shao Feng Chen ◽  
Huan Ding Wang ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Model Updating ◽  
Back Propagation ◽  
Element Model ◽  
Steel Truss ◽  
Artificial Neural ◽  
The Finite Element Model

Based on the artificial neural network, the parameters of a steel truss are identified. And the finite element model of truss is corrected. In order to improve the efficiency of model updating by artificial neural networks, the momentum is introduced into the back propagation algorithm. Based on the theory of probability and mathematical statistics, the expectation confidence interval of the measured deflections and strains is obtained. In this way, the samples to train the neural network are optimized. The numerical results show that the back propagation neural network proposed on this paper is able to correct the finite element model of the truss effectively.

scholarly journals Red Neuronal Artificial para estimar la resistencia a compresión, en concretos fibro-reforzados con polipropileno [Artificial neural network to predict the compressive strength, in polypropylene fiber-reinforced concrete]

2012 ◽  
Author(s):  
Luis Octavio González Salcedo ◽  
Aydee Patricia Guerrero Zúñiga ◽  
Silvio Delvasto Arjona ◽  
Adrián Luis Ernesto Will
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Polypropylene Fiber ◽  
Stability Control ◽  
Fiber Reinforced Concrete ◽  
Fiber Reinforced ◽  
Artificial Neural ◽  
Polypropylene Fiber Reinforced Concrete

Resumen En diseño y construcción de estructuras de concreto, la resistencia a compresión lograda a los 28 días, es la especificación de control de estabilidad de la obra. La inclusión de fibras como reforzamiento de la matriz cementicia, ha permitido una ganancia en sus propiedades, además de la obtención de un material de alto desempeño; sin embargo, la resistencia a compresión sigue siendo la especificación a cumplir en la normatividad de la construcción. Las redes neuronales artificiales, como un símil de las neuronas biológicas, han sido utilizadas como herramientas de predicción de la resistencia a compresión en el concreto sin fibra. Los antecedentes en este uso, muestran que es de interés el desarrollo de aplicaciones en los concretos reforzados con fibras. En el presente trabajo, redes neuronales artificiales han sido elaboradas para predecir la resistencia a compresión en concretos reforzados con fibras de polipropileno. Los resultados de los indicadores de desempeño muestran que las redes neuronales artificiales elaboradas pueden realizar una aproximación adecuada al valor real de la propiedad mecánica, abriendo una futura e interesante agenda de investigación. Palabras ClavesResistencia a compresión; concreto reforzado con fibras; fibra de polipropileno; predicción; inteligencia artificial; redes neuronales artificiales.   Abstract In concrete structures’ design and construction, the compressive strength achieved at 28 days, is the work’s stability control specification. The inclusion of reinforcing fibers into the cementicious matrix, has allowed a gain in their properties, as well as obtaining a high performance material, however, the compressive strength remains the specification to meet the construction regulations. Artificial neural networks as a biological neurons’ simile have been used as tools for predicting the plain concrete compressive strength. The backgrounds in this application show that interest is the development of applications in fiber-reinforced concrete. In this paper, artificial neural networks have been developed to predict the compressive strength in polypropylene fiber reinforced concrete. The results of the performance indicators show that the developed artificial neural networks can perform an adequate approximation to the actual value of the mechanical property, opening an interesting future research.KeywordsCompressive strength, fiber-reinforced concrete, polypropylene fiber, prediction, artificial intelligence, artificial neural networks.

scholarly journals Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models

2021 ◽  
Vol 10 (5) ◽  
pp. 293
Author(s):  
Blerina Vika ◽  
Ilir Vika
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Smooth Transition ◽  
Parameter Method ◽  
Economic Time Series ◽  
Nonlinear Methods ◽  
Linear And Nonlinear ◽  
Artificial Neural

Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework.   Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021

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