Construction accident investigation reports contain critical information, but extracting useful insights from the voluminous Chinese text is challenging. Traditional methods rely on expert judgment, which leads to time-consuming and potentially inaccurate results. To overcome this problem, we propose a novel approach that combines text mining techniques and latent Dirichlet allocation (LDA) models to analyze standardized accident investigation reports in the Chinese construction industry. The proposed method integrates an information entropy term frequency-inverse document frequency (TF-IDF) weighting scheme to evaluate term importance and accounts for word and model uncertainty. The method was applied to a set of construction industry accident reports to identify the key factors leading to safety accidents. The results show that the causal factors of accidents in Chinese accident investigation reports consist of keywords and negative expressions, including “failure to timely identify safety hazards” and “inadequate site safety management”. Failure to timely identify safety hazards is the most common factor in accident investigation reports, and the negative expressions commonly used in the reports include “not timely” and “not in place”. The information entropy TF-IDF method is superior to traditional methods in terms of accuracy and efficiency, and the LDA model that considers word frequency and feature weights is better able to capture the underlying themes in the Chinese corpus. And the subject terms that make up the themes contain more information about the causes of accidents. This approach helps site managers more quickly and effectively understand the causal factors and key messages that lead to accidents from incident reports. It gives site managers insight into common patterns and themes associated with safety incidents, such as unsafe practices, hazardous work environments, and non-compliance with safety regulations. This enables them to make informed decisions to improve safety management practices.

In order to achieve a comprehensive evaluation of the environmental impact of ecological restoration projects (ERP) under the current destruction and restoration of coastal ecological areas, this paper takes into account the impact of positive and negative indicators on the environment; analyzes the positive and negative benefits of ERP; and establishes a comprehensive environmental impact index system for marine ERP from ecological, economic, and social perspectives through the DPSIR model. On this basis, the cloud model and Monte Carlo simulation are used to obtain the comprehensive assessment grade of the construction period, short-term operation, and long-term operation in the project life cycle. The results show that the benefits of ERP, considering the impact of negative factors, are significantly reduced, and the benefits of ERP will increase remarkably in the long-term operation period. In engineering practice, the environmental pressure factor caused by excessive human activities during construction and operation periods is a key negative factor affecting the overall benefits of ERP. For project decision makers and other stakeholders, the comprehensive assessment grade considering negative impacts is more practical. At the same time, decision makers should take active response measures in the framework of long-term sustainable development, set a tolerance threshold for negative pressure indicators, and strengthen the management of ERP.