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In developed countries such as Europe and the United States, building information modeling (BIM) technology has become an indispensable tool in the construction industry. In recent years, it has also contributed to a tide of reform in the construction industry in China; BIM technology has gradually been applied to large, complex projects and has become indispensable to lean construction. Based on BIM and lean theory, this study establishes a KanBIM quality control (QC) system to achieve a more efficient QC process by analyzing the tools and technologies necessary for the system. The research results have practical significance; they can contribute to improving the quality of construction enterprises, reduce project costs, and enrich the lean QC theory and advance BIM education and implementation in the construction industry.

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Constructing porous structures in electromagnetic interference (EMI) shielding materials is a common strategy to decrease the secondary pollution caused by the reflection of electromagnetic waves (EMWs). However, the lack of direct analysis methods makes it difficult to fully understand the effect of porous structures on EMI, hindering EMI composites’ development. Furthermore, while deep learning techniques, such as deep convolutional neural networks (DCNNs), have significantly impacted material science, their lack of interpretability limits their applications to property predictions and defect detection tasks. Until recently, advanced visualization techniques provided an approach to reveal the relevant information behind DCNNs’ decisions. Inspired by it, a visual approach for porous EMI nanocomposite mechanism studies is proposed. This work combines DCNN visualization with experiments to investigate EMI porous nanocomposites. First, a rapid and straightforward salt-leaked cold-pressing powder sintering method is employed to prepare high-EMI CNTs/PVDF composites with various porosities and filler loadings. Notably, the solid sample with 30 wt % loading maintains an ultrahigh shielding effectiveness of 105 dB. The influence of porosity on the shielding mechanism is discussed macroscopically based on the prepared samples. To determine the shielding mechanism, a modified deep residual network (ResNet) is trained on a dataset of scanning electron microscopy (SEM) images of the samples. The Eigen-CAM visualization of the modified ResNet intuitively shows that the amount and depth of the pores impact the shielding mechanisms and that shallow pore structures contribute less to EMW absorption. This work is instructive for material mechanism studies. Besides, the visualization has the potential as a porous-like structure marking tool.

The more uniform dispersion of MWCNTs in the matrix was caused by the high biaxial drawing ratios. Trace amounts of fillers were added to maintain low dielectric loss while increasing the dielectric properties of the BOPE/MWCNT composite films.