Research on Unsafe Behavior of Construction Workers Under the Bidirectional Effect of Formal Rule Awareness and Conformity Mentality

At present, China’s engineering safety management has developed to a certain level, but the number of casualties caused by construction accidents is still increasing in recent years, and the safety problems in the construction industry are still worrying. For purpose of effectively reducing construction workers’ unsafe behavior and improve the efficiency of construction safety management, based on multi-agent modeling, this paper analyzes the influencing factors during construction workers’ cognitive process from the perspective of safety cognition, constructs the interaction and cognition of the agent under the bidirectional effect of formal rule awareness and conformity mentality model, and set behavior rules and parameters through the Net Logo platform for simulation. The results show that: Unsafe behavior of construction workers is related to the failure of cognitive process, and the role of workers’ psychology and consciousness will affect the cognitive process; The higher the level of conformity intention of construction workers, the easier it is to increase the unsafe behavior of the group; Formal rule awareness can play a greater role only when the management standard is at a high level, and can correct the workers’ safety cognition and effectively correct the workers’ unsafe behavior; Under certain construction site environmental risks, the interaction between formal rule awareness and conformity mentality in an appropriate range is conducive to the realization of construction project life cycle management. This study has certain theoretical and practical significance for in-depth understanding of safety cognition and reducing unsafe behavior of construction team.

Semantic Network Analysis Using Construction Accident Cases to Understand Workers’ Unsafe Acts

Unsafe acts by workers are a direct cause of accidents in the labor-intensive construction industry. Previous studies have reviewed past accidents and analyzed their causes to understand the nature of the human error involved. However, these studies focused their investigations on only a small number of construction accidents, even though a large number of them have been collected from various countries. Consequently, this study developed a semantic network analysis (SNA) model that uses approximately 60,000 construction accident cases to understand the nature of the human error that affects safety in the construction industry. A modified human factor analysis and classification system (HFACS) framework was used to classify major human error factors—that is, the causes of the accidents in each of the accident summaries in the accident case data—and an SNA analysis was conducted on all of the classified data to analyze correlations between the major factors that lead to unsafe acts. The results show that an overwhelming number of accidents occurred due to unintended acts such as perceptual errors (PERs) and skill-based errors (SBEs). Moreover, this study visualized the relationships between factors that affected unsafe acts based on actual construction accident case data, allowing for an intuitive understanding of the major keywords for each of the factors that lead to accidents.

Applying decision tree algorithm to explore occupational injuries in the Turkish construction industry

Occupational injuries and fatalities are one of the most significant issues in the construction industry. Variables, such as workers’ behavior, age, worksite condition, and type of activity, play key roles in the occurrence of construction accidents. In recent years, data mining techniques have been successfully used not only in health, economy, and social sciences but also in construction-related fields. In this study, C5.0 decision tree algorithm was used to analyze the accident data obtained from the Social Security Institution of Turkey. A classification tree model was created to discover the associations between the attributes. The results show the relationship between the injury status of workers and the attributes, and the accuracy rate of the model was 70.26%. Meanwhile, according to findings, unsafe conditions, type of accident, and activity type were the most important attributes in the model. Furthermore, the predictor importance of the attributes was compared, and several outcomes were discovered; for instance, the workers’ educational background has greater predictive power than age. On the other hand, the branches of the decision tree pointed out several attribute sequences due to their high rated serious/fatal injury rates. The results of this study can be used in the prevention and mitigation strategies for construction accidents.

Construction Safety during Pandemics: Learning from the Xinjia Express Hotel Collapse during COVID-19 in China

Many construction accidents occur in China each year, leading to a large number of deaths, injures, and property losses. Due to the outbreak of COVID-19, little attention is paid to construction safety, resulting in severe accidents. To prevent construction accidents and learn to how address safety issues in future pandemics, this study proposed an improved STAMP (Systems Theoretic Accident Modeling and Processes) model to analyze the collapse accident of the Xinjia Express Hotel used for COVID-19 quarantine in China. Through the application of the STAMP approach, the causes of the construction accident and the relationship between various causal factors are analyzed from a systematic perspective. The identified causes are divided into five categories: contractors, management of organizations, technical methods, participants, and interactive feedback. Finally, safety recommendations are drawn from this study to improve construction safety and safety management in pandemics.

Analysis of Fire Accident Factors on Construction Sites Using Web Crawling and Deep Learning Approach

Fire safety on construction sites has been rarely studied because fire accidents have a lower occurrence compared to construction’s “Fatal Four”. Despite the lower occurrence, construction fire accidents tend to have a larger severity of impact. This study aims at using news media data and big data analysis techniques to identify patterns and factors related to fire accidents on construction sites. News reports on various construction accidents covered by news media were first collected through web crawling. Then, the authors identified the level of media exposure for various keywords related to construction accidents and analyzed the similarities between them. The results show that the level of media exposure for fire accidents on construction sites is much higher than for fall accidents, which suggests that fire accidents may have a greater impact on the surroundings than other accidents. It was found that the main causes of fire accidents on construction sites are violations of fire safety regulations and the absence of inspections, which could be sufficiently prevented. This study contributes to the body of knowledge by exploring factors related to fire safety on construction sites and their interrelationships as well as providing evidence that the fire type should be emphasized in safety-related regulations and codes on construction sites.

A Research Framework of Mitigating Construction Accidents in High-Rise Building Projects via Integrating Building Information Modeling with Emerging Digital Technologies

The construction of high-rise building projects is a dangerous vocation due to the uniqueness and nature of the activities, as well as the complexity of the working environment, yet safety issues remain crucial in the construction industry. Digital technologies, such as building information modeling (BIM), have been identified as valuable tools for increasing construction productivity, efficiency, and safety. This research aimed to mitigate the accident safety factors in high-rise building projects via integrating BIM with emerging digital technologies in the construction industry, such as photogrammetry, GPS, RFID, augmented reality, (AR), virtual reality (VR), and drone technology. Qualitative research was conceived in the ground theory approach. Forty-five online interviews with construction stakeholders and qualitative data analysis were carried out using the NVivo 11 software package. According to the findings, interviewees were more motivated to use photogrammetry and drone technologies in high-rise building projects in order to increase construction safety. Positive, negative, and neutral attitudes about BIM integration with emerging digital technologies were discovered. Furthermore, a research framework was developed by consolidating research findings that articulate the measures and future needs of BIM integration with other digital technologies to mitigate construction accidents in high-rise building projects. The framework also renders practical references for industry practitioners towards effective and safer construction.

MACHINE LEARNING APPLICATIONS IN CONSTRUCTION SAFETY LITERATURE

Despite significant improvements in safety management practices, the construction industry remains among the most unsafe industries. Thus, it is an essential need to reduce the number of construction accidents through prediction models. In this context, machine learning (ML) methods are extensively used in construction safety literature to predict several outcomes of construction accidents. This study provides a literature review in ML applications in construction safety literature to illustrate research directions for future research. Based on the literature review, 43 journal articles were deeply investigated, and distribution of the articles were classified based on six features: journal, year, adopted machine learning methods, model development approach, utilized dataset, and sub-topics. The findings show that the prediction models in construction safety have taken considerable attention recently. Besides, linear regression and logistic regression were used as a benchmark model, while support vector machine and decision tree were the most frequently implemented ML methods. The number of publications that considered classification problem is two times higher than those adopted regression models. Utilized data were mainly captured from national databases or construction companies. Severity evaluation of construction accidents was the most widely investigated sub-topic, while there is a gap in the literature related to effects of culture on accident outcome and conflict, claim and nonconformance. The findings of this study can provide valuable information for researchers with trends in construction safety literature.