In recent times, with the development of protection stratum, it is more and more important to investigate its effects. In this paper, in view of this situation, similar simulation experiment and site test are used to investigate the real-time effects at a certain mine of Pingdingshan Coal Group Company. The results of similar simulation experiment and site test show the stress and displacement variation of overlying rock and protected seam; define the max relative deformation and Roof Falling Interval; and determine the Protected Zone and Pressure Relief Angle. So, the result of research has great significance in directing the production of the mine and in developing the technology of protection stratum mining.

To address the difficulty of obtaining real fault data from elevator traction drive systems and the potential overestimation caused by random window splitting, this study uses two public motor-drive datasets to examine how validation protocols, signal modalities, and noise conditions affect diagnostic evaluation, rather than to claim direct validation of field performance in actual elevator systems. A PMSM inverter-drive fault diagnosis dataset is used as the main dataset to evaluate multiclass classification performance based on electrical, thermal, and derived features. A multimodal MOTOR dataset is used as an independent secondary dataset to analyze the effects of validation protocols, signal modalities, and noise disturbance on model performance. The results show that Random Forest achieves a Macro-F1 of 0.9901 under random splitting on the PMSM dataset. On the MOTOR dataset, the Macro-F1 reaches 0.9682 under random splitting but decreases to 0.5856 under strict block-split validation, indicating that random window splitting may substantially overestimate generalization performance for continuous signal data. The modality ablation results show that the vibration-only modality performs best under strict block-split validation, with a Macro-F1 of 0.6420, whereas the noise analysis indicates that this modality is sensitive to disturbance. The results show that public motor-drive data can provide a reproducible methodological test bed for studying evaluation bias and signal reliability, but they should not be interpreted as direct evidence of diagnostic performance in actual elevator systems.

Construction safety prediction commonly addresses accidents or current hazards, whereas the timing of repeated violations after case resolution is less clearly characterized. This retrospective longitudinal cohort prediction study used public U.S. Occupational Safety and Health Administration enforcement records to construct 741,436 employer–standard observations from 2012 to 2025, including 16,002 observed same-standard recurrences. Follow-up began at a resolution-based anchor and was right-censored at five years or the administrative study end. CatBoost-Cox, XGBoost-Cox, and XGBoost-AFT combined inspection context, resolution information, pre-index history, and regulatory hierarchy. Models were trained on 2012–2018 anchors, validated in 2019, and compared in a 2020 benchmark that informed model selection. The selected 63-predictor XGBoost-Cox model achieved a 2020 C-index of 0.8062 (95% CI 0.7908–0.8200) and 3-year AUC of 0.8128 (0.7953–0.8290); recent AUC was 0.7988 (0.7884–0.8091). A separate highway holdout yielded AUC 0.7281 (0.6463–0.8080) among calendar-complete observations. A sensitivity analysis censoring development outcomes at 31 December 2019 retained AUCs of 0.8105 and 0.7948 in the two later cohorts. Section-level encoding provided a consistent predictive increment. The highest-risk decile contained 42.61% and 36.45% of recorded 3-year events, although absolute-risk calibration deteriorated. The contribution is a resolution-anchored framework linking standard-specific recurrence, unequal follow-up, and construction hazard history. A predictor-level audit could not confirm historical availability of the extracted inputs, leaving temporal leakage unresolved. The framework supports retrospective assessment; prospective use requires verified inputs, recalibration, and independent validation. The endpoint remains inspection-detected recurrence rather than unobserved hazards or a demonstrated preventive effect.