Multilayer perceptron in damage detection of bridge structures

Pandey, Prafull; Barai, Sudhirkumar V.

doi:10.1016/0045-7949(94)00377-f

Cited by 117 publications

(47 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Szewczyk and Hajela [7] applied an improved counter-propagation neural network to evaluate a reduction in the member sti ness of a frame structure with nine bending elements, by using measured static displacements under prescribed loads. Pandey and Barai [8] detected damage in a bridge truss by applying ANN of multilayer perceptron architectures to numerically simulated data. Using static displacements, natural frequencies and modal shapes, Zhao et al [9] applied a counter-propagation neural network to locate damage in beams and frames.…”

Section: Introductionmentioning

confidence: 99%

A neural network approach for structural identification and diagnosis of a building from seismic response data

Huang

Hung

Wen

et al. 2002

Earthq Engng Struct Dyn

111

View full text Add to dashboard Cite

SUMMARYThis work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back-propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a ÿve-storey steel frame, subjected to di erent strengths of the Kobe earthquake, in shaking table tests.

show abstract

Section: Introductionmentioning

confidence: 99%

A neural network approach for structural identification and diagnosis of a building from seismic response data

Huang

Hung

Wen

et al. 2002

Earthq Engng Struct Dyn

111

View full text Add to dashboard Cite

show abstract

“…The training samples for the RA method are arbitrarily selected from the 81 sample con"gurations for the FF method in Table 2 and are corresponded to samples 3,10,15,20,23,35,52, 58 and 70. The training samples for the HC and LI methods on the other hand are identical to those selected in the previous example as shown in Table 2.…”

Section: Update Of the Materials Properties And The Boundary Conditionmentioning

confidence: 99%

“…Rhim and Lee [9] examined the feasibility of using NN in conjunction with system identi"cation techniques to detect the existence and to identify the characteristics of damage in composite structures. An application of NN in the damage detection of steel bridge structures was studied by Pandey and Barai [10]. An NN-based approach was presented for the detection of changes in the characteristics of structure-unknown systems [11].…”

Section: Introductionmentioning

confidence: 99%

Selection of Training Samples for Model Updating Using Neural Networks

Chang

et al. 2002

Journal of Sound and Vibration

View full text Add to dashboard Cite

“…This technique has caught the interest of most researchers and has today become an essential part of the technology industry, providing a good ground for solving many of the most difficult prediction problems in various areas of engineering applications (Baughman 1995;Guler 2005;Inan et al 2006;Li and Jiao 2002;Moghadassi et al 2009;Mohaghegh 1995;Nascimento et al 2000;Phung and Bouzerdoum 2007;Ü beyli 2009). ANN has also gained vast popularity in solving various Civil Engineering problems (Baughman 1995;Beale and Demuth 2013;Chen et al 1995;Flood and Kartam 1994;Hasancebi and Dumlupınar 2013;Kang and Yoon 1994;Kirkegaard and Rytter 1994;Neaupane and Adhikari 2006;Pandey and Barai 1995;Rafiq et al 2001). Azad et al (2010) proposed the following two-step procedure to predict the residual flexural strength of corroded beams for which the cross-sectional details, material strengths, corrosion activity index I corr T , and diameter of rebar, D were known.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

Imam

Anifowose

Azad

2015

International Journal of Concrete Structures and Materials

View full text Add to dashboard Cite

Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor (C f ) and through the residual strength (M res ). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a C f -based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

show abstract

Multilayer perceptron in damage detection of bridge structures

Cited by 117 publications

References 7 publications

A neural network approach for structural identification and diagnosis of a building from seismic response data

A neural network approach for structural identification and diagnosis of a building from seismic response data

Selection of Training Samples for Model Updating Using Neural Networks

Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

Contact Info

Product

Resources

About