Prefabricated buildings that are more environmentally friendly have been vigorously promoted by the Chinese government because of the reduced waste and carbon emissions during the construction process. Most of the construction processes of prefabricated buildings are completed in the prefabricated component factory, but the safety risks during the hoisting process cannot be ignored. In this paper, the initial framework of a Bayesian Network (BN) is obtained from the combination of the improved Human Factors Analysis and Classification System Model (HFACS) and BN. The improved similarity aggregation method (SAM) is used to calculate the prior probability of BN, which can better summarize and deal with the fuzzy judgment of experts on risk accidents. The improved SAM can consider both the weight of experts and the relative consistency of their opinions, which is of great significance for improving the reliability of BN inputted data. This paper uses the construction project in Sanya, Hainan Province, to verify the validity of the model. The results show that the calculation results of the model are basically consistent with the actual situation. The safety risk of this project is relatively low, and the premise of unsafe behaviors and unsafe supervision are the key risk factors of the project. In addition to maintaining good construction conditions and workers’ healthy states, it is also necessary to carefully check the performance of tower cranes and equipment such as spreaders. During the operation process of the tower crane, workers should avoid walking or staying within the hoisting range.

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.

Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in subway station construction, a novel model based on least-squares support vector machines (LSSVM) optimized by quantum-behaved particle swarm optimization (QPSO) was proposed. First, early warning factors from five aspects (man, machine, management, material, and the environment) were selected based on accident causation theory and literature research. The data acquisition method of each risk factor was provided in detail. Then, the LSSVM with strong small sample analysis and nonlinear analysis abilities was chosen to give the early warning. To further ameliorate the early warning accuracy of the LSSVM, QPSO with a strong global retrieval ability was used to find the optimal calculation parameters of the LSSVM. Seventeen subway stations of Chengdu Metro Line 11 in China were picked as the empirical objects. The results demonstrated that the best regularization parameter was 1.742, and the best width parameter was 14.167. The number of misjudged samples of the proposed model was 1, and the early warning error rate was only 4.41%, which met the needs of engineering practice. Compared with the classic and latest methods, the proposed model was found to have a faster prediction speed and higher prediction accuracy.

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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.