Damage Identification Method of Beam Structure Based on Modal Curvature Utility Information Entropy

Xiang, Changsheng; Li, Lingyun; Zhou, Yu; Ye, Zi

doi:10.1155/2020/8892686

Cited by 9 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The damage degree is greater than 40%, the increase of the mutation value of the index increases rapidly. It can be inferred that the structural components at the damage position have entered the plastic failure stage [35,36]. Due to the measurement error in the measurement process, the fitted DSF value has a negative value in a healthy state.…”

Section: Resultsmentioning

confidence: 99%

Archives of Civil Engineering

Lu,

Dong,

Pan

et al. 2022

View full text Add to dashboard Cite

Time series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. EMD decomposes nonlinear and non-stationary signals into different intrinsic mode functions (IMFs) according to frequency. IMF reduces the complexity of the signal and makes it easier to extract damage-sensitive features (DSF). The ARIMA model is used to extract damage sensitive features in IMF signals. The damage sensitive characteristic value of each node is used to analyze the location and damage degree of the damaged structure of the bridge. Considering that there are usually multiple failures in the actual engineering structure, this paper focuses on analysing the location and damage degree of multi-damaged bridge structures. A 6-meter-long multi-destructive steel-whole vibration experiment proved the state of the method. Meanwhile, the other two damage identification methods are compared. The results demonstrate that the DSF can effectively identify the damage location of the structure, and the accuracy rate has increased by 22.98% and 18.4% on average respectively.

show abstract

Section: Resultsmentioning

confidence: 99%

Archives of Civil Engineering

Lu,

Dong,

Pan

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The curvature damage index has been proven to be capable of highlighting the location of single and multiple damages at distinct areas of the beam [13]. The modal curvature utility information entropy index is provided which is based on the information entropy theory and combines the modal curvature index in beam structure damage diagnosis [14]. The vibration‐based technique is utilized with damage prediction methods such as strain energy damage index, model flexibility, and model curvature to identify the damage in structure [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of crack locations in beam using artificial neural network‐based modified curvature damage index

Gupta

Das

2023

Materialwissenschaft Werkst

View full text Add to dashboard Cite

Although the frequency response‐curvature methodology is commonly used to detect irregularities in mechanical and civil structures, the artificial neural network‐based frequency response‐curvature damage index method may have good efficacy in the detection and localization of structural damages. By utilizing experimental data sets, a novel method is proposed to pinpoint a saw‐cut damage location and the degree of damage in beam models. Using a dynamic data logger, the frequency response function of a beam model is obtained for altering damage levels at different positions. As frequency response data contains environmental and operational noise, the accuracy of obtained results may get reduced. To improve the accuracy by reducing the noise effect, the experimentally obtained frequency response data is trained through an artificial neural network. Using central difference approximation, the sets of trained modal data are utilized to determine the improved mode shape curvature. The curvature damage index is then obtained by using the improved mode shape curvature for different damaged scenarios to ultimately identify structural damages.

show abstract

“…He et al [18] demonstrated how the curvature mode difference index, when combined with finite element simulation and testing, can be used to determine the location and degree of structural damage in cantilever beams. Xiang et al [19] proposed an information entropy based on curvature modal utility as a damage identification indicator; tests and numerical simulations on supported beams are utilized to verify the validity of the index.…”

Section: Introductionmentioning

confidence: 99%

Wooden Beams Modal Damage Identification Parameters Based on Strain

Cao,

Meng,

Gao

et al. 2023

Buildings

View full text Add to dashboard Cite

This study completely investigates the damage detection problem based on strain modal characteristics using simply supported wooden beams as the research object. First, modal analysis was performed, and finite element models of both damaged and undamaged timber beams were created using ABAQUS. Second, a number of strain modal characteristics (strain modal, strain modal difference, and strain modal curvature difference) were used to identify the damage to the wooden beam construction. Lastly, the modal parameters of the wooden beam under various damage scenarios were ascertained using damage identification experiments, and the test results were contrasted with those obtained from a numerical simulation. The findings showed that, while each of the three damage identification indices has a distinct identification effect, they can all locate the damage to wooden beams. Strain mode curvature difference > strain mode difference > strain mode is the order of them. The results of this study can be used as a reference to identify damage to timber beams in the future.

show abstract

Damage Identification Method of Beam Structure Based on Modal Curvature Utility Information Entropy

Cited by 9 publications

References 30 publications

Archives of Civil Engineering

Archives of Civil Engineering

Evaluation of crack locations in beam using artificial neural network‐based modified curvature damage index

Wooden Beams Modal Damage Identification Parameters Based on Strain

Contact Info

Product

Resources

About