In the field of cranes, unreasonable structure design leads to high energy consumption. In order to solve the problems of heavy weight and serious steel consumption of a crane structure, a green energy-saving design method based on computational intelligence is proposed. For minimizing the weight of a structure, two optimization models are proposed. The specular reflection algorithm is used to make the green and lightweight design. A multi-objective optimization model for the green design is constructed. The minimum waste generated in the manufacturing process is the objective function of this model. Fuzzy mathematics theory is utilized to comprehensively evaluate the impact of crane structure weight and processing waste on the environment, and a structural optimization model with fuzzy comprehensive evaluation indicators for the green design is introduced. The results indicate that compared with the original design, the processing waste after fuzzy comprehensive optimization is 63.43% lower and the cross-sectional area of the main girder is reduced by 27.03%.

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The comprehensive effect of multiple factors that include the geometric characteristics, load status, service characteristics, and failure mechanism will affect the safety of bridge crane structure. To evaluate the security of the bridge crane structure, the real‐time prediction method of fatigue life of the bridge structure based on digital twin is proposed. The specific type of general bridge crane is selected as the physical entity of the research object, and the information acquisition system is utilized to get the current service status information about the physical entity. On this basis, combined with the historical service information and inherent information of physical entity, the fuzzy database is established. Meanwhile, twin data are formed by the clear quantification of fuzzy information and data processing technology. In accordance with structural characteristic and work cycle process of bridge crane, the analytical models of load, strength, defect, and fatigue life are established, respectively. The multi‐theoretical calculation model is completed by encapsulating the analysis models and transmitting information, and then the main factors affecting the fatigue life of bridge crane structure are determined. With that, the comprehensive evaluation coefficient is calculated by the fuzzy comprehensive evaluation theory. The response relationship between the information data and the fatigue life of bridge crane structure is described by the Kriging surrogate model constructed with experimental design. Real‐time prediction of fatigue life of bridge crane structure is realized in a virtual space to depict the life cycle process. Taking QD20/10 t × 43 m × 12 m general bridge crane as an example, the feasibility and applicability of the proposed method are verified, which provides a strong theoretical basis for dependable service and timely scrapping of cranes.

The nominal values of structural design parameters are usually calculated using a traditional deterministic optimization design method. However, owing to the failure of this type of method to consider potential variations in design parameters, the theoretical design results can be far from reality. To address this problem, the specular reflection algorithm, a recent advancement in intelligence optimization, is used in conjunction with a robust design method based on sensitivity. This method not only is able to fully consider the influence of parameter uncertainty on the design results but also has strong applicability. The effectiveness of the proposed method is verified by numerical examples, and the results show that the robust design method can significantly improve the reliability of the structure.

Fruit fly optimization algorithm, which is put forward through research on the act of foraging and observing groups of fruit flies, has some merits such as simplified operation, strong robustness, easy to parallel computing, and fast convergence rate; it could solve the bottlenecks of traditional intelligent optimization algorithms on precocity and low convergence speed effectively. Fruit fly optimization algorithm is applied to almost all the numerical optimization problems and is very useful in engineering applications. When the design variable is negative, traditional fruit fly optimization algorithm is not qualified for the extraordinarily slow convergence rate during the late stage of calculation and easy to be trapped in local optimum. Because of the defects of classical fruit fly optimization algorithm, a new coding method of the process of optimization is improved by this article, so the design variables could be searched toward the direction. In addition, a novel bionic global optimization—fruit fly optimization algorithm of learning—is proposed by introducing the concept of “study.” This article tries to apply fruit fly optimization algorithm of learning to compare calculations; therefore, four classical test functions and two engineering problems are performed. It turned out that not only does fruit fly optimization algorithm of learning inherit the advantages of fruit fly optimization algorithm, but has a strong learning ability. The introduction of “study” ability into fruit fly optimization algorithm notably improves the efficiency and capability of optimization; it has characteristics of fast convergence rate and fast speed of approaching the global optimum solutions. Fruit fly optimization algorithm of learning has the ability to solve practical problems, and its engineering prospect is promising.