Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis.

The state-of-charge (SOC) is a pivotal metric in lithium-ion battery management systems. Here, we introduce a novel strategy that synergizes advanced feature selection with a closed-loop estimation architecture, thereby enhancing both the robustness and precision of data-driven SOC assessments. First, public data are used as the training set, and the random forest (RF) algorithm is utilized to determine the degree of influence of each feature of the training set on SOC, and to select the key feature factors affecting SOC. Secondly, we optimize the parameters (smoothing factor σ ) of the generalized regression neural network (GRNN) using the Whale of a Bat Optimization Algorithm (WOA-BAT), and propose a practical model of the GRNN for estimating the SOC. In addition, we developed a closed-loop estimation framework employing the Sage-Husa Adaptive Kalman Filter. Rigorous experiments were then performed across batteries subjected to varying temperatures and operational regimes, confirming the method’s robustness and broad applicability. The simulation results show that the root-mean-square error (RMSE) of SOC prediction is less than 0.5% and the mean absolute error (MAE) is less than 2.5%, which provides good advantages and prediction ability.