System Dynamics Simulation of Intervention Strategies for Unsafe Behaviors Among Prefabricated Building Construction Workers

Abstract

Prefabricated building construction is a crucial approach to achieving “green” building goals, yet it differs significantly from traditional cast-in-place construction methods. Due to complex cross-operations, high mechanization requirements, insufficient worker experience, and inadequate safety measures, prefabricated construction faces greater safety management challenges than traditional methods. This study identifies 17 key indicators influencing unsafe behaviors among prefabricated building construction workers across four dimensions: physical environment, individual factors, team factors, and safety management. A combined weighting method, integrating the entropy weight method and the coefficient of variation method, was employed to determine the weight of each factor. Based on the weight analysis results, a system dynamics intervention model for unsafe behaviors of prefabricated building construction workers was developed and simulated using the Vensim platform. The results indicate that, among single intervention measures, improving individual comprehensive skills has the most significant effect, reducing unsafe behaviors by 37.78%, though this still falls short of the desired target. In contrast, combined interventions yield superior outcomes, with reductions of 68.87% for individual factor interventions, 59.93% for safety management interventions, 46.32% for team factor interventions, and 20.71% for physical environment interventions. These findings highlight the significant advantages of combined interventions. Based on the study results and the characteristics of prefabricated construction, this paper proposes specific intervention strategies, emphasizing the importance of training and institutional development to systematically enhance workers’ safety behaviors.

1. Introduction

In a pivotal move to advance global sustainable development, China formally proposed the “Dual Carbon” strategy at the 75th session of the United Nations General Assembly in September 2020, committing to achieve peak carbon dioxide emissions by 2030 and carbon neutrality by 2060. The construction industry, as a significant contributor to resource consumption during its development, has triggered widespread global concern regarding its environmental impact [1]. The 2023 China Building Energy Consumption Research Report highlights that China’s construction sector ranks first globally in scale; the annual carbon emissions generated by its construction activities account for approximately 11% of the global building energy consumption [2]. Evidently, traditional construction methods are no longer sufficient to meet the demands of green development in the building industry. In contrast, prefabricated construction offers significant advantages, such as reducing construction waste, shortening project timelines, and improving material utilization efficiency. By simultaneously enhancing construction efficiency and achieving carbon reduction, prefabricated construction has emerged as a pivotal strategy for implementing “Dual Carbon” goals in the construction sector. The Chinese government has vigorously promoted this construction methodology through a series of strategic initiatives [3,4,5]. In recent years, the area of newly constructed prefabricated buildings in China has experienced a remarkable surge, reflecting the rapid adoption of this innovative approach [6]. It is projected that, by 2025, prefabricated buildings will account for over 30% of the total newly added urban construction area [7].

Building safety remains a critical issue that has garnered sustained attention both within and outside the industry. Globally, construction accidents result in thousands of fatalities and numerous severe injuries each year. In the United States, construction incidents accounted for nearly 20% of workplace fatalities in 2022, despite the sector constituting only 6% of the total workforce [8]. According to publicly available data, China recorded a total of 689 production safety accidents in the construction and municipal engineering sector in 2020, resulting in 794 fatalities. Among these incidents, falls from heights, struck-by-object accidents, and crane-related injuries ranked as the top three causes, accounting for 59.07%, 12.05%, and 6.53% of the total, respectively [9]. However, as the proportion of prefabricated buildings in China’s newly constructed area continues to rise, the inherent characteristics of this construction method—such as its high degree of mechanization, extensive use of large, prefabricated components, and frequent human–machine interactions—have significantly increased the reliance on various heavy machinery. This, in turn, has exacerbated risks such as falls from heights and mechanical injuries [10]. Therefore, the safety management of prefabricated building construction faces heightened challenges, necessitating more scientific and targeted intervention measures to mitigate the probability of accidents [11].

Numerous scholars have conducted extensive and in-depth research on building safety and its influencing factors. Heinrich [12], through an investigation of 75,00 industrial accidents, found that 88% of the accidents were caused by unsafe human behaviors. Research by the U.S. Bureau of Labor Statistics [13] indicated that human errors accounted for 90% of crane-related accident fatalities. Fard [14], in a study of 125 local accidents related to prefabricated buildings, discovered that over 70% of the incidents were associated with unsafe behaviors of construction workers. Sun [15] analyzed 79 prefabricated building safety accidents and identified that 63 of them were caused by workers’ unsafe behaviors, concluding that 70% of on-site accidents stemmed from unsafe actions by workers or teams. Fu et al. [16] employed two experimental designs to explore the impact of implicit safety knowledge (work experience) and explicit safety knowledge (professional knowledge) on hazard identification performance. Based on accident characteristic analysis, they pointed out that the occurrence of safety accidents is primarily attributed to unsafe human behaviors. Chi et al. [17] utilized fault tree analysis (FTA) combined with Boolean Algebra to analyze fatal accidents in Taiwan’s construction industry from multiple dimensions, highlighting that unsafe behaviors are the main cause of construction accidents. Therefore, a thorough investigation into the factors influencing construction workers’ unsafe behaviors and the development of targeted intervention measures are crucial for reducing accident risks and enhancing construction safety management.

