Undetected fatigue and overload damages at the key locations of the crane boom are among the biggest threats in construction, leading to structural failure. Thus, the structural health of the crane boom should be monitored in real time to ensure that it works under the designed load capacity. In this study, we developed a lightweight digital twin by the multifidelity surrogate (MFS) model to improve the real-time monitoring and prediction accuracy of the structural safety of a crane boom. Digital twin technology, which can establish real-time mapping between the physical space and the digital space, has a promising potential for online monitoring and analysis of structures, equipment, and even human bodies. By combining the MFS model and sensor data, the lightweight digital twin can dynamically mirror the crane boom postures and predict its structural performance in real time. In this study, the structural analysis of the crane boom is limited to the linear elastic stage of materials. Numerical experiments showed that the accuracy of the lightweight digital twin was enhanced compared with that established by the single-fidelity surrogate model, and the computational cost of the lightweight digital twin was decreased with respect to the digital twin built by the numerical method. Meanwhile, the uncertainty from the physical space was analyzed to enhance the reliability of the lightweight digital twin. Thus, the lightweight digital twin developed in our work can ensure accurate safety prediction and design optimization for crane booms.

Large cable shovel is a complex mechatronic system used for primary production in the open pit mine. For such structure-control highly coupled system, the conventional sequential design strategy (structure design followed by the control optimization in sequence) cannot manage this interaction adequately and explicitly. In addition, the large cable shovel consists of large number of sub-systems and/or disciplines, which also poses challenges to the global optimal design for large cable shovel. To enhance large cable shovel’s performance, an integrated design optimization strategy combining the structure-control simultaneous design (co-design) and the multidisciplinary design optimization is established in this study to perform the global optimization for the large cable shovel. In this proposed multidisciplinary co-design, the point-to-point trajectory planning method is extended to achieve the simultaneous optimization of the structure and control system. Besides the structure and control, the dynamics/vibration and energy consumption are taken into account in this multidisciplinary co-design. The objectives are to minimize the energy consumption per volume of ore and to minimize the excavating time. By comparing the multidisciplinary co-design and the conventional sequential design, it is found that the multidisciplinary co-design can not only make large cable shovel’s structure more compact with relatively small vibration, but also generate more flexible control speeds by making the best of the power motors.

Ballast systems and ballast water dynamic allocation between ballast tanks are very important for ensuring the offshore operation efficiency and safety of the revolving floating crane (RFC). Its modeling and solving have multiple difficulties such as modeling complexity, solving complexity and engineering practicability. Early studies showed that domain knowledge is of great significance for the optimization of the design quality and innovation of such complex engineering issues. By analyzing the coupled operation process characteristics among the floating crane, ship hull and ballast system, a ballast water allocation optimization model based on dynamic programming strategy is established. The domain knowledge of ship ballasting is extracted, and a domain knowledge base of expert experience rules for the ballast water allocation is established. A Fuzzy-Particle Swarm Optimization (FPSO) algorithm is given to obtain the optimal allocation scheme, which uses fuzzy logic inference to process domain knowledge and improve the solving quality. Three different cases are given to illustrate the validity of the proposed model and algorithm by comparing it with other algorithms. The analysis results show that the established optimization method can effectively improve the operation efficiency and reduce the calculation time and the number of ballast tanks involved in allocation, which makes the optimal scheme more suitable for engineering applications.