scholarly journals A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural Network

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
K. Sumangala ◽  
C. Antony Jeyasehar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prestressed Concrete ◽  
Damage Assessment ◽  
Concrete Beams ◽  
Assessment Procedure ◽  
Dynamic Tests ◽  
Static And Dynamic Tests ◽  
Artificial Neural ◽  
Trained Network

A damage assessment procedure has been developed using artificial neural network (ANN) for prestressed concrete beams. The methodology had been formulated using the results obtained from an experimental study conducted in the laboratory. Prestressed concrete (PSC) rectangular beams were cast, and pitting corrosion was introduced in the prestressing wires and was allowed to be snapped using accelerated corrosion process. Both static and dynamic tests were conducted to study the behaviour of perfect and damaged beams. The measured output from both static and dynamic tests was taken as input to train the neural network. Back propagation network was chosen for this purpose, which was written using the programming package MATLAB. The trained network was tested using separate test data obtained from the tests. A damage assessment procedure was developed using the trained network, it was validated using the data available in literature, and the outcome is presented in this paper.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

scholarly journals Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

2019 ◽  
Vol 255 ◽  
pp. 02010
Author(s):  
Fakharudin Abdul Sahli ◽  
Zainol Norazwina ◽  
Dzulkefli Noor Athirah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mathematical Modelling ◽  
Nitrogen Concentration ◽  
Network Modelling ◽  
Connection Weight ◽  
Palm Fruit ◽  
Cultivation Process ◽  
Artificial Neural ◽  
Trained Network

Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.

A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement

2005 ◽  
Vol 32 (4) ◽  
pp. 644-657 ◽  
Author(s):  
Ayman Ahmed Seleemah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Experimental Work ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Transverse Reinforcement ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Different relationships have been proposed by codes and researchers for predicting the shear capacity of members without transverse reinforcement. In this paper, the applicability of the artificial neural network (ANN) technique as an analytical alternative to existing methods for predicting this shear capacity is investigated using a critically reviewed and agreed upon database of experimental work that serves as a basis of comparison and (or) assessment of existing and new relationships. Both ANN and eight different codes and researcher's predictions of the shear capacity of the specimens of the database were compared. The ANN predictions are much superior to those of any of the current available relationships.Key words: artificial neural networks, shear capacity, transverse reinforcement, beams.

Long Span Prestressed Concrete Continuous Rigid-Frame Bridge ANN Construction Control

2012 ◽  
Vol 204-208 ◽  
pp. 2261-2264
Author(s):  
Geng Feng Ren ◽  
Cun Jun Zou ◽  
Yue Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prestressed Concrete ◽  
Construction Control ◽  
Bridge Construction ◽  
Long Span ◽  
Continuous Rigid Frame Bridge ◽  
Rigid Frame ◽  
Artificial Neural ◽  
Rigid Frame Bridge

Based on the theory of ANN (Artificial Neural Network),The paper raised the method of construction control, and introduced the common forecasting method. According to the characteristic of ANN itself and the complexity of factors which influence the elevation, the paper analysed the influence aspects of ANN. On the promise of bridge construction precision, the paper raised section measure、elasticity model、temperature、delay of construction and cantilever for neural network’s input vector in bridge construction process. With the help of Graphical User Interface, built ANN, made the forecast function in the bridge construction into reality. Introduce the theory of Artificial Neural Network(ANN) into long span prestressed concrete continuous rigid-frame bridge construction control.

scholarly journals Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3364
Author(s):  
Dan Yue ◽  
Yihao He ◽  
Yushuang Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Detection ◽  
Back Propagation ◽  
Good Precision ◽  
Modulation Transfer ◽  
Detection Range ◽  
Bp Artificial Neural Network ◽  
Artificial Neural ◽  
Trained Network

A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.

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