In recent years, both industry practitioners and those in academia have shown a significant increase in attention to the field of unsafe behavior interventions. Scholars worldwide have achieved fruitful results in research on unsafe behavior interventions, primarily focusing on industries with prominent safety issues, such as coal mining [18,19], railways [20], and construction [21,22,23]. For instance, Li [18] calculated the weights of miners’ unsafe behavior indicators using the analytic hierarchy process (AHP) and established a phased-intervention model of “pre-behavior, during behavior, and post-behavior” based on the principles of system dynamics (SD), proposing intervention measures for different stages. Wang et al. [19] employed principal component analysis (PCA), binary logistic regression (BLR), and Poisson regression (PR) as analytical techniques to estimate the relationships between various influencing factors and unsafe behaviors among coal miners, emphasizing that intervention measures could potentially stimulate safer behaviors. Tong et al. [20] utilized targeted intervention methods to analyze unsafe behavior data of subway construction workers, accurately identifying and locating key nodes of unsafe behaviors, and developing targeted intervention strategies. Ni et al. [21] identified 13 triggering factors of unsafe behaviors among new-generation construction workers based on grounded theory, constructed a multi-level hierarchical model using the DEMATEL-ISM method, and proposed combined intervention strategies. Zhang et al. [22] simulated and analyzed the impact of safety education, penalty mechanisms, key nodes, and operational convenience on the spread of unsafe behaviors based on the SIR-C model, proposing a dual-dimensional intervention strategy focusing on both behavior and propagation paths. Mazlina et al. [23] employed structural equation modeling (SEM) to analyze the influence of safety intervention measures on construction workers’ safety behaviors, highlighting the idea that focusing on technical interventions and selecting appropriate safety practices can significantly improve construction workers’ safety behaviors. The aforementioned studies utilized various methods and techniques such as structural equation modeling, system dynamics, and targeted intervention methods to summarize influencing factors of unsafe behaviors, analyze the mechanisms of unsafe behavior occurrence, and develop intervention strategies, while also constructing models to validate their effectiveness. These studies provide valuable insights and methodologies for the research in this project.

Through literature review, it is evident that research in the field of prefabricated building construction safety primarily focuses on construction safety accident analysis [14,24], risk management [25,26], and intervention measures [27,28,29]. For instance, Fard [14] analyzed safety accidents in prefabricated buildings in the United States. The study found that the most common accidents were falls, and the most frequent cause was structural instability. It was suggested that the primary methods to maintain safety at prefabricated construction sites include ensuring the structural stability of stacking, hoisting, and installing prefabricated components, improving safety protection equipment for high-altitude installations, and providing specialized safety training for prefabricated construction. James [24] proposed that standardized use of mechanical equipment can effectively reduce the occurrence of safety accidents in prefabricated buildings. Xin et al. [25] established a comprehensive evaluation index system for construction risks in prefabricated buildings and introduced blind number theory to achieve a comprehensive risk assessment. Chang et al. [26] introduced the PTF-VIKOR model based on prospect theory, constructing a risk assessment index system for prefabricated building construction from five aspects: personnel, equipment, management, technology, and environment. They used interval-valued Pythagorean fuzzy numbers (IVPFNs) for weighting and validated the model’s effectiveness through risk analysis of prefabricated building projects. Chu [27] proposed a framework based on monocular vision for biomechanical analysis or ergonomic posture assessment to identify incorrect postures of prefabricated building construction workers during tasks, thereby improving work efficiency and safety. Shen et al. [28] developed a prefabricated building construction monitoring system based on the Revit platform, achieving automatic risk identification and response mechanisms during construction through the integration of BIM technology. Zhou et al. [29] utilized BIM technology to construct a construction process model, systematically identifying and eliminating potential safety hazards through digital simulation of construction phases, and further proposed a safety management process for prefabricated building construction. Overall, existing research primarily adopts a macro perspective, focusing on in-depth analysis of risk management and accident influencing factors, and proposes systematic safety management measures combined with computer technology, providing theoretical foundations and practical guidance for the scientific and intelligent management of construction safety.

In recent years, some scholars have shifted their research focus to micro-level subjects, delving into the safety issues of prefabricated building construction from the perspective of construction workers. For instance, Wang [30] utilized structural equation modeling from a social psychology perspective to reveal the formation mechanism and group differences of intentionally unsafe behaviors among prefabricated building construction workers, concluding that workers’ behavioral attitudes have a significant positive impact on intentional unsafe behaviors. Yuan [31] established an intervention model for unsafe behaviors of crane operators in prefabricated buildings from the perspective of accident causation theory, proposing intervention recommendations specifically targeting crane-related accidents. Xu [32] employed the hazard and operability (HAZOP) analysis method to investigate the causes and potential consequences of unsafe behaviors among lifting workers and proposed corresponding response strategies. Zhang [33] utilized computer vision recognition to analyze unsafe behaviors of construction workers and suggested preventive and corrective measures. From the existing literature, two significant gaps in the research on unsafe behaviors of prefabricated building construction workers are evident: First, there is a lack of targeted research. Current studies on workers’ unsafe behaviors predominantly focus on traditional construction fields. However, prefabricated buildings differ significantly from traditional construction in terms of construction techniques, risk characteristics, and personnel allocation, making safety intervention measures for traditional construction difficult to directly apply. This highlights the urgent need for targeted research. Second, the scientific rigor and practicality of existing intervention strategies are insufficient. Current research largely remains at the qualitative analysis level, lacking quantitative effect evaluation and comparative analysis, which hinders effective practical guidance.

Therefore, this study focuses on the field of prefabricated building construction safety, systematically exploring the influencing factors and intervention strategies for workers’ unsafe behaviors. First, by combining literature analysis and empirical research, key influencing factors of unsafe behaviors are identified, and the entropy weight method and coefficient of variation method are employed to assign weights to the indicators, achieving scientific quantification of the influencing factors. Second, a system dynamics model is constructed to simulate the dynamic evolution process of unsafe behaviors after the implementation of intervention measures, enabling the quantitative evaluation of the effectiveness of interventions. Simultaneously, the integration of the entropy weight method, coefficient of variation method, and system dynamics optimizes the quantitative expression of relationships among factors, enhancing the model’s scientific rigor and reliability. Finally, targeted intervention strategies are proposed based on simulation results, providing theoretical foundations and practical support for reducing unsafe behaviors and improving the safety management level of prefabricated building construction.

2. Identification of Factors Influencing Unsafe Behaviors of Construction Personnel

The prerequisite for managing construction workers’ safe behavior is to identify the influencing factors of safety behavior [34]. Based on the characteristics of prefabricated building construction, this study analyzes existing safety accident cases in China’s prefabricated construction industry and reviews relevant research findings [11,35]. As a result, the influencing factors of unsafe behaviors among prefabricated building construction workers are categorized into four aspects: physical environment, individual factors, team factors, and safety management.

