When constructing long-span spatial steel structures, the unformed structure is often incomplete and unstable. The construction sequence significantly influences the mechanical state of the structure during the construction stages (CSs), affecting both the path and time effects. This study examined the mechanical properties of the construction process using an actual project as a case study, comparing two methods: one-step forming and stage-by-stage forming. Critical turning points of stress and displacement during the CSs were identified as the initial installation and unloading stages. Stress concentrations frequently occurred at temporary support points, and peak displacements often appeared at the outer overhanging bars of the structure. A well-planned construction sequence can effectively manage the structure’s formation, boundaries, and loading to ensure construction safety and stability. The conclusions and analysis methods from this study provide valuable references for the design and construction of similar long-span spatial steel structures.

Prefabricated buildings are widely utilized due to their effectiveness in reducing carbon emissions. The construction stage has a significantly higher carbon emission rate than the other stages of their life cycle, but this is difficult to accurately quantify and predict due to the high variability. This study clarifies the system boundary of carbon emissions and the parameters of influence in carbon emissions predictions. The carbon emission quantification model was improved by using the process analysis method and the carbon emission factor method, and a modular calculation formula was proposed. Based on the machine learning algorithm, a carbon emissions prediction model for prefabricated buildings’ construction stage was established and hyperparameter optimization was conducted. A sample database for predicting prefabricated buildings’ carbon emissions during the construction stage was established using a modular quantification method, and the thin plate spline interpolation algorithm was introduced to expand this. The prediction results of carbon emission prediction models using four algorithms, SVR, BPNN, ELM, and RF, were compared and analyzed by RMSE and R². The results show that the model based on BPNN has the highest prediction accuracy when determining the carbon emissions of prefabricated building during the construction stage, and this method can provide a more accurate reference for subsequent quantitative research on carbon emissions from prefabricated buildings.

Automated vision-based sensing for personal protective equipment (PPE) compliance in high-formwork support system (HFSS) construction environments faces three compounding challenges: extreme within-image scale variation, dense scaffold occlusions, and small safety hook targets prone to missed detection. Existing sensing systems address only binary presence detection and cannot assess whether safety harnesses are anchored in compliance with regulatory requirements. This paper proposes YOLO-ILB, a lightweight task-specific object detector built on YOLO11n with three targeted improvements. The C3k2_IDWC module replaces standard convolutions with multi-branch Inception depthwise convolutions, improving multi-scale feature discrimination at reduced computational cost. The SPPF_LSKA module embeds large separable kernel attention into the SPPF aggregation path, strengthening global context awareness to suppress scaffold background interference. A BiFPN neck replaces the original PAN structure, enabling bidirectional cross-scale weighted feature fusion to balance detection of small hooks and large harnesses within a sin gle image. A UAV-based sensing dataset was constructed using a DJI Mini 3 Pro (4032 × 3024 px) across 17 real construction sites under varied altitudes, viewing angles, and illumination conditions, yielding 2700 annotated images across five object categories. YOLO-ILB achieves mAP50 = 0.939 with only 1.923 M parameters and 5.7 G FLOPs at 262.3 FPS, outperforming eight mainstream YOLO baselines while remaining deployable on resource-constrained edge computing nodes. A geometry-based compliance algorithm further classifies three harness anchoring states—correct high anchoring, incorrect low anchoring, and unclipped or excessively distant hook—without additional sensors or annotations, achieving 90.82% overall accuracy on 305 field instances and extending the sensing system from presence detection to regulatory compliance assessment.

Fall-from-height fatalities in underground construction are closely associated with formwork scaffold operations, where dense steel members cause severe non-line-of-sight (NLOS) and multipath effects that degrade positioning performance. Although ultra-wideband (UWB) technology offers high theoretical ranging accuracy, its deployment-dependent performance in metal-rich scaffold environments remains insufficiently quantified. This study focuses on physical deployment optimization rather than algorithmic compensation. A full-scale formwork scaffold was constructed, and a stepwise one-factor controlled experimental design was employed to quantify the effects of anchor height (H) and horizontal spacing (S) on 3D positioning accuracy. The results show that sub-meter accuracy can be achieved through appropriate deployment, with a minimum 3D RMSE of 0.317 m and over 80% of single-axis errors confined within a 0.2 m engineering-valid region. For this specific setup, the optimal S = 1.5 m correlates with the scaffold grid size (approximately 0.8 times the 1.8 m bay width). While we hypothesize this ratio dependency applies to other geometries, this remains a site-specific observation requiring future cross-validation. Further analysis indicates that this deployment balances vertical signal visibility and multipath suppression. In addition, while the Position Dilution of Precision (PDOP) metric reflects geometric sensitivity, it does not linearly correlate with actual positioning errors under coplanar UWB deployments. These findings provide a rigorous static error model, serving as a critical prerequisite for developing robust real-time safety monitoring systems in scaffold-intensive construction environments.

Prefabricated reinforced concrete staircases (PC staircases) are prefabricated components that are widely used in prefabricated buildings and are used in large quantities. During the production and construction of a PC staircase, the lifting point setting directly affects the construction safety, construction efficiency, and construction quality. In this paper, we analyze the quality problems and safety risks in the design, production, and construction of PC staircases under the constraints of multidimensional spatial characteristics, clarify the key technical difficulties of prefabricated staircase lifting under the multidimensional spatial and temporal constraints, and analyze the factors that should be considered in the setting of lifting points. In this paper, a prefabricated staircase lifting point setting database is established and a thin-plate spline interpolation algorithm is introduced to expand it. Based on the support vector machine algorithm, the process of optimization is carried out for the kernel function scale parameter and penalty factor, and it is concluded that for every increase of two in the number of cross-validation folds, the percentage reduction in minimum RMSE is 9.4%, 17.8%, and 4.2%, respectively, the percentage increase in the optimization time is 39.7%, 61.8%, and 27.3%, respectively, and a PC staircase lifting point setup method based on the small-sample database is proposed. The number of lifting points and lifting point locations of the PC staircase satisfying the multidimensional spatial feature constraints can be obtained by inputting the five design parameters of the PC staircase, namely, the number of treads, the height of the treads, the width of the treads, the width of the staircase, and the weight of the staircase, into the lifting point setup method proposed in this paper. The reliability of the precast reinforced concrete staircase lifting point setting method proposed in this paper when considering the multidimensional spatial constraint characteristics is verified by the precast staircases in deep shafts for assembly construction at the Chongqing metro station.