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A majority of metro projects have been constructed to reduce urban traffic congestion and to improve the convenience of public transportation, but these projects also produced a significant amount of engineering slag and mud. The shield construction method could improve the efficiency and safety; this technique has been frequently used in tunnel excavation projects. However, the spoil produced during the shield construction is challenging to deal with. In literature, though there has been an increasing number of studies on the technologies of utilizing shield spoil, the on-site utilization of shield spoil is still a subject of little research. This study introduced an implementation framework for the on-site utilization of shield spoil based on successful case experiences. It aims to assist project managers in efficiently implementing on-site resource utilization projects and to address the gaps in the relevant field. A case study was conducted in the Shenzhen Metro Line 13 North Extension Project; this study collected data through six semi-structured interviews and field research, analyzed the data, and constructed a framework using thematic analysis and focus group discussions. The three components of the implementation framework are project planning, project construction, and project closing, and each section describes what must be accomplished during that phase. Additionally, three recommendations—(1) combining intelligent technology to optimize the existing equipment, (2) utilizing lean technology in project execution, (3) establishing strategic partnerships with construction waste recycling enterprises—are also made for the development of the shield spoil utilization project. This study uses the Shenzhen Metro Line 13 North Extension Project as a model to theoretically underpin future research on shielding spoil on-site during resource utilization projects and to achieve the repeatable promotion of shield spoil utilization technology in Shenzhen.

Deep learning-based methods, especially convolutional neural networks (CNNs), have shown their effectiveness for image classification. In this paper, vision transformer technology is used to classify the surface defects of processed bamboo, which can be more quick and accurate compared with the low efficiency of manual identification. In the first step, we replace the activation function from Gelu to Mish in the encoder part, but the classification performance is not satisfied. Then, to get a better classification results, we keep the original activation function and introduce the DropBlock. Compared with dropout, DropBlock can obtain better classification accuracy. Finally, compared with the results after transfer learning, it is proved that replacing dropout with DropBlock can improve the classification accuracy. The results on the bamboo chip datasets show that the accuracy of this method is 2% higher than the original transformer network whether using transfer learning.

Compliant mechanisms with stiffness variation are required in engineering applications. But most studies in the field of compliant mechanisms have only focused on a mechanism with a single stiffness. This paper presents a design method of compliant mechanisms with multi-level stiffness variation using internal self-contact between rigid parts. The contact changes the reconfigurations of compliant mechanisms to vary their stiffness for a specific motion range. We avoid the need for an external control to achieve piecewise stiffness variation. We derived the general design guideline by which the desired multiple stiffness and motion ranges can be acquired through rational design of the structural parameter. To illustrate the effectiveness of the design idea, we fabricated proof-of-concept prototypes for experimental investigations, and the experimental results demonstrated that the designed compliant mechanism has a multiple-motion range corresponding to different stiffness. The design ideas can also be extended to the multi-axis spatial compliant mechanisms. This design idea enriches the theory and application of compliant mechanisms.