2.1. Physical Environment

Research shows that the external objective environment affects employees’ risk perception, risk identification, and risk decision making, thereby influencing their behavior [36]. In prefabricated building construction, the environmental factors that impact workers’ unsafe behaviors mainly include the natural environment and the construction site environment.

Compared to traditional construction, prefabricated building construction can reduce on-site construction time by 30% to 50%. However, on-site operations in prefabricated construction, including the transportation, assembly, and hoisting of prefabricated components, are still susceptible to natural environmental factors such as adverse weather conditions [36,37], which can interfere with workers’ behavior to some extent. In common adverse weather conditions such as fog, gusty winds, and heavy rain, additional uncontrollable factors may arise, affecting construction workers’ risk perception and judgment, potentially leading to unsafe behaviors [11,38].

The construction site environment primarily refers to the conditions of site cleanliness and order, as well as the performance status of machinery and equipment. The quality of the construction site environment directly affects workers’ psychological states. Working in a disorganized and dirty environment can lead to negative emotions, distract workers from safety awareness, and increase the likelihood of unsafe behaviors [38,39]. The condition of facilities and equipment includes both the performance of machinery and the integrity of safety facilities. Well-functioning machinery and comprehensive safety equipment can enhance operational safety to a certain extent, reducing the occurrence of unsafe behaviors [11,40].

2.2. Individual Factors

Construction workers, as individuals with independent thinking and action capabilities, are primarily responsible for most unsafe behaviors in prefabricated construction. Individual factors are considered intrinsic influences leading to unsafe behaviors among the workforce [34,38].

In traditional cast-in-place construction, the required skills are relatively simple. However, in prefabricated construction, most tasks are related to precast components. For example, the responsibilities of an assembly worker include hoisting, installation adjustment, grouting, and concrete pouring, making it a multidisciplinary trade [35]. As a result, prefabricated construction places higher demands on workers’ professional technical skills, stress tolerance, and adaptability. The individual factors influencing unsafe behaviors in construction refer to workers’ unsafe actions based on their physiological, psychological cognition, and habitual tendencies. Depending on the degree of subjective influence, these unsafe behaviors can be categorized into unconscious and conscious unsafe behaviors [41].

Unconscious unsafe behavior is defined as a subconscious behavioral choice made by construction workers when they experience physical or mental fatigue, physiological deficiencies, a lack of safety knowledge and skills, or insufficient safety experience [19,42,43,44]. As prefabricated construction becomes more specialized and standardized, most workers involved in such projects transition from traditional construction backgrounds. Consequently, they often lack the necessary knowledge, skills, and experience [45]. This transition makes them more susceptible to perceptual errors, increasing the likelihood of unsafe behavior [35].

Conscious unsafe behavior refers to deliberate unsafe actions influenced by workers’ safety awareness, attitudes, and sense of responsibility [46]. On one hand, due to a lack of safety awareness, construction workers may be driven by personal interests or influenced by others to consciously imitate or attempt unsafe behaviors [47,48]. On the other hand, an improper safety attitude and a lack of safety responsibility may lead to unsafe actions [34,45], such as deliberately violating regulations, disregarding discipline, and failing to follow instructions.

2.3. Group Factors

The study by Panagiotis Mitropoulos et al. indicates that in high-risk industries such as aviation, healthcare, oil, and construction, teamwork can effectively prevent and reduce unsafe behaviors among personnel [49]. A well-coordinated team mechanism and a positive team atmosphere are fundamental to promoting teamwork.

During the construction of prefabricated buildings, various trades are involved, including component assemblers, signal workers, crane operators, embedded workers, steel processing and distribution workers, and grouting workers. The construction process is intricate and typically requires close coordination among team members [35,50,51]. In this context, higher demands are placed on construction workers’ communication and coordination skills. Establishing an effective team coordination mechanism and fostering teamwork and communication skills among prefabricated building construction workers are crucial measures for preventing unsafe behaviors.

The concept of team atmosphere includes both the safety climate of the project team and the interpersonal relationships within the team [52]. The concept of safety climate was first introduced by renowned scholar Zohar in the early 1980s, emphasizing the importance of safety among organizational members [49]. Studies have shown that improving the safety climate can significantly reduce unsafe behaviors among construction workers. A strong safety climate within the project team encourages workers to follow standardized procedures, thereby minimizing unsafe behaviors [45,53]. Additionally, positive interpersonal relationships help alleviate excessive work pressure, lack of focus, and negative occupational psychology, such as complacency, risk-taking, luck-seeking, and resistance to safety measures, thereby significantly reducing the likelihood of unsafe behaviors [54,55].

2.4. Security Management

According to Heinrich’s accident causation theory and the trajectory intersection theory, accidents are closely related to safety management factors. By utilizing management measures such as guidance, organization, and control, safety behavior interventions can be implemented to effectively correct or even eliminate unsafe behaviors among construction workers [56]. Under the integrated construction model of prefabrication in factories and on-site assembly, prefabricated buildings impose significantly higher requirements for standardized and process-based construction due to technical challenges such as multidisciplinary coordination and high-precision matching. By implementing full-process supervision and management of key stages, including prefabricated component production, transportation, hoisting, and connection, safety risks such as production errors and construction interface defects can be effectively mitigated [31,57]. Safety management is specifically reflected in two aspects: system establishment and personnel management [58].

Regulations provide clear standards and guidelines for safe behavior, reducing deviations in execution. A well-established regulatory framework can significantly lower the occurrence rate of unsafe behaviors among construction workers [20]. Prefabricated construction enterprises can guide, motivate, restrain, and regulate workers’ behavior by establishing a safety investment guarantee system [50,59], a safety behavior reward and punishment system [48,50], and an emergency safety management system [60,61], thereby reducing unsafe behaviors.

