As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples.

Hydrogen has gradually become one of the indispensable sources of energy for mankind. Since the discovery of hydrogen embrittlement (hydrogen-induced degradation of material properties) more than 100 years ago, fatigue properties in hydrogen environments have been studied. Fatigue crack growth of materials in a hydrogen environment is a complex process involving the interaction of multiple factors. Hydrogen binds to atoms within the material, leading to diffusion and aggregation of hydrogen atoms, which causes an increase in internal stresses. These stresses may concentrate at the crack tip, accelerating the rate of crack expansion and leading to fatigue fracture of the material. The work of current researchers has summarised a number of fatigue models to help understand this phenomenon. This paper firstly summarises the existing hydrogen embrittlement mechanisms as well as hydrogen embrittlement experiments. It then focuses on the mechanism of fatigue crack propagation in hydrogen environments and related literature. It also analyses and summarises a cluster diagram of the literature generated using CiteSpace. The fatigue life prediction methods for materials in hydrogen environment are then summarised in this paper. It aims to provide some guidance for the selection and design of materials in developing fields such as fatigue materials in hydrogen environment. Finally, challenges in the current research on the fatigue properties of materials under hydrogen embrittlement conditions are pointed out and discussed to guide future research efforts.

The development of surrogate models based on limited data is crucial in enhancing the speed of structural analysis and design optimization. Surrogate models are highly effective in alleviating the challenges between design variables and performance evaluation. Bidirectional Long Short-Term Memory (BiLSTM) is an advanced recurrent neural network that exhibits significant advantages in processing sequential data. However, the training of BiLSTM involves the adjustment of multiple hyperparameters (such as the number of layers, the number of hidden units, and the learning rate), which complicates the training process of the model. To enhance the efficiency and accuracy of neural network model development, this study proposes an Improved Whale Optimization Algorithm-assisted BiLSTM establishment strategy (IWOA-BiLSTM). The new algorithm enhances the initial population design and population position update process of the original Whale Optimization Algorithm (WOA), thereby improving both the global search capability and local exploitation ability of the algorithm. The IWOA is employed during the training process of BiLSTM to search for optimal hyperparameters, which reduces model training time and enhances the robustness and accuracy of the model. Finally, the effectiveness of the model is tested through a parameter optimization problem of a specific analog circuit. Experimental results indicate that, compared to traditional neural network models, IWOA-BiLSTM demonstrates higher accuracy and effectiveness in the optimal parameter design of analog circuit engineering problems.