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Metal additive manufacturing has significantly evolved since the 1990s, achieving a market valuation of USD 6.36 billion in 2022, with an anticipated compound annual growth rate of 24.2% from 2023 to 2030. While powder-bed-based methods like powder bed fusion and binder jetting dominate the market due to their high accuracy and resolution, they face challenges such as lengthy build times, excessive costs, and safety concerns. Non-powder-bed-based techniques, including direct energy deposition, material extrusion, and sheet lamination, offer advantages such as larger build sizes and lower energy consumption but also encounter issues like residual stress and poor surface finish. The existing reviews of non-powder-bed-based metal additive manufacturing are restricted to one technical branch or one specific material. This survey investigates and analyzes each non-powder-bed-based technique in terms of its manufacturing method, materials, product quality, and summary for easy understanding and comparison. Innovative designs and research status are included. [ABSTRACT FROM AUTHOR]

Mites are among the most abundant invertebrates in subsurface ecosystems, and their community assemblages and distributions are often significantly influenced by the diversity of habitat resources. The cave ecosystem encompasses drastic changes in nonbiological factors, such as changes in lighting conditions from bright to extraordinarily dark and habitat gradients of surface plant resources from abundant to scarce or even disappearing, providing an ideal unique environment for evaluating the assembly mechanism of soil animal communities. Nevertheless, there still needs to be a sufficient understanding of the biodiversity patterns and drivers of mite communities across environmental gradients in karst caves. We conducted a comprehensive survey on the composition and diversity of soil mites in three photometric zones (dark, twilight, and light) of a typical karst cave and its adjoining extractive environments (forest scrub and farmland). Our research aimed to investigate the ecological relationships of mite communities between above‐ and below‐ground habitats and the effects of abiotic factors on mite communities. We collected 49 families, 86 genera, and 1284 mites. In the external cave environment, we captured 1052 mites from 72 genera and 45 families; in the internal cave environment, we captured 232 mites from 46 genera and 29 families. The abundance, richness of genera, and diversity parameters of the mite community decreased from the cave entrance to the cave interior with decreasing light intensity. Oribatid mites dominated the mite community. Protoribates and Scheloribates were the dominant genera, along with Tectocepheus and 11 other genera, which primarily distinguished the mite communities among different habitats. Forty endemic taxa were found in the external cave environment, compared to 14 endemic taxa in the internal cave environment. The mite community showed a strong preference for the cave ecosystem habitat. Temperature, humidity, and soil nitrogen content significantly influenced the distribution pattern of mite communities (VIP > 0.8, p < 0.05). [ABSTRACT FROM AUTHOR]

To address the issue of costly computational expenditure related to high‐fidelity numerical models, surrogate models have been widely used in various engineering tasks, including design optimization. Despite the successful application of the existing surrogate models, physics‐based models depend largely on simplifications and assumptions, which render parameter calibration challenging; whereas data‐driven models require substantial data to reach their full potential, with their performance often being constrained in tasks when obtaining massive data is difficult. In this study, a hybrid surrogate model is proposed combining physics‐based and data‐driven models to rapidly estimate building seismic responses. The application of this model is exemplified through effective estimation of inter‐story drift ratios (IDRs), being a critical factor in shear‐wall structure design. Initially, a data augmentation technique and a parametric modeling procedure are introduced to significantly enhance the dataset diversity. Subsequently, a task decomposition strategy is proposed to effectively integrate a data‐driven graph neural network (GNN) and a physics‐based flexural‐shear model. Additionally, the output layer and the loss function of the GNN are modified to enhance the estimation accuracy by eliminating fundamental errors. Results of numerical experiments indicate that the proposed hybrid model can complete IDR estimations in an average time of 0.56 s, with a mean absolute percentage error of 12.7%. This performance significantly surpasses that of existing purely data‐driven and physics‐based models. A case study shows that the efficiency of the proposed hybrid model is approximately 100 times greater than that of conventional finite element software. This enables an accurate assessment of the design compliance with code requirements. The results of this study can be applied to the design optimization of seismic‐resistant building structures. [ABSTRACT FROM AUTHOR]

The construction material quantity (CMQ) is widely concerned in the structural design of reinforced concrete buildings and is often included among the objective functions of computer‐aided optimization design techniques. To minimize construction cost and carbon emissions, an accurate and efficient CMQ estimation method is timely required. In this study, a novel graph neural network (GNN) is proposed, whose architecture and loss function are specifically designed for CMQ estimation. With a heterogeneous feature fusion mechanism, the GNN can automatically extract features from all CMQ‐related information, in contrast to the existing data‐driven methods that rely heavily on manually selected features. By further incorporating a prior knowledge inclusion strategy, the GNN can avoid fundamental errors that might be encountered by purely data‐driven methods. To enrich the diversity of the CMQ dataset, a data augmentation method is proposed incorporating generative adversarial networks and parametric modeling. Numerical experiments and case studies show that the proposed CMQ estimation method is superior to the existing data‐driven methods in terms of accuracy and is 500 times faster than typical commercial structural design software. This study is anticipated to benefit the objective evaluation of computer‐aided design, thereby facilitating the promotion of low‐cost and low‐carbon‐emission building designs. [ABSTRACT FROM AUTHOR]

A realistic and practical damping model is essential for structural dynamics. In this study, the second-order accurate time discretization of a high-performance uniform damping model for response history analysis is derived. Its stability and accuracy are proven, and its object-oriented implementation is introduced in OpenSees. The proposed time discretization accurately represents energy dissipation in individual components with minimal additional computational costs. It applies to various time integrators, element formulations, and structural systems, allowing for high-fidelity evaluation of structural performance and ensuring public safety. These advantages are illustrated through detailed case studies. [ABSTRACT FROM AUTHOR]