Unsafe behavior refers to individuals violating labor discipline, operational procedures, and other hazardous actions. Therefore, safety management of unsafe behavior focuses on personnel management, specifically through safety supervision [47,62,63] and leadership [36,64]. The supervision mechanism can quickly correct unsafe behavior through instant feedback and rewards or penalties [65], forming a positive cycle of continuous improvement. Effective supervision leads to better safety outcomes [66]. Supervisors’ proactive safety behavior on construction sites has a positive impact on workers’ safety attitudes [67]. During the prefabricated building construction process, supervision of the construction site should be strengthened to ensure the implementation of safety management systems. Managers and team leaders, as the direct supervisors of workers, should take the lead and set an example, encouraging all personnel to comply with safety regulations and actively participate in safety initiatives [34].

In summary, the fishbone diagram of the influencing factors of unsafe behavior among prefabricated building construction workers is shown in Figure 1. The sources from the literature are listed in

Table 1. The sources and weights of factors influencing unsafe behavior.

Categories	Influence Factors	Source	Weight Coefficient 1
Physical environment (0.14)	Weather conditions	[36,37]	0.04
	Construction layout on site	[38,39]	0.05
	Facility and equipment status	[11,40]	0.05
Individual factors (0.35)	Safety knowledge and skills	[42,43]	0.11
	Physical and mental state	[34,43,44]	0.03
	Safety experience	[19,44]	0.11
	Safety attitude and sense of responsibility	[34,45]	0.05
	Safety cognition and consciousness	[47,48]	0.05
Group factors (0.24)	Group communication	[35,50,51]	0.06
	Group coordination and cooperation	[35,50,51]	0.04
	Group safety atmosphere	[45,53]	0.10
	Interpersonal relationship	[54,55]	0.04
Safety management (0.27)	Safety behavior incentive system	[48,50]	0.08
	Safety investment guarantee system	[50,59]	0.07
	Safety emergency management system	[60,61]	0.03
	Safety supervision	[47,62,63]	0.05
	Safety leadership	[36,64]	0.04

¹ The weight is obtained by multiplying the entropy weight and the coefficient of variation weight (Equation (10)).

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3. Determination of the Weighting of Factors Influencing Unsafe Behaviors

In the determination of index weights, the main methods include subjective weighting, objective weighting, and combined weighting. Subjective weighting has a high degree of arbitrariness and is less reliable than objective weighting. However, objective weighting also has its own limitations. By combining multiple objective weighting methods, the results become more reliable and valuable for reference [68]. The entropy weight method quantifies the effectiveness of information transmission by calculating the information entropy value of each indicator, thereby assigning corresponding weights to the indicators. The weight results rely solely on the distribution characteristics of the data, effectively eliminating the interference of subjective factors [69]. The coefficient of variation method measures the degree of data dispersion by calculating the ratio of the standard deviation to the mean. Its weight allocation is entirely based on the dispersion characteristics of the data itself, eliminating the influence of data units and magnitudes, thus ensuring strong objectivity [70]. The entropy weight method provides a basis for weight allocation from the perspective of information content, while the coefficient of variation method supplements weight calculation from the perspective of data dispersion. The combination of both methods can more accurately reflect the importance of each indicator. Chen et al. (2022) [71] validated in the evaluation of water resource system resilience that combining the entropy weight method with the coefficient of variation method can effectively avoid biases in evaluation results caused by a single method. This combination ensures a more reasonable weight distribution, leading to more stable and reliable evaluation outcomes [71]. Therefore, this study adopts the entropy weight method combined with the coefficient of variation method to determine the weights of factors influencing unsafe behaviors. Consequently, this paper elects to utilize the entropy weight method in conjunction with the coefficient of variation method to ascertain the weights of the factors influencing unsafe behaviors.

3.1. Data Sources

This study collects data through a questionnaire survey, which consists of three main sections: respondents’ basic information, safety evaluation of prefabricated buildings, and evaluation of factors influencing unsafe behaviors of construction workers in prefabricated building projects. All influencing factors are assessed using a five-point Likert scale, where “severely influential, highly influential, moderately influential, slightly influential, and minimally influential” correspond to scores of 5, 4, 3, 2, and 1, respectively. The survey respondents included researchers, construction workers, and management personnel engaged in prefabricated building projects. A total of 200 questionnaires were distributed, with 171 collected, resulting in a recovery rate of 85.5%. After eliminating incomplete and inconsistent responses, a total of 153 valid questionnaires were obtained.

The 153 valid questionnaires collected were preliminarily analyzed, and the respondents’ basic information is presented in

Table 2. Basic information of the respondents.

Information	Classification	Frequency	Percentage
Gender	Men	129	84.31%
	Women	24	15.69%
Educational background	Primary school	12	7.84%
	Junior high school	25	16.34%
	High school	33	21.57%
	Junior college	29	18.95%
	Bachelor’s degree or above	54	35.29%
Age	18–25	19	12.42%
	26–35	40	26.14%
	36–45	63	41.18%
	45–55	23	15.03%
	55 or above	8	5.23%
Nature of Work	Construction personnel	67	43.79%
	Site management personnel	36	23.53%
	Architectural designers	24	15.69%
	University and enterprise researchers	26	16.99%

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Before using the data, SPSS 22.0 software was employed to conduct reliability and validity tests on the questionnaire data. The reliability test showed a Cronbach’s Alpha value of 0.844, indicating satisfactory reliability. The validity test resulted in a KMO value of 0.837 and a Bartlett’s sphericity test Sig. value of 0.000, demonstrating that the questionnaire data had satisfactory validity.

