Fault diagnosis on beam-like structures from modal parameters using artificial neural networks

Hakim, S J S; Razak, Hashim Abdul; Ravanfar, S. A.

doi:10.1016/j.measurement.2015.08.021

Cited by 86 publications

(39 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hakim et al [20] have developed ANNs based approach to localize the position and to quantify the severities of cracks in an I-beam structure. They have considered the first five natural frequencies and mode shapes of the structure as input to the network model.…”

Section: *Corresponding Authormentioning

confidence: 99%

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Jena¹,

Parhi

2019

Scientia Iranica

View full text Add to dashboard Cite

The current work is based on the development of an inverse approach in the domain of Recurrent Neural Networks (RNNs) to identify and quantify cracks on a multi-cracked cantilever beam structure subjected to transit mass. At first, the responses of the multicracked structure subjected to transit load are determined using fourth order Runge-Kutta numerical method and finite element analysis (FEA) has been executed using ANSYS software to authenticate the employed numerical method. The existences and positions of cracks are identified from the measured dynamic excitation of the structure. The crack severities are found out by FEA as forward problem. The modified Elman's Recurrent Neural Networks (ERNNs) approach has been implemented as inverse problem to predict the locations and severities of cracks in the structure by applying Levenberg-Marquardt (LM) back propagation algorithm. The present analogy has been carried out in a supervised manner to check the convergence of the proposed algorithm. The proposed ERNNs method converge good results with those of theory and FEA.

show abstract

Section: *Corresponding Authormentioning

confidence: 99%

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Jena¹,

Parhi

2019

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…Most studies in civil engineering use ANNs for predicting the concrete compressive strength [16][17][18][19][20][21][22][23][24]. Other applications of ANNs include damage detection in beams by utilising vibration measurements [25][26][27][28][29] and the assessment of shear resistance in concrete beams [30][31][32][33][34]. Several researchers have used ANNs to predict Fig.…”

Section: Introductionmentioning

confidence: 99%

Neural-network-based approach to predict the deflection of plain, steel-reinforced, and bamboo-reinforced concrete beams from experimental data

2019

View full text Add to dashboard Cite

The necessity of providing low-cost housing to economically weaker sections of society has been recognised by the national government of India. In mountainous areas, the use of locally available construction material, such as bamboo, as concrete reinforcement has increased due its easy availability and economic benefit. However, due to the inadequate codal provisions for the design and detailing of bamboo-reinforced structures, evaluating the serviceability criteria for their deflection behaviour under different loads is difficult. Furthermore, factors such as bond failure between reinforcement and concrete, shrinkage and corrosion of reinforcing material, and uncertainty in material strength make the prediction of deflection even more cumbersome. This study presents an artificial neural network (ANN)-based method modelled using MATLAB for predicting the deflection behaviour of three types of beams, namely plain, steel-reinforced, and bamboo-reinforced beams. Experimental investigation is conducted to record data at regular load increments for the aforementioned three beam typologies fabricated in the laboratory under two-point loading for 28 days. A total of 122 laboratory test data are recorded for modelling the ANN. The used approach involves predicting the relationship among the applied load, tensile strength of the reinforcement, percentage (amount) of reinforcement (taken as input), and deflection of the beam (obtained as output). The present ANN approach exhibits gives satisfactory performance (coefficient of determination (R 2) = 0.9983 and mean square error = 0.00049) in predicting the deflection behaviour of beams. Hence, the ANN approach can be used as an efficient and robust tool in predicting serviceability behavior of different types of reinforced concrete beams.

show abstract

“…For system identification and health monitoring of structures, the multi‐layer neural networks are widely utilized in the literature (Adeli, ; Adeli & Jiang, ; Arangio & Beck, ; Chang, Lin, & Chang, ; Hakim, Razak, & Ravanfar, ; Lam & Ng, ; Sirca & Adeli, ; Sohn et al., ; Yin & Zhu, ), and currently, deep learning neural networks have also begun to be applied in this area (Abdeljaber, Avci, Kiranyaz, Gabbouj, & Inmand, ; Cha, Choi, & Büyüköztürk, ; Grande, Castillo, Mora, & Lo, ; Lin, Nie, & Ma, ; Gao & Mosalam, ; Wang, Zhao, Li, Zhao, & Zhao, ; Yang et al., ). In this paper, the commonly used multi‐layer feedforward neural networks are investigated, and they have been confirmed to be able to approximate any functional relationship between inputs and outputs with a single hidden layer (Cybenko, ).…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for architecture design of Bayesian neural network in structural model updating

Yin

Zhu

2019

Computer aided Civil Eng

View full text Add to dashboard Cite

There has been growing interest in applying the artificial neural network (ANN) approach in structural system identification and health monitoring. The learning process of neural network can be more robust when presented in the Bayesian framework, and rational architecture of the Bayesian neural network is critical to its performance. Apart from number of hidden neurons, the specific forms of the transfer functions in both hidden and output layers are also crucially important. To the best of our knowledge, however, the simultaneous design of proper number of hidden neurons, and specific forms of hidden‐ and output‐layer transfer functions has not yet been reported in terms of the Bayesian neural network. It is even more challenging when the transfer functions of both layers are parameterized instead of using fixed shape forms. This paper proposes a tailor‐made algorithm for efficiently designing the appropriate architecture of Bayesian neural network with simultaneously optimized hidden neuron number and custom transfer functions in both hidden and output layers. To cooperate with the proposed algorithm, both the Jacobian of the network function and Hessian of the negative logarithm of weight posterior are derived analytically by matrix calculus. This is much more accurate and efficient than the finite difference approximation, and also vital for properly designing the Bayesian neural network architecture as well as further quantifying the confidence interval of network prediction. The validity and efficiency of the proposed methodology is verified through probabilistic finite element (FE) model updating of a pedestrian bridge by using the field measurement data.

show abstract

Fault diagnosis on beam-like structures from modal parameters using artificial neural networks

Cited by 86 publications

References 18 publications

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Neural-network-based approach to predict the deflection of plain, steel-reinforced, and bamboo-reinforced concrete beams from experimental data

An efficient algorithm for architecture design of Bayesian neural network in structural model updating

Contact Info

Product

Resources

About