Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model

Wang, Ziyi; Ma, Donghui; Qian, Wei; Wang, Wei; Guo, Xiaodong; Xu, Qingfeng; Huan, Junhong; Gao, Zhongwei

doi:10.1155/2019/7108262

Cited by 5 publications

(8 citation statements)

References 34 publications

(51 reference statements)

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C i is the comprehensive evaluation index of the i -th prediction method, i =1,2,…, M , E ij is the j -th indicator of the i -th method, j =1,2,…, n , and min( E j ) is the minimum value of the j -th indicator in the m -th method. The higher the value of C , the better the prediction effect of the corresponding combined prediction model [ 43 ]. The seven indicator values for the eight sets of data after prediction were substituted into equation ( 9 ) to obtain the indicator C values for each method, as shown in Figure 13 .…”

Section: Comparison and Analysis Of Prediction Results Of Each Modelsmentioning

confidence: 99%

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.

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Section: Comparison and Analysis Of Prediction Results Of Each Modelsmentioning

confidence: 99%

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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“…When the stress wave detection method and drilling resistance detection method are used alone, their detection precision is usually relatively low, and it is difficult to obtain more comprehensive information on internal defects of wooden members (Wang et al 2019). To be more specific, the detected image obtained by the stress wave detection method can intuitively show the decay distribution of the measured section, but the boundary of the image is very fuzzy and the detection results are not accurate enough.…”

Section: Combined Forecasting Model Of Internal Defect Of Timber Comp...mentioning

confidence: 99%

“…To make up for the shortcomings of each method, different detection methods have been combined to analyze quantitatively and qualitatively the internal damage of wood (An et al 2008;Ge et al 2014;Wang and Allison 2008;Ouis and Zerizer 2006;Choi et al 2007). Moreover, there are already several publications reporting on the determination of weights related to the combination of different nondestructive tests (Chang et al 2016a,b;Dai et al 2017;Chang et al 2019;Wang et al 2019). However, these combined forecasting models are often built solely based on specific evaluation criteria, and they only consider objective test data and ignore experts' experience when determining the weights of the combined model.…”

Section: Introductionmentioning

confidence: 99%

Coupling model of fuzzy soft set and Bayesian method to forecast internal defects of ancient wooden structures based on nondestructive test

Wang

et al. 2019

BioRes

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In order to improve the detection precision of internal defect in the ancient wooden structures, defect simulation tests on pine and elm commonly used in ancient buildings were performed by using stress wave detection and drilling resistance detection. Based on detection data, three typical evaluation criteria, which are the information entropy, the correlation coefficient, and residual sum of squares, were selected as a priori information. Combining with the expert’s fuzzy evaluation value, Bayesian formula was used to modify the prior information to determine the weight coefficients of the two detection methods, and a combined prediction model was established. The results show that the combination of subjectivity and objectivity enables the revised weights to more reasonably and accurately reflect the relative importance of each detection method in prediction evaluation, which reduces the forecasting error. Specifically speaking, the mean error of the combined model was reduced by 49.8% and 59.8%, respectively, compared with stress wave detection and drilling resistance detection. Moreover, the five error indicators of this combined forecasting model are the smallest in all methods, indicating the proposed method has a better forecasting effect. It provides an effective application tool for the practice of forecasting the internal defects of wooden components in ancient buildings.

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“…Finally, a sectional image of the test sample image was generated. Wang et al (2019) used a stress wave and impedance meter to detect the internal damage of wood components, and induced ordered weighted averaging (IOWA) operators, induced ordered weighted geometric averaging (IOWGA) operators, and induced ordered weighted harmonic averaging (IOWHA) operators were introduced to establish a combined prediction model to predict the internal damage of ancient wood structures. Yue et al (2019) compared electric resistance tomography and stress wave tomography for decay detection in trees.…”

Section: Introductionmentioning

confidence: 99%

A case-based reasoning method for discriminating damage levels in ancient wood components based on fuzzy similarity priority

Wang

et al. 2021

BioRes

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In order to rapidly identify internal damage levels accurately in ancient wood components, stress wave detection technology was used to perform simulated damage tests on pine specimens. Based on the detected wave velocity data, the diameter of the specimen, the attenuation coefficient, and the ratio of the wave velocities on the four paths were selected as the discriminant factors for identifying the level of internal damage in the specimens. A case-based reasoning method for discriminating internal damage levels in ancient wood components based on fuzzy similarity priority was proposed. A fuzzy similarity priority relationship between the target case and the source case was established. By introducing the idea of variable weights, the weight of each discriminant factor was determined via the “penalize-excitation” variable weight function. The comprehensive similarity sequences between the target case and the source case were obtained. The source case that was most similar to the target case was used to determine the damage level of the target case. The results showed that this method can quickly and accurately identify the damage levels in ancient wood components, which provides a new method for the safe evaluation of ancient wood buildings.

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Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model

Cited by 5 publications

References 34 publications

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Coupling model of fuzzy soft set and Bayesian method to forecast internal defects of ancient wooden structures based on nondestructive test

A case-based reasoning method for discriminating damage levels in ancient wood components based on fuzzy similarity priority

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