3.2. Calculation of the Weights of the Indicators of Impact Factors

3.2.1. Entropy Weighting Method to Determine Weights

The entropy weight method is an objective weighting method that reflects the amount of information provided by an indicator through its information entropy. The smaller the information entropy of an indicator, the greater its degree of variation, indicating that it provides more information and should be assigned a higher weight. The calculation steps of the entropy weight method are as follows:

(1) According to Figure 1, the evaluation index system is determined, and the collected questionnaire scoring data are organized into the original matrix. The positive and negative indicators are standardized using Equations (1) and (2) to obtain normalized vectors, ${\mathrm{r}}_{\mathrm{i}\mathrm{j}}$. A positive indicator means that, the higher its value, the greater its positive effect on the research problem, whereas a negative indicator has the opposite effect. The corresponding processing is performed as follows:

$${\mathrm{r}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-{\mathrm{x}}_{\mathrm{j} \mathrm{m}\mathrm{i}\mathrm{n}}}{{\mathrm{x}}_{\mathrm{j} \mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{x}}_{\mathrm{j} \mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(1)

$${\mathrm{r}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{j} \mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{{\mathrm{x}}_{\mathrm{j} \mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{x}}_{\mathrm{j} \mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(2)

In the formula, ${\mathrm{x}}_{\mathrm{i}\mathrm{j}}$ represents the index value of the $\mathrm{i}$-th influencing factor under the $\mathrm{j}$-th evaluation indicator, while ${\mathrm{x}}_{\mathrm{j}\max }$ and ${\mathrm{x}}_{\mathrm{j}\min }$ denote the maximum and minimum values of the $\mathrm{j}$-th evaluation indicator, respectively. ${\mathrm{r}}_{\mathrm{i}\mathrm{j}}$ represents the standardized index value of the $\mathrm{i}$-th influencing factor under the $\mathrm{j}$-th evaluation indicator.

Then, the standardized indicator data are used to construct the indicator data relationship matrix, $\mathrm{R}$:

$$\mathrm{R}={\left\{{\mathrm{r}}_{\mathrm{i}\mathrm{j}}\right\}}_{\mathrm{m}\times \mathrm{n}}=\left[\begin{array}{cccc}{\mathrm{r}}_{11}& {\mathrm{r}}_{12}& \cdots & {\mathrm{r}}_{1\mathrm{n}}\\ {\mathrm{r}}_{21}& {\mathrm{r}}_{22}& \cdots & {\mathrm{r}}_{2\mathrm{n}}\\ ⋮& ⋮& ⋮& ⋮\\ {\mathrm{r}}_{\mathrm{m}1}& {\mathrm{r}}_{\mathrm{m}2}& \cdots & {\mathrm{r}}_{\mathrm{m}\mathrm{n}}\end{array}\right]$$

(3)

In the formula, $\mathrm{R}$ consists of standardized index data; n represents the number of evaluation indicators; and m denotes the number of influencing factors.

(2) The entropy of the normalized vector for the $\mathrm{j}$-th evaluation indicator is calculated as follows:

$${\mathrm{e}}_{\mathrm{j}}=-\mathrm{k}\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{f}}_{\mathrm{i}\mathrm{j}}\ln {\mathrm{f}}_{\mathrm{i}\mathrm{j}}$$

(4)

In the formula, ${\mathrm{f}}_{\mathrm{i}\mathrm{j}}={\mathrm{r}}_{\mathrm{i}\mathrm{j}}/{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}$; $\mathrm{k}=1/\mathrm{l}\mathrm{n} \mathrm{m}$. The greater the entropy of an indicator, the smaller the difference between its value and the optimal value of that indicator.

The entropy weight coefficient of the $\mathrm{j}$ entropy weight coefficient of the evaluation indicator, ${\mathrm{w}}_{1\mathrm{j}}$:

$${\mathrm{w}}_{1\mathrm{j}}=(1-{\mathrm{e}}_{\mathrm{j}})/\sum _{\mathrm{j}=1}^{\mathrm{n}}(1-{\mathrm{e}}_{\mathrm{j}})$$

(5)

In the formula, ${\mathrm{e}}_{\mathrm{j}}$ represents the entropy of the normalized vector, ${\mathrm{r}}_{\mathrm{j}}$, for the $\mathrm{j}$-th evaluation indicator.

3.2.2. Weight Determination by Using the Coefficient of Variation Method

The coefficient of variation method is an objective weighting method that determines weights based on the standard deviation of an indicator, where a larger amount of information corresponds to a higher weight. The weighting steps are as follows:

$${\overline{\mathrm{x}}}_{\mathrm{j}}={\displaystyle \frac{1}{\mathrm{m}}}\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}$$

(6)

$${\mathsf{\sigma }}_{\mathrm{j}}=\sqrt{{\displaystyle \frac{1}{\mathrm{m}}}\sum _{\mathrm{i}=1}^{\mathrm{m}}{({\mathrm{x}}_{\mathrm{i}\mathrm{j}}-{\overline{\mathrm{x}}}_{\mathrm{j}})}^2}$$

(7)

$${\mathsf{\delta }}_{\mathrm{j}}={\mathsf{\sigma }}_{\mathrm{j}}/{\overline{\mathrm{x}}}_{\mathrm{j}}$$

(8)

$${\mathrm{w}}_{2\mathrm{j}}={\mathsf{\delta }}_{\mathrm{j}}/\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\delta }}_{\mathrm{j}}$$

(9)

In the formula, ${\overline{\mathrm{x}}}_{\mathrm{j}}$ represents the mean of the $\mathrm{j}$-th indicator, ${\mathsf{\sigma }}_{\mathrm{j}}$ represents the standard deviation of the j-th indicator, ${\mathsf{\delta }}_{\mathrm{j}}$ represents the coefficient of variation of the j-th indicator, and ${\mathrm{w}}_{2\mathrm{j}}$ represents the weight coefficient of the j-th evaluation indicator.

