Dynamic and experimental analysis on response of multi-cracked structures carrying transit mass

Parhi, Dayal R.; Jena, Shakti P.

doi:10.1177/1748006x16682605

Cited by 6 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Amezquita-Sanchez et al [28] have conducted a literature survey on the implementation of Artificial Neural Networks in the era of civil engineering for structural system credentials problem. Jena and Parhi [29][30] have carried out numerical along with FEA and experimental studies to determine the responses of different types of beam structures subjected to moving load. Yeang et al [31] developed an algorithm for damage localization in structure subjected to moving vehicle.…”

Section: *Corresponding Authormentioning

confidence: 99%

“…With proper procedures the equation (5) has been simplified and reached to the final solution equation, which has been earlier solved by Jena and Parhi [29][30] is presented below-…”

Section: Problem Formulationmentioning

confidence: 99%

“…The response of the vibrating structure is determined by solving eqn. (6) using Runge-Kutta fourth order rule with proper cares [29][30]. The response of the structure due to the interaction of moving load has been calculated from the solution of equation (6).…”

Section: Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

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%

“…With proper procedures the equation (5) has been simplified and reached to the final solution equation, which has been earlier solved by Jena and Parhi [29][30] is presented below-…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

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

“…Hakim et al [7] have developed a damage identification procedure in the domain ANNs by taking into account an I-beam structure. Jena et al [8][9][10][11] have carried out numerous studies like theoretical, FEA and experimental to determine the responses of different types of structures subjected to transit mass. They have determined the structure's response at various damage and moving load parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The entire damage detection procedure has been carried out in a supervised manner. The numerical, FEA and experimental works which are already validated with each other earlier by the authors [8][9][10] are considered as forward problems, while the modified RNNs analogy is considered as inverse method in the present formulation. The present method has been also compared with author's previous works [12,13].…”

Section: Introductionmentioning

confidence: 99%

On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach

Jena¹,

Parhi

2021

Mechanics & Industry

View full text Add to dashboard Cite

Parameters identification on structure subjected to moving load can be predicted by using the accurate and reliable data. The concepts of recurrent neural networks (RNNs) approach have been used in parameters (crack locations and severities) identifications in structure subjected to moving load in the present methodology. This methodology has incorporated the knowledge based Elman's recurrent neural networks (ERNNs) and Jordan's recurrent neural networks (JRNNs) jointly for the identification of parameters. This approach has been addressed as the inverse problem for predicting the locations and quantification of cracks in the structure in a supervised manner. The Levenberg-Marquardt's back propagation algorithm is implemented to train the proposed networks. To check the robustness of the present method, Numerical studies followed by Finite Element Analysis (FEA) and experimental verifications (Forward problems) are presented as a case study by considering a multi-cracked simply supported structure under a moving mass. The estimated crack locations and severities obtained from the proposed RNNs model converge well with those of FEA and experiments. From the demonstration of the case study, it concludes that the proposed analogy can identify and quantify the crack locations and severities effectively.

show abstract

Parametric Analysis of the Nonlinear Dynamics of a Cracked Cantilever Beam

Hsu¹,

Tien²

2023

Journal of Vibration and Acoustics

View full text Add to dashboard Cite

Structural damage occurs in a variety of civil, mechanical, and aerospace engineering systems, and it is critical to effectively identify such damage in order to prevent catastrophic failures. When cracks are present in a structure, the breathing phenomenon that occurs between crack surfaces typically triggers nonlinearity in the dynamic response. In this work, in order to thoroughly understand the nonlinear effect of cracks on structural dynamics, two modeling approaches are integrated to investigate the crack-induced nonlinear dynamics of cantilever beams. First, a modeling method referred to as the discrete element (DE) method is employed to construct a model of a cracked beam. The DE model is able to characterize the breathing phenomenon of cracks. Next, a simulation technique referred to as the hybrid symbolic-numeric computational (HSNC) method is used to analyze the nonlinear response of the cracked beam. The HSNC method provides an efficient way to evaluate both stationary and non-stationary dynamics of cracked systems since it combines efficient linear techniques with an optimization tool to capture the system's nonlinear response. The proposed computational platform thus enables efficient multiparametric analysis of cracked structures. The effects of crack location, crack depth, and excitation frequency on the cantilever beam are parametrically investigated using the proposed method. Nonlinear features such as sub-harmonic resonance, non-stationary motion, multi-stability, and frequency shift are also discussed in this paper.

show abstract

Dynamic and experimental analysis on response of multi-cracked structures carrying transit mass

Cited by 6 publications

References 19 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

On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach

Parametric Analysis of the Nonlinear Dynamics of a Cracked Cantilever Beam

Contact Info

Product

Resources

About