A GENERAL DEEP LEARNING APPROACH FOR TIME- AND SPACE-VARIANT SYSTEM RELIABILITY-BASED DESIGN ANALYSIS

Reliability-based design analysis plays a crucial role in ensuring the safety and performance of complex systems operating in time- and space-variant environments. Traditional approaches for system reliability analysis often rely on simplified assumptions and do not adequately capture the dynamic nature of real-world systems. In this paper, we propose a general deep learning approach for time- and spacevariant system reliability-based design analysis. The proposed approach leverages the power of deep learning techniques to learn complex relationships between system inputs, environmental conditions, and system reliability. By utilizing deep neural networks, the model can capture intricate patterns and dependencies, enabling accurate predictions of system reliability under varying operating conditions. We develop a comprehensive dataset that incorporates a wide range of system inputs and corresponding reliability data obtained from real-world scenarios. The dataset includes time-dependent and spatially varying parameters to reflect the variability in system behavior. Through careful pre-processing and feature engineering, we ensure the representation of critical system characteristics and environmental factors. The deep learning model is trained using the dataset, employing advanced optimization algorithms and regularization techniques to enhance model generalization and prevent overfitting. We demonstrate the effectiveness of our approach by conducting extensive experiments and comparing the results with traditional reliability analysis methods. Deep learning approach outperforms conventional techniques by providing more accurate and reliable predictions of system reliability. Moreover, the model's ability to adapt to varying time and space conditions allows for a comprehensive assessment of system performance, aiding in the design and optimization of complex systems. This study contributes to the field of system reliability-based design analysis by introducing a novel deep learning approach that addresses the limitations of traditional methods. The proposed approach opens avenues for future research in utilizing advanced machine learning techniques to enhance system safety and performance in dynamic environments.