3.2.3. Combined Weight Calculation by Entropy Weighting Method of Coefficient of Variation

Based on the above two weighting methods, the entropy weight and the coefficient of variation weight are obtained. The multiplicative combination method [72] is adopted to determine the objective combined weight of the indicators, as follows:

$$\mathrm{W}={\mathrm{w}}_{1\mathrm{j}}{\mathrm{w}}_{2\mathrm{j}}/\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{1\mathrm{j}}{\mathrm{w}}_{2\mathrm{j}}$$

(10)

The weight results of influencing factors of unsafe behavior of prefabricated building construction workers obtained through the combined weighting method are presented in Table 1.

4. Simulation of Intervention Strategies for Construction Workers’ Unsafe Behaviors

4.1. SD Intervention Model

System dynamics was first proposed by Forrester in 1958 to explain and analyze the causal relationships and dynamic feedback processes among internal factors of complex systems. It is particularly useful for analyzing causal relationships in feedback loop structures within various systems and is especially adept at handling nonlinear, multi-feedback, and high-order system problems [73]. Due to its ability to explore the impact of different strategies on system behavior from multiple system levels, combining qualitative analysis as a premise with quantitative analysis, system dynamics is widely applied in the study of interaction mechanisms between factors and behaviors in complex systems [74]. It has been extensively used in intervention studies on unsafe behaviors in complex operational scenarios such as coal mining [75,76], tunnel engineering [77], and civil aviation security screening [78].

Vensim PLE 7.3.5, developed by Ventana Systems, Inc., is a visual modeling tool and one of the widely used system dynamics simulation software globally. It offers significant advantages in conceptualizing dynamic systems, simulation modeling, predictive analysis, and strategy evaluation. The Vensim software features a simple and user-friendly interface, allowing researchers to draw various model structures such as causal feedback diagrams and stock-flow diagrams in the visual modeling window. Additionally, the equation editor enables the input of relational equations between variables, facilitating model construction and simulation analysis. Therefore, based on the principles of system dynamics, this study utilizes Vensim software to construct an intervention model for unsafe behaviors of prefabricated building construction workers, evaluating the effects of different intervention measures. As shown in Figure 2, the model includes 5 state variables, 5 rate variables, 17 auxiliary variables, and 12 constants, as detailed in

Table 3. Variable set in SD model of unsafe behavior of prefabricated construction personnel.

Variable Categories	Variables
State variable	Unsafe behavior level, physical environment level, individual safety level, group safety level, and safety management level.
Rate variable	Reduction in unsafe behavior level, increment in physical environment level, increment in individual safety level, increment in group safety level, and increment in safety management level.
Auxiliary variables	Weather conditions, construction layout on site, facility and equipment status, safety knowledge and skills, physical and mental state, safety experience, safety attitude and sense of responsibility, safety cognition and consciousness, group communication, group coordination and cooperation, group safety atmosphere, interpersonal relationship, safety behavior incentive system, safety investment guarantee system, safety emergency management system, safety supervision, and safety leadership.
Constant	Optimize the construction work environment, strengthen risk prevention and control, strengthen facility and equipment management, pay attention to the physical and mental health of personnel, improve personal comprehensive skills, correct attitude towards safe work, strengthen group communication and cooperation, clarify the division of labor among group members, creating a group safety atmosphere, establish a safety management system, strengthen safety supervision and inspection, and optimize construction organization design.

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4.2. Intervention Simulation

In this study, the simulation period is set to 12 months, with a time unit of 1 month. The initial value of the unsafe behavior level is set to 200 based on model settings and existing research [36,61]. After repeated adjustments and testing, the final values for constants and state variables were determined through expert evaluation, considering variable attributes, functions, and model requirements. The initial values of state variables (unsafe behavior level, physical environment level, individual factors level, team factors level, and safety management level) are set as (200, 68, 68, 69, 69). The initial intervention level is uniformly set as a constant of 35, and the level of unsafe behavior is calculated to be 183.597 in the initial state. Based on the weight values obtained from the combined weighting method and the system dynamics flowchart, the functional relationships of the relevant variables are established.

All formulas in this study are derived from the DPS data processing module in Vensim software and the relevant literature [5,7,9]. Based on the principles of system dynamics, the DYNAMO function is used to establish mathematical relationships between variables, constructing simulation equations for parameter variables in the main loop [79].

$$\mathrm{U}\mathrm{n}\mathrm{s}\mathrm{a}\mathrm{f}\mathrm{e} \mathrm{b}\mathrm{e}\mathrm{h}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{o}\mathrm{r} \mathrm{l}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{l} = \mathrm{I}\mathrm{N}\mathrm{T}\mathrm{E}\mathrm{G}(-\mathrm{R},\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{u}\mathrm{n}\mathrm{s}\mathrm{a}\mathrm{f}\mathrm{e} \mathrm{b}\mathrm{e}\mathrm{h}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{o}\mathrm{r} \mathrm{l}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{l})$$

(11)

In the equation, INTEG represents the integral function, indicating the value of the state variable. $\mathrm{R}$ is the flow rate variable, representing the reduction in unsafe behavior level, and its equation is as follows:

$$\mathrm{R}={\mathsf{\alpha }}_1{\mathrm{L}}_1+{\mathsf{\alpha }}_2{\mathrm{L}}_2+{\mathsf{\alpha }}_3{\mathrm{L}}_3+{\alpha }_4{\mathrm{L}}_4$$

(12)

Here, α represents the weights of influencing factor (${\mathsf{\alpha }}_1, {\mathsf{\alpha }}_2{, \mathsf{\alpha }}_3{, \mathsf{\alpha }}_4$) = (0.14, 0.35, 0.24, 0.27); $\mathrm{L}$ represents the state variables; ${\mathrm{L}}_1,{\mathrm{L}}_2,{\mathrm{L}}_3,{\mathrm{L}}_4$ represents the levels of the physical environment, individual factors, team factors, and safety management, respectively.

Based on the principle of the control variable method, this study employs simulation methods to adjust constants and evaluate the effects of different intervention strategies. Building upon the initial simulation results, each intervention measure’s initial value is doubled in turn to simulate the changes in unsafe behavior after implementing a single intervention strategy.

