Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network

Sadeghi, Faraz; Yu, Yang; Zhu, Xinqun; Li, Jianchun

doi:10.1016/j.engstruct.2021.112824

Cited by 40 publications

(10 citation statements)

References 49 publications

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“…General Regression Neural Network (GRNN) was proposed by American scholar Specht D.F. in 1991, which is a new type of neural network based on nonlinear regression theory [29,30]. It can discover and continuously approximate the true value according to the implicit relationship in the sample data.…”

Section: General Regression Neural Networkmentioning

confidence: 99%

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Tian

Zhu

et al. 2021

Remote Sensing

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Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure.

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Section: General Regression Neural Networkmentioning

confidence: 99%

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Tian

Zhu

et al. 2021

Remote Sensing

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“…Furthermore, the accuracy of SDI largely depends on the damage index adopted by the VBDI methods. The existing frequency domain indexes, such as natural frequency (Chinka et al, 2021), curvature mode shape (Gomes and Giovani, 2022), flexibility matrix (Huang et al, 2020) and modal strain energy (Huang et al, 2023; Sadeghi et al, 2021) have been widely used in SDI. All of them are based on the structural modal parameters or their extension.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification under ambient temperature variations based on CNN and normalized modal flexibility-autoregressive coefficients hybrid index

Gu,

Xiang,

Luo

et al. 2024

Advances in Structural Engineering

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This study proposes a novel method to identify structural damage considering ambient temperature variations. In this method, the normalized modal flexibility based index (nMFBI) and autoregressive (AR) coefficients are combined to form the nMFBI-AR hybrid index, and convolutional neural networks (CNN) are exploited to locate and quantify the damage. Moreover, the effects of ambient temperature variations and measurement noise are not considered in the training dataset, which is preferable in practical engineering. To verify the effectiveness of the proposed method, firstly, a numerical structure of a simply supported beam is investigated, and the performance of the nMFBI-AR index is evaluated by making a comparison with the nMFBI and AR indexes. Then, the proposed method is further verified by a test model of a three-story frame and a practical engineering example of a continuous rigid frame bridge. The results demonstrate that although the influence of ambient temperature variations and measurement noise are only considered in the test datasets, this approach has the best performance in locating and quantifying structural damage, and the errors are less than 16%, which is promising. In addition, this paper provides a guideline and a new idea for the study of damage index based on time-frequency hybrid information.

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“…The identification of damage in steel–concrete structures by means of modal analysis measurements has been addressed in a few papers. An energy transfer ratio index, Wrobleski et al (2017), and Modal Strain Energy changes, Sadeghi et al (2021), were selected as the features for identifying structural damage. In Xia et al (2007), global and local methods of damage identification are compared highlighting better results for local approaches which compare slab and girder transverse responses.…”

Section: Introductionmentioning

confidence: 99%

Damage identification for steel-concrete composite beams through convolutional neural networks

Bilotta

Morassi

Turco

2023

Journal of Vibration and Control

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The identification of damage in the connection of steel–concrete composite beams is pursued by means of Convolutional Neural Networks. The data used for training the networks are gray level images obtained by converting a set of transmissibility functions of the beam. It is shown how simple Convolutional Neural Networks can be trained for the identification of the damage by evaluating its position and intensity. Data availability is thoroughly discussed highlighting the effects of various factors, among them the number and richness of the images used in the training phase, on the predictive capabilities of the network. In this regard, the results obtained are satisfactory as confirmed also by comparisons with data extracted from an experimental campaign. The problem of the unavoidable presence of modelling errors is also discussed and counteracted.

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Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network

Cited by 40 publications

References 49 publications

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Structural damage identification under ambient temperature variations based on CNN and normalized modal flexibility-autoregressive coefficients hybrid index

Damage identification for steel-concrete composite beams through convolutional neural networks

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