Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

Özbayrak, Ahmet; Ali, Muhammed Kamal; Çıtakoğlu, Hatice

doi:10.1007/s13369-022-07445-6

Cited by 17 publications

(5 citation statements)

References 60 publications

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“…Instead, it evaluates the null hypothesis (H 0 ) by assuming that all samples originate from the same continuous distribution and ranks them accordingly. A similar test has been employed in the past to assess the ML model predictions against measurements in studies conducted by Citakoglu and Demir [71], Özbayrak et al [72], and Zouzou and Citakoglu [73].…”

Section: Comparison Of Machine Learning Modeling Resultsmentioning

confidence: 99%

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Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Tariq,

Uzun,

Deliktaş

et al. 2023

Z Angew Math Mech

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Microbars are one of the important components of microelectromechanical systems. With the recent increase in their applications, the importance of understanding their mechanical response has become an important topic. In this study, for the first time, the mechanical behavior of microbars based on the strain gradient theory is investigated using a machine learning (ML) approach. Four distinct ML models, namely artificial neural network (ANN), support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR), are developed for microbars with clamped boundary conditions. The performance of these models is individually assessed using five different metrics: coefficient of determination (R2), Mean Absolute Error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Nash‐Sutcliffe efficiency coefficient (NSE). The best‐performing model is selected based on these comparisons. Additionally, a semi‐analytical approach is employed to determine the natural frequencies of microbars under general elastic boundary conditions using the Fourier sine series and Stokes' transform. While the R2 value for all four models indicated a good fit of 0.999, the percentage difference in MAE and RMSE values between the training and testing data for DTR and RFR models was relatively higher as compared to ANN and SVR. The results showed that ANN and SVR models exhibit the best performance in predicting the natural frequencies on both training and testing data across all three metrics. Finally, a study on the free axial vibration frequencies of microbar under various effects was conducted.

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Section: Comparison Of Machine Learning Modeling Resultsmentioning

confidence: 99%

“…A similar test has been employed in the past to assess the ML model predictions against measurements in studies conducted by Citakoglu and Demir [71], Özbayrak et al. [72], and Zouzou and Citakoglu [73].…”

Section: Resultsmentioning

confidence: 99%

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Tariq,

Uzun,

Deliktaş

et al. 2023

Z Angew Math Mech

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show abstract

“…Model 1 exhibited the best performance. The article addresses the difficulty of predicting the buckling load in transversely reinforced cantilever beams, a significant issue in structural engineering, thus making a valuable contribution to the field [45].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

İnce,

Taşdemir

2024

Systems

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Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive demand forecasting analysis for retail furniture sales in the U.S. for the next 36 months using Multiple Linear Regression (MLR) and Holt–Winters methods. Leveraging retail sales data from 2019 to 2023, alongside key influencing factors such as furniture imports, consumer sentiment, and housing starts, we developed two predictive models. The results indicated that retail furniture sales exhibited strong seasonality and a positive trend, with the lowest forecasted demand in April 2024 (USD 9118 million) and the highest in December 2026 (USD 13,577 million). The average annual demand for 2024, 2025, and 2026 is projected at USD 12,122.5 million, USD 12,522.67 million, and USD 12,922.17 million, respectively, based on MLR, while Holt–Winters results are slightly more conservative. The models were compared using the Mean Absolute Percentage Error (MAPE) metric, with the MLR model yielding a MAPE of 3.47% and the Holt–Winters model achieving a MAPE of 4.21%. The study’s findings align with global market projections and highlight the growing demand trajectory in the U.S. furniture industry, providing valuable insights for strategic decision-making and operations management.

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“…For instance, Başakın et al [79] employed several ML algorithms to forecast monthly wind speed time series, and after attaining the predictive results, they subjected the ML forecasts and measured wind speed series to the Kruskal-Wallis test to ensure that the models not only provide accurate predictions but are also statistically significant. In a similar vein, Özbayrak et al [82] performed a statistical significance assessment using the Kruskal-Wallis test of the regression estimations and numerical model results, and as a result, they concluded that the null hypothesis was rejected in the corresponding test, meaning that the actual and predicted values had the same distribution. This study subjected the obtained results to additional tests in order to verify whether the configured models provide statistically significant outcomes.…”

Section: Investigating the Statistical Significance Of The Obtained R...mentioning

confidence: 99%

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

Ekmekcioğlu

2023

Water

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The current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and projections were made for different horizons, including short-term (1-month: t + 1), mid-term (3-months: t + 3 and 6-months: t + 6), and long-term (12-months: t + 12) periods. The original sc-PDSI time series was subjected to the partial autocorrelation function to identify the input configurations and, accordingly, one- (t − 1) and two-month (t − 2) lags were used to perform the forecast of the targeted outcomes. This research further incorporated the recently introduced variational mode decomposition (VMD) for signal processing into the predictive model to enhance the accuracy. The proposed model was not only benchmarked with the standalone XGBoost but also with the model generated by its hybridization with the discrete wavelet transform (DWT). The overall results revealed that the VMD-XGBoost model outperformed its counterparts in all lead-time forecasts with NSE values of 0.9778, 0.9405, 0.8476, and 0.6681 for t + 1, t + 3, t + 6, and t + 12, respectively. Transparency of the proposed hybrid model was further ensured by the Mann–Whitney U test, highlighting the results as statistically significant.

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Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

Cited by 17 publications

References 60 publications

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

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