The greater the difference between the simulation results after adjusting the simulation constants and the initial state, the more significant the intervention effect [4].

5. Results

5.1. Analysis of Single Intervention Results

Figure 3, Figure 4, Figure 5 and Figure 6 show the dynamic changes in unsafe behavior levels over a 12-month period under the initial state and after implementing single intervention measures for each subsystem. The corrective effects of each intervention measure are presented in Table 3.

Figure 3 presents the simulation results of unsafe behavior levels after implementing a single intervention measure for the physical environment subsystem. The physical environment interventions, ranked in descending order of effectiveness, are as follows: strengthening risk prevention and control, enhancing facility and equipment management, and optimizing construction operations.

Figure 4 presents the simulation results of unsafe behavior levels after implementing a single intervention measure for the individual factors subsystem. The intervention measures, ranked in descending order of effectiveness, are as follows: improving personal comprehensive skills, fostering a correct work safety attitude, and ensuring workers’ physical and mental well-being.

Figure 5 presents the simulation results of unsafe behavior levels after implementing a single intervention measure for the team factors subsystem. Among the team interventions, creating a team safety climate has the most significant effect, followed by enhancing team communication and coordination, and clarifying team member roles and responsibilities.

Figure 6 presents the simulation results of unsafe behavior levels after implementing a single intervention measure for the safety management subsystem. The intervention effectiveness of safety management measures, ranked from highest to lowest, is as follows: establishing a safety management system, strengthening safety supervision and inspection, and optimizing construction organization design.

As shown in Figure 3, Figure 4, Figure 5 and Figure 6, the simulation results after implementing intervention measures align with the trends observed in the initial state, indicating that the model is relatively stable [36]. By comparing the simulation results of each intervention measure with the initial state reduction rate, it can be observed that the improvement of individual comprehensive skills (37.78% reduction) has the most significant effect on suppressing unsafe behaviors. This is followed by the correction of safety work attitudes in individual interventions (36.07% reduction) and the establishment of safety management systems in safety management interventions (29.96% reduction). Skills training plays a significant role in improving workers’ perceived behavioral control and enhancing safety awareness, thereby reducing the occurrence of unsafe behaviors [80]. By improving workers’ technical abilities, their confidence in safe operations can be strengthened, ultimately reducing safety risks caused by insufficient skills [35]. Yuan Xin conducted an intervention study on unsafe behaviors of hoisting workers in prefabricated buildings using a specific project in Shanghai as a case study. The research found that implementing hoisting safety skills training can significantly reduce unsafe behaviors during hoisting operations [31]. Through observation and analysis, it can be concluded that, after implementing a single intervention measure, the level of unsafe behavior among construction workers decreases to some extent. However, the reduction is relatively limited, with a maximum decrease of only 37.78%. It is evident that adopting a single intervention strategy is insufficient to effectively reduce the level of unsafe behavior among construction workers. Combined interventions have been widely applied in the construction, transportation, and coal industries and have been proven to significantly reduce the occurrence of unsafe behaviors [18,81,82]. Therefore, this study will further conduct combined intervention simulations to verify their actual effectiveness in reducing unsafe behaviors.

5.2. Analysis of Combined Intervention Results

Combined intervention refers to the concurrent implementation of multiple intervention measures, aiming to more effectively prevent and control unsafe behaviors when they occur [36,83]. In this study, the initial values of multiple unsafe behavior interventions within each intervention subsystem were simultaneously doubled to simulate the combined intervention for each subsystem. As shown in Figure 7, the simulation results illustrate the effects of combined interventions implemented in the individual factors, physical environment, safety management, and group factors subsystems. The corrective effects of each combined intervention are shown in

Table 4. Comparison of the effects of different intervention strategies.

Intervention Subsystem	Intervention Measures	Unsafe Behavior Level	Drop Rate %
Physical environment	Strengthen risk prevention and control	161.41	12.08%
	Optimize the construction work environment	176.48	3.88%
	Strengthen facility and equipment management	173.25	5.64%
Individual factors	Pay attention to the physical and mental health of personnel	171.71	6.47%
	Improve personal comprehensive skills	114.23	37.78%
	Correct attitude towards safe work	117.38	36.07%
Group factors	Strengthen group communication and cooperation	161.86	11.84%
	Clarify the division of labor among group members	172.73	5.92%
	Creating a group safety atmosphere	145.57	20.71%
Safety management	Establish a safety management system	128.59	29.96%
	Optimize construction organization design	156.09	14.98%
	Strengthen safety supervision and inspection	142.06	22.63%
Combination intervention	Physical environment intervention	145.57	20.71%
	Individual factor intervention	57.15	68.87%
	Group factor intervention	98.55	46.32%
	Security management intervention	73.58	59.93%

. The results indicate that, after implementing combined interventions, the level of unsafe behavior significantly decreased, demonstrating that combined interventions have a remarkable effect in mitigating unsafe behaviors among prefabricated building construction workers. Compared to the initial state of unsafe behavior levels, the combined intervention targeting individual factors had the most significant inhibitory effect, with a reduction of 68.87%, followed by safety management intervention (59.93%), group factor intervention (46.32%), and physical environment intervention (20.71%). The simulation results align with the weight ranking derived from the study on influencing factors of unsafe behavior among construction workers.

This is because, according to the Theory of Planned Behavior, an individual’s behavioral intention is primarily determined by three core factors: attitude toward the behavior, subjective norms, and perceived behavioral control [84]. The combined intervention targeting individual factors simultaneously influences these three dimensions by correcting work attitudes, enhancing comprehensive skills, and addressing physical and mental well-being. These measures complement and reinforce each other, creating a synergistic effect that more effectively promotes the formation and maintenance of safe behavior among workers [64,85]. Management interventions primarily influence individual and team behavior choices and execution through clear institutional and regulatory frameworks, leadership demonstration, and supervision feedback. These interventions are highly controllable and directly impactful [61,86]. In contrast, group and physical interventions are relatively less effective due to the influence of group dynamics, social norms, and individual differences. Management interventions primarily influence individual and team behavioral choices and execution through clear institutional and regulatory frameworks, leadership demonstration, and supervision feedback. These interventions are highly controllable and directly impactful [61,86]. In contrast, group and physical interventions are relatively less effective due to the influence of group dynamics, social norms, and individual differences.

