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Damage identification of bridge structure model based on empirical mode decomposition algorithm and Autoregressive Integrated Moving Average procedure

Lu Weijia, Dong Jiafan, Pan Yuheng, Li Guoya, Guo Jinpeng

Archives of Civil Engineering

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2022

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Vol. 68, nr 4

653--667

EN

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.

2

The influence of rainfall time series fractality on forecasting models’ efficiency

Rahmani Farhang, Fattahi Mohammad Hadi

Acta Geophysica

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2022

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Vol. 70, no 3

1349--1361

EN

Forecasting rainfall time series is of great significance for hydrologists and geoscientists. Thus, this study represents a contribution to understanding the impact of the fractal time series variety on forecasting model performance. Multiple fractal series were generated via p-model and used for modeling. Subsequently, the forecasting was delivered based on existing observed monthly rainfall data (three stations in the UK, from 1865 to 2002) through five forecasting models. Finally, the association between series fractality and models’ performance was examined. The results indicated that the forecasting based on the mono-fractal series resulted in the most reliable results (R2=1 and RMSE less than 0.02). In the case of multifractal series, modeling based on series with the right side of the asymmetric curve of the multifractal spectrum presented series with the lowest RMSE (0.96) and highest R2 (0.99) (desirable performance). In contrast, the forecasting based on series with the left side of the asymmetric curve of the multifractal spectrum suggested the most unreliable outcomes (R2 range [−0.0007 ~ 0.988] and RMSE range [0.8526 ~ 39.3]). The forecasting based on the symmetric curve of the multifractal spectrum series delivered regular performance. Accordingly, high and low errors are expected from forecasting based on the time series with a left-skewed multifractal spectrum and right-skewed multifractal spectrum (and mono-fractal time series), respectively. Hybrid models were the best options for forecasting mono-fractal and multifractal time series with right side asymmetric and symmetric multifractal spectrum curves. The ARIMA model was suitable to predict multifractal time series with left side asymmetric multifractal spectrum curves.

3

Assessing the representative elementary volume of rock types by X-ray computed tomography (CT) – a simple approach to demonstrate the heterogeneity of the Boda Claystone Formation in Hungary

Abutaha Saja M., Geiger János, Gulyás Sándor, Fedor Ferenc

Geologos

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2021

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Vol. 27, No. 3

157--172

EN

X-ray computed tomography (CT) can reveal internal, three-dimensional details of objects in a non-destructive way and provide high-resolution, quantitative data in the form of CT numbers. The sensitivity of the CT number to changes in material density means that it may be used to identify lithology changes within cores of sedimentary rocks. The present pilot study confirms the use of Representative Elementary Volume (REV) to quantify inhomogeneity of CT densities of rock constituents of the Boda Claystone Formation. Thirty-two layers, 2 m core length, of this formation were studied. Based on the dominant rock-forming constituent, two rock types could be defined, i.e., clayey siltstone (20 layers) and fine siltstone (12 layers). Eleven of these layers (clayey siltstone and fine siltstone) showed sedimentary features such as, convolute laminations, desiccation cracks, cross-laminations and cracks. The application of the Autoregressive Integrated Moving Averages, Statistical Process Control (ARIMA SPC) method to define Representative Elementary Volume (REV) of CT densities (Hounsfield unit values) affirmed the following results: i) the highest REV values corresponded to the presence of sedimentary structures or high ratios of siltstone constituents (> 60%). ii) the REV average of the clayey siltstone was (5.86 cm3) and (6.54 cm3) of the fine siltstone. iii) normalised REV percentages of the clayey siltstone and fine siltstone, on the scale of the core volume studied were 19.88% and 22.84%; respectively. iv) whenever the corresponding layer did not reveal any sedimentary structure, the normalised REV values would be below 10%. The internal void space in layers with sedimentary features might explain the marked textural heterogeneity and elevated REV values. The drying process of the core sample might also have played a significant role in increasing erroneous pore proportions by volume reducation of clay minerals, particularly within sedimentary structures, where authigenic clay and carbonate cement were presumed to be dominant.

4

A combined method for wind power generation forecasting

Le Tuan-Ho

Archives of Electrical Engineering

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2021

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Vol. 70, nr 4

991--1009

EN

Most of the existing statistical forecasting methods utilize the historical values of wind power to provide wind power generation prediction. However, several factors including wind speed, nacelle position, pitch angle, and ambient temperature can also be used to predict wind power generation. In this study, a wind farm including 6 turbines (capacity of 3.5 MW per turbine) with a height of 114 meters, 132-meter rotor diameter is considered. The time-series data is collected at 10-minute intervals from the SCADA system. One period from January 04th, 2021 to January 08th, 2021 measured from the wind turbine generator 06 is investigated. One period from January 01st, 2021 to January 31st, 2021 collected from the wind turbine generator 02 is investigated. Therefore, the primary objective of this paper is to propose a combined method for wind power generation forecasting. Firstly, response surface methodology is proposed as an alternative wind power forecasting method. This methodology can provide wind power prediction by considering the relationship between wind power and input factors. Secondly, the conventional statistical forecasting methods consisting of autoregressive integrated moving average and exponential smoothing methods are used to predict wind power time series. Thirdly, response surface methodology is combined with autoregressive integrated moving average or exponential smoothing methods in wind power forecasting. Finally, the two above periods are performed in order to demonstrate the efficiency of the combined methods in terms of mean absolute percent error and directional statistics in this study.

5

To predict military spending in China based on ARIMA and artificial neural networks models

Kuo K.-C., Chang C.-Y., Lin W.-C.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 3b

176--181

EN

This study takes the initiative to forecast China’s military spending based on autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANNs) models. The mean absolute percentage error (MAPE) approach is applied to measure prediction accuracy. The results indicate that these single variable ARIMA models show higher accuracy and stability than those made by the single variable ANNs models across the four time periods, namely the short term (1 year), the medium term (3 years), the medium-long term (5 years), and the long term (10 years). As to multiple variable ANNs models, the prediction accuracy of each model with different variables has advantages in different time periods. The highest accuracy for the long term predictions among all of the multivariate models is made by ANN2 including China’s military spending and GDP. ANN3 including variables of China’s military spending, GDP, and inflation rates illustrates the most accurate prediction for the short term and medium-long term, while ANN4 including China’s military spending, GDP, inflation rates, and Taiwan’s military spending shows the highest accuracy for the medium term prediction. This concludes the contributions of this study.

PL

W artykule przedstawiono wyniki analizy dotyczącej przewidywanych wydatków Chin na militaria, opracowanej na podstawie modelu autoregresji (ang. ARIMA) oraz sztucznych sieci neuronowych (ANN). Dokładność predykcji oparta została na funkcji średniej wartości absolutnej procentowego uchybu. Badania wykazują, że model ARIMA ma wyższą dokładność i stabilność niż model oparty na ANN w odniesieniu do czterech, różnych okresów (1, 3, 5, 10 lat), przy czym dla ANN badanie wykonano dla czterech wartości dokładności predykcji.