Researchers and practitioners are focusing greater attention on safety citizenship behavior (SCB), an important factor in preventing injuries and improving workplace safety conditions; however, very little research has been done to study the cognitive mechanisms of employees engaged in this proactive safety behavior. This study aims to explain critical antecedents and cognitive mechanisms of construction workers’ SCBs based on an integrated theoretical framework of the theory of planned behavior (TPB) and norm activation model (NAM). This study was conducted by distributing a questionnaire survey to 719 construction workers in China. The results of structural equation modeling (SEM) indicated that personal norms (PNs), attitudes toward SCB (ATT), subjective norms (SNs), and perceived behavioral control (PBC) have significant positive impacts on the intention of SCB, and the size of direct effects decreases in turn. The study also confirmed the significant indirect effects of SN on behavioral intention by way of ATT, PBC, and PN; ATT, PN, and PBC predicted SCB through the mediating processes of SCB intention. The study demonstrates the applicability and effectiveness of the integrative model in predicting workers’ intentions to adopt SCBs and provides targeted suggestions and strategies for the sustainable development of safety management. These findings have meaningful implications for academic research on SCBs and the industrial practice of safety management.

The literature and practices of construction safety management have highlighted the importance of domain knowledge. Effectively extracting the domain knowledge elements (DKEs) of construction safety management remains a challenging task. To address this problem, this paper develops a rule-based natural language processing (NLP) approach for extracting DKEs from Chinese text documents in the domain of construction safety management. First, a linguistic pattern of DKEs was constructed according to lexical analysis and syntactic dependency parsing. Then, the extraction rules and workflow paths were established and tested. The results indicated that most DKEs in the domain of construction safety management are composed of specific compound parts of speech (nouns and noun phrases), specific word dependencies (attribution, verb-object, subject-verb, preposition-object, and coordinate relationship), and words of specific lengths (two to six Chinese characters). This work is the first to reveal the Chinese linguistic patterns and linguistic features of DKEs in the domain of construction safety management. The findings of this study can facilitate the establishment and supplementation of domain lexicons and knowledge-based safety management systems and can guide safety training for construction safety management.

China’s construction industry developed rapidly and safety production has become a vital issue. Improving the safety behavior of construction workers is an important measure to effectively decrease construction safety accidents. At present, a New Generation of Construction Workers (NGCWs) born after 1980 has gradually become the main force of construction companies in China and the special group characteristics coming from the intergenerational difference may make them behave differently in safety-related activities, therefore, it is very important to study how to promote their safety behavior. This paper aimed to explore the influencing mechanism of job satisfaction on the safety behavior of NGCWs and examine the mediating role of safety knowledge sharing and work engagement. Confirmatory factor analysis and structural equation modeling analysis were applied to test the theoretical model. Empirical research results indicated that job satisfaction can effectively promote safety behavior through safety knowledge sharing and work engagement. Safety knowledge sharing plays a complete mediating role between job satisfaction and safety compliance behavior, as well as between job satisfaction and safety participation behavior. Moreover, work engagement plays a complete mediating role between job satisfaction and safety participation behavior, which can provide valuable management references for China’s construction companies to strengthen their safety behavior.

The outbreak of COVID-19 has introduced critical challenges in the architecture, engineering, and construction (AEC) industry; to address these challenges, building information modelling (BIM) can be applied as a project management tool to help enhance collaboration among stakeholders and improve business performance. Amidst the COVID-19 crisis, there is a greater need to explore and implement effective strategies to promote a wider adoption of BIM. However, increasing the willingness of project participants to adopt BIM through event management has not received much attention. Therefore, based on event system theory and innovation diffusion theory, we developed a model to explore the influence of the COVID-19 crisis on the willingness of AEC participants to adopt BIM. Structural equation modelling was performed to test the hypotheses. The results demonstrate that the intention of the AEC project participants to adopt BIM is directly driven by the COVID-19 event criticality and perceived usefulness of BIM. Moreover, the event criticality and BIM technical features (relative advantage, compatibility, and complexity) can indirectly affect this intention, through the perceived usefulness. However, the impact of event disruption and novelty on the BIM adoption intention is not significant. Several recommendations are provided to improve the BIM adoption intention of AEC participants during and after the pandemic.

China’s urban rail transit (URT) construction is coming into the stage of rapid development under the guidance of national policies. However, the URT construction projects belong to high-risk projects and construction safety accidents occur frequently. Presently, safety risk management is in continuous development. Unfortunately, due to risk data deficiencies and lack of relationship between participants and safety risk factors, most of the research results cannot be well applied to URT projects. To overcome the limits, this paper has applied the text mining method into safety risk analysis. Through word frequency analysis and cluster analysis, 15 safety risk factors and 3 participants are identified from 156 accident reports. In addition, the accident descriptive model has been established, which is composed of indirect safety risk factors (management defects), direct safety risk factors and participants. In this model, each accident is the standardized description of the corresponding accident information. This is useful for risk data accumulation and analysis. Then the network structure analysis and risk assessment methods are utilized to make clear 63 relationships among participants, management defects and direct safety risk factors. Subsequently, the risk value of each relationship is evaluated. These safety risk information is integrated into the accident descriptive model by using accident points. Finally, ABC analysis which is a popular and effective method used to classify items into specific categories that can be managed and controlled separately is used to analyze the safety risk management’s core process(A), important process(B) and general process(C) in the accident descriptive model. The research results show that the constructor should pay attention to construction coordination, safety specifications, safety measures and personnel education, the supervisor should attach importance to timely communication, the monitoring unit should pay attention to advanced forecast and dynamic control. The main research contributions are as follows: (1) A method of obtaining risk data from unstructured content has been provided; (2) The accident descriptive model could be utilized for risk data continuous accumulation; (3) The emphases of URT construction safety risk management are made clear.