6. Discussion on Intervention Strategies

Based on the characteristics of prefabricated building construction, this study further describes the specific intervention measures from four aspects, as shown in

Table 5. Intervention measures for unsafe behaviors of prefabricated building construction workers.

Categories	Intervention Strategies	Detailed Contents
Physical environment	Strengthen risk prevention and control	Take safety precautions against severe weather: temporary suspension measures are implemented for on-site construction activities such as installation and dismantling of lifting machinery and equipment, component hoisting, and outdoor high-altitude operations. Isolate hazardous sources and build security lines of defense: post hazard warning signs and installing safety facilities, including edge and opening protections.
	Optimize the construction work environment	Ensure smooth and safe roads: the roads within the construction site should be level and unobstructed to meet the transportation requirements of prefabricated components. Plan the location of tower cranes scientifically to ensure the safety and efficiency of hoisting: the location of loading and unloading of components should be within the radius of the tower crane, reserving space for the use of component turnover. Strengthen on-site construction management to ensure a clean and safe environment.
	Strengthen facility and equipment management	Use qualified operating equipment: select transport and hoisting equipment, support devices, and hoisting tools that match the specifications and dimensions of prefabricated components and have good working performance. Ensure on-site safety protection: all personnel entering the construction site must wear safety helmets correctly, and high-altitude workers must use safety belts and be equipped with safety protection equipment that meets their job requirements. Improve equipment management systems: regularly organize inspections and maintenance of large lifting machinery such as tower cranes and external wall protection frames, replace worn parts in time, and keep equipment inspection records.
Individual factors	Pay attention to the physical and mental health of personnel	Focus on physical health and enhance happiness: establish a comprehensive health monitoring system for all personnel, implement flexible working arrangements, and create a diversified platform for cultural and sports activities. Build a dedicated mental health team to boost employee engagement: improve employee psychological income through psychological counseling, emotional counseling, psychological training, and other measures.
	Improve personal comprehensive skills	Develop a customized training system to optimize job competency alignment: set up corresponding training programs for specialized trades in prefabricated construction, such as welding, lifting, grouting, and component assembly workers. Promote intelligent training tools to enhance interaction and effectiveness: conduct immersive safety training using VR technology to enhance workers’ vigilance and safety awareness. Implement a phased skills certification system and a mandatory certificate system: strict practice with access standards to ensure that construction workers have the corresponding professional abilities.
	Correct attitude towards safe work	Conduct systematic capacity building and behavioral interventions to enhance safety literacy: develop a scale for unsafe behavior tendencies and implement behavioral correction measures for construction personnel at risk of unsafe behaviors. Deepen risk awareness through case-based education and reshape safety values.
Group factors	Strengthen group communication and cooperation	Conduct regular daily work communication and safety production technical briefings: organize specialized safety production technical exchanges before high-altitude and hoisting operations for large and complex components to ensure safe operation. Establish a safety collaboration mechanism: clarify the cooperation relationships and work coordination requirements among various professional trades, and ensure effective work coordination and collaboration.
	Clarify the division of labor among group members	Divide labor clearly and implement responsibilities to promote the construction of cross operations orderly.
	Create a group safety atmosphere	Improve safety publicity to create a safe construction atmosphere. Regularly organize team-building activities to enhance team cohesion.
Safety management	Establish a safety management system	Develop safety operating procedures to standardize construction behaviors: establish safety operating procedures for PC component hoisting, mold assembly, product transfer, storage, and loading, and improve safety management systems. Establish a safety performance evaluation system: establish a safety behavior reward fund to incentivize safe practices, while also disciplining those who violate regulations, thereby guiding the standardization of safety behaviors. Develop emergency plans and response processes and conduct regular emergency drills. Develop a safety investment guarantee system: based on the characteristics of prefabricated building construction, ensure and rationally allocate the investment of human resources, material resources, and financial resources.
	Optimize construction organization design	Improve the process flow to ensure smooth and safe construction: establish standardized construction process workflows, and develop reasonable transportation, storage, and hoisting sequences, as well as construction schedules, based on component specifications, types, and usage locations. Develop specialized hoisting plans and support schemes: select representative units for trial installation of prefabricated components to refine the construction plan.
	Strengthen safety supervision and inspection	Use drone monitoring to ensure the implementation of safety systems: monitor and provide real-time warnings for worker behavior in critical construction site areas (e.g., component hoisting zones, grouting operation zones). Establish a safety management team to conduct regular safety inspections: develop a key safety issue checklist, promptly address identified problems, and eliminate potential safety hazards. Set up a leadership behavior positive conduction system: leaders at all levels set an example by consciously observing all safety systems, leading their groups to follow suit.

: physical environment, individual factors, team factors, and safety management.

Based on the analysis of intervention simulation results, priority should be given to strengthening interventions from both individual factors and safety management. Specifically, it is recommended to enhance workers’ comprehensive skills through intensive training while relying on institutional development to regulate safety behaviors, thereby improving overall safety awareness and execution.

Safety training is considered an important approach to enhancing workers’ comprehensive skills and risk awareness. Wang [73] pointed out that conducting safety training and improving skill levels can effectively suppress the spread of unsafe behaviors. The specific strategies are as follows: Customized training system—optimize job competency matching; develop specialized safety training courses for different trades in prefabricated building construction, such as component installation workers and grouting workers; provide detailed explanations of hazard points and key safety operation guidelines. Promotion of intelligent training tools—enhance the interactivity and effectiveness of training; