Integrated OVMD-BiGRU-SMAC Framework for Forecasting Construction Accidents in the Kingdom of Saudi Arabia

Construction Accidents (CA) remain a major concern for occupational safety, especially within high-risk environments such as those found across the expanding construction sector in the Kingdom of Saudi Arabia (KSA). This research introduces an innovative prediction framework that combines signal dec...

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Vydané v:	IEEE access Ročník 13; s. 124543 - 124555
Hlavní autori:	Alsulami, Badr T., Khattak, Afaq
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accidents Configurations Construction accidents Construction accidents & safety Construction industry Construction site accidents Decomposition Deep learning Forecasting Image reconstruction Injuries Noise Occupational safety Optimization Predictive models Safety management signal processing time series Time series analysis Time-frequency analysis Saudi Arabia
ISSN:	2169-3536, 2169-3536
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Abstract	Construction Accidents (CA) remain a major concern for occupational safety, especially within high-risk environments such as those found across the expanding construction sector in the Kingdom of Saudi Arabia (KSA). This research introduces an innovative prediction framework that combines signal decomposition with advanced deep learning methods to estimate CA trends. Initially, the framework applies Optimized Variational Mode Decomposition (OVMD) to break down historical CA time series into distinct temporal components known as Intrinsic Mode Functions (IMFs). These IMFs are individually forecasted using Bidirectional Gated Recurrent Unit (BiGRU) models, which are capable of learning sequential patterns in both temporal directions. To enhance the predictive accuracy, the hyperparameters of each BiGRU model are optimized using the Sequential Model-based Algorithm Configuration (SMAC) technique. The proposed framework is trained on monthly CA data in the KSA from June 2010 to March 2023. Among the tested configurations, the proposed OVMD-BiGRU-SMAC model produced the most reliable and better results and achieves RMSE value of 17.26, MAE of 14.02, and R2 of 0.874. In comparison, the OVMD-TCN-SMAC model showed the weakest performance, with an RMSE of 23.93, MAE of 19.11, and R2 of 0.742. These results demonstrate the effectiveness of combining signal decomposition with deep learning techniques in order to caputer the irregular and nonstationary patterns of CA data and provide more reliable forecasts to support safety management and proactive planning efforts.
AbstractList	Construction Accidents (CA) remain a major concern for occupational safety, especially within high-risk environments such as those found across the expanding construction sector in the Kingdom of Saudi Arabia (KSA). This research introduces an innovative prediction framework that combines signal decomposition with advanced deep learning methods to estimate CA trends. Initially, the framework applies Optimized Variational Mode Decomposition (OVMD) to break down historical CA time series into distinct temporal components known as Intrinsic Mode Functions (IMFs). These IMFs are individually forecasted using Bidirectional Gated Recurrent Unit (BiGRU) models, which are capable of learning sequential patterns in both temporal directions. To enhance the predictive accuracy, the hyperparameters of each BiGRU model are optimized using the Sequential Model-based Algorithm Configuration (SMAC) technique. The proposed framework is trained on monthly CA data in the KSA from June 2010 to March 2023. Among the tested configurations, the proposed OVMD–BiGRU–SMAC model produced the most reliable and better results and achieves RMSE value of 17.26, MAE of 14.02, and R2 of 0.874. In comparison, the OVMD–TCN–SMAC model showed the weakest performance, with an RMSE of 23.93, MAE of 19.11, and R2 of 0.742. These results demonstrate the effectiveness of combining signal decomposition with deep learning techniques in order to caputer the irregular and nonstationary patterns of CA data and provide more reliable forecasts to support safety management and proactive planning efforts.
Author	Khattak, Afaq Alsulami, Badr T.
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SubjectTerms	Accidents Configurations Construction accidents Construction accidents & safety Construction industry Construction site accidents Decomposition Deep learning Forecasting Image reconstruction Injuries Noise Occupational safety Optimization Predictive models Safety management signal processing time series Time series analysis Time-frequency analysis
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Title	Integrated OVMD-BiGRU-SMAC Framework for Forecasting Construction Accidents in the Kingdom of Saudi Arabia
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