Cameras, with their low cost and efficiency, are widely used in construction management and structural health monitoring. However, existing reviews on camera sensor placement (CSP) are outdated due to rapid technological advancements. Furthermore, the construction industry poses unique challenges for CSP implementation due to its scale, complexity, and dynamic nature. Previous reviews have not specifically addressed these industry-specific demands. This study aims to fill this gap by analyzing articles from the Web of Science and ASCE databases that focus exclusively on CSP in construction. A rigorous selection process ensures the relevance and quality of the included studies. This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. Simultaneously, Building Information Modeling is employed to consider the progress of construction and visualize optimized layouts, improving the effect of CSP. This paper delves into perspective distortion, the field of view considerations, and the occlusion impacts, proposing a unified framework that bridges practical execution with the theory of optimal CSP. Furthermore, the roadmap for future exploration in the CSP of construction is proposed. This work enriches the study of construction CSP, charting a course for future inquiry, and emphasizes the need for adaptable and technologically congruent CSP approaches amid evolving application landscapes.

Three-dimensional concrete printing (3DCP) technology with solid wastes has significant potential for sustainable construction. However, the hardened mechanical properties of components manufactured using 3DCP technology are affected by weak interlayer interfaces, limiting the widespread application of 3DCP technology. To address the inherent limitations of 3DCP technology, conventional improvement strategies, such as external reinforcement and the optimization of material properties, lead to increased production costs, complex fabrication, and decreased automation. This study proposes an innovative spatial path optimization method to enhance the mechanical performance of 3D-printed, cement-based components. The novel S-path design introduces additional printed layers in the weak interlayer regions of the printed samples. This design improves the spatial distribution of fiber-reinforced filaments in continuous weak zones, thus enhancing the functional efficiency of fibers. This approach improves the mechanical performance of the printed samples, achieving compressive strengths close to those of cast samples and only a 20% reduction in average flexural strength. Compared to using a conventional printing path, the average compressive strength and flexural strength are improved by 30% and 55%, respectively, when the S-path layout is employed in 3DCP. Additionally, this method significantly reduces the anisotropy in compressive and flexural strengths to 26% and 28% of samples using conventional printing paths, respectively. Therefore, the proposed method can improve the mechanical properties and stability of the material, reducing the safety risks of printed structures.

This study aims to facilitate a comparison between construction monitoring data and simulation results, focusing on the dynamic adjustment of safety monitoring parameters in shield construction. First, a finite element simulation was performed to define a reasonable range for shield parameters based on settlement control values, thereby determining the theoretical settlement value. An early warning system was then developed integrating two key factors: theoretical and control settlement values. Finally, Dynamo was used to merge the digital and analog data, enhancing the visual representation of the monitoring information. The findings show that combining simulations with an early warning system effectively addresses the dynamic control challenges of shield construction parameters. Furthermore, integrating digital and analog monitoring significantly improves the efficiency of real-time visualization in monitoring data. This research provides a novel and effective methodology for enhancing shield tunnel construction safety, precision, and efficiency, offering critical insights for large-scale infrastructure projects and contributing to more reliable monitoring systems in complex construction environments.

The widespread application of concrete with specific functions has become indispensable in modern technology. However, the persistent issue of electromagnetic pollution poses a serious hazard to human health, electronic equipment, and military operations. Although various conventional electromagnetic absorbing materials have been incorporated, the achievable EMW-absorption performance is still restricted, with only a narrow effective absorption bandwidth. This study investigates the application of advanced 3D-printing technology to produce concrete with enhanced EMW-absorption properties with the incorporation of SAP (super-absorbent polymers). To achieve this, concrete samples with three SAP occupying the concrete volumes (0 vol.%, 20 vol.%, and 40 vol.%) and three methods (pretreatment-addition) were examined to provide an in-depth analysis of the properties and microstructures. The study reveals superior electromagnetic absorption in concrete enhanced with SAP compared to the untreated counterpart. Specifically, samples subjected to 40 vol.% Dry Treatment SAP exhibited exceptional performance, achieving 98.77% absorption at 7.53 GHz frequency with a peak reflectance of −19.12 dB, outperforming unmodified absorbing resin concrete by 25.44%. Moreover, microscopic analysis revealed irregular void distribution within the concrete, while the 3D-printing and -mixing processes led to SAP particle fractures, forming a complex 3D structure, thereby enhancing EMW-absorption performance. Ultimately, by selecting appropriate SAP pre-treatment and mixing methods based on the specific frequency range, this study provides crucial references and practical guidance for the application of EMW-absorbing concrete in military and technological contexts.

The large-scale accumulation of coal gangue has created increasing environmental pressure, while its use as aggregate in cementitious materials remains limited by its high water absorption, porous structure and unstable mechanical performance. This study develops a machine learning-assisted multi-objective optimization framework for lightweight shotcrete incorporating surface-pretreated coal gangue aggregates and polyvinyl alcohol fibres. Two pretreatment methods—namely, silica-fume slurry coating (CGACM) and dry adsorption activation (CGACD)—were applied to improve the aggregate surface characteristics. Experimental data on compressive strength, splitting strength and density were used to train backpropagation neural networks and support vector machine and random forest models, with hyperparameters optimized by the Beetle Antennae Search algorithm. The trained models were then coupled with a multi-objective optimization procedure to balance mechanical performance, density, material cost and CO₂ emissions. The results show that surface pretreatment can improve the performance of coal gangue lightweight shotcrete, while the proposed optimization framework can identify mixture designs with balanced strength, reduced density and improved economic and environmental performance. Compared with untreated or non-optimized mixtures, the optimized surface-pretreated mixtures achieved a more favorable trade-off among mechanical, cost and carbon-emission objectives. This study provides a data-driven approach for the sustainable design and practical utilization of coal gangue in lightweight shotcrete.