A novel structural damage detection method using a hybrid IDE–BP model

Mei, Jiangtao; Wu, Lei; Chen, Erqi; Xiao, Wensheng; Liu, Zhong; Guo, Jingjing; Li, Wentao

doi:10.1016/j.knosys.2023.110606

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…Ahmadi-Nedushan et al [130] proposed a two-stage structural method for detecting damage that utilized the modal flexibility, the strain energy, and a modified teaching-learning optimization algorithm. Mei et al [131] combined the improved differential evolution algorithm (IDE) and back-propagation ANN to identify structural damage, which further improved the accuracy of assessments of damage by the traditional back-propagation neural network.…”

Section: Combinations Of Flexibility Methods and Other Methodsmentioning

confidence: 99%

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Peng,

Yang,

Qin

et al. 2023

Coatings

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Material damage in structures must be detected in a timely manner to prevent engineering accidents. Damage detection based on structural flexibility has attracted widespread attention in recent years due to its simplicity and practicality. This article provides a detailed overview of damage detection methods based on structural flexibility. Depending on the calculation method and data used, flexibility-based methods can be divided into the following categories: flexibility difference, flexibility derivative index, flexibility sensitivity, flexibility decomposition, static flexibility, and combinations of flexibility with other methods. The basic principles and main calculation formulas of various flexibility methods are explained, and their advantages and disadvantages are analyzed. For the method using flexibility difference, the advantage is that the calculation is very simple and does not require the construction of a finite element model of the structure. The disadvantage is that it requires the measurement of modal data of the intact structure, and this method cannot quantitatively assess the degree of damage. For the method using the flexibility derivative index, the advantage is that it only requires the modal data of the damaged structure to locate the damage, but this method is particularly sensitive to noise in the data and is prone to misjudgment. For methods based on flexibility sensitivity and flexibility decomposition, the advantage is that they can simultaneously obtain the location and degree of damage in the structure, but the disadvantage is that they require the establishment of accurate finite element models in advance. Static flexibility methods can compensate for the shortcomings of dynamic flexibility methods, but they usually affect the normal use of the structure during static testing. Combining flexibility-based methods with advanced intelligent algorithms and other methods can further improve their accuracy and efficiency in identifying structural damage. Finally, this article discusses the challenges that have not yet been solved among damage detection methods based on structural flexibility.

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Section: Combinations Of Flexibility Methods and Other Methodsmentioning

confidence: 99%

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Peng,

Yang,

Qin

et al. 2023

Coatings

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“…Thus, the computational complexity of initial step is O ((N s + 1)m) 2 n + ((N s + 1)m) 3 . In the iterative process, the computational complexity of H f is O (N s + 1)mn 2 . For P M , H −1 f [S] T {P} leads to O (N s + 1)mn 2 + (N s + 1)n costs.…”

Section: Costs and The Computational Complexity Of [S] T [S][s]mentioning

confidence: 99%

“…Engineering structures are often affected by various factors in their service process, such as natural loads and artificial loads, which may lead to structural damage. Long-time structural damage may even lead to structural collapse, which leads to economic losses and casualties [1,2]. In order to avoid all kinds of disasters caused by structural damage, timely and effective structural damage identification is pretty necessary.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Structural Damage Identification Method with a Generic Non-Convex Penalty

Li,

Yi,

Wang

et al. 2024

Mathematics

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Due to the advantage that the non-convex penalty accurately characterizes the sparsity of structural damage, various models based on non-convex penalties have been effectively utilized to the field of structural damage identification. However, these models generally ignore the influence of the uncertainty on the damage identification, which inevitably reduces the accuracy of damage identification. To improve the damage identification accuracy, a probabilistic structural damage identification method with a generic non-convex penalty is proposed, where the uncertainty corresponding to each mode is quantified using the separate Gaussian distribution. The proposed model is estimated via the iteratively reweighted least squares optimization algorithm according to the maximum likelihood principle. The numerical and experimental results illustrate that the proposed method improves the damage identification accuracy by 3.98% and 7.25% compared to the original model, respectively.

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“…BP neural networks have been used for the prediction of hot air drying characteristics of wheat [17], microwave vacuum drying characteristics of carrots [18], prediction of polysaccharides in rhizomes of Atractylodis macrocephaly [19], prediction of flavor changes in the drying process of ginger [20], and prediction of infrared drying of broccoli moisture ratio [21]. Although BP neural networks have many advantages, they also have some limitations, such as easy overfitting, sensitivity to initial weight selection, and the need for a large amount of training data [22]. In practical applications, it is necessary to choose the appropriate network structure and learning algorithm according to the specific problem's needs and the data's characteristics and carry out appropriate tuning and optimization [23].…”

Section: Introductionmentioning

confidence: 99%

Quality and Process Optimization of Infrared Combined Hot Air Drying of Yam Slices Based on BP Neural Network and Gray Wolf Algorithm

Zhang,

Zheng,

Xiao

et al. 2024

Foods

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In this paper, the effects on drying time (Y1), the color difference (Y2), unit energy consumption (Y3), polysaccharide content (Y4), rehydration ratio (Y5), and allantoin content (Y6) of yam slices were investigated under different drying temperatures (50–70 °C), slice thicknesses (2–10 mm), and radiation distances (80–160 mm). The optimal drying conditions were determined by applying the BP neural network wolf algorithm (GWO) model based on response surface methodology (RMS). All the above indices were significantly affected by drying conditions (p < 0.05). The drying rate and effective water diffusion coefficient of yam slices accelerated with increasing temperature and decreasing slice thickness and radiation distance. The selection of lower temperature and slice thickness helped reduce the energy consumption and color difference. The polysaccharide content increased and then decreased with drying temperature, slice thickness, and radiation distance, and it was highest at 60 °C, 6 mm, and 120 mm. At 60 °C, lower slice thickness and radiation distance favored the retention of allantoin content. Under the given constraints (minimization of drying time, unit energy consumption, color difference, and maximization of rehydration ratio, polysaccharide content, and allantoin content), BP-GWO was found to have higher coefficients of determination (R2 = 0.9919 to 0.9983) and lower RMSEs (reduced by 61.34% to 80.03%) than RMS. Multi-objective optimization of BP-GWO was carried out to obtain the optimal drying conditions, as follows: temperature 63.57 °C, slice thickness 4.27 mm, radiation distance 91.39 mm, corresponding to the optimal indices, as follows: Y1 = 133.71 min, Y2 = 7.26, Y3 = 8.54 kJ·h·kg−1, Y4 = 20.73 mg/g, Y5 = 2.84 kg/kg, and Y6 = 3.69 μg/g. In the experimental verification of the prediction results, the relative error between the actual and predicted values was less than 5%, proving the model’s reliability for other materials in the drying technology process research to provide a reference.

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A novel structural damage detection method using a hybrid IDE–BP model

Cited by 7 publications

References 51 publications

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

A Probabilistic Structural Damage Identification Method with a Generic Non-Convex Penalty

Quality and Process Optimization of Infrared Combined Hot Air Drying of Yam Slices Based on BP Neural Network and Gray Wolf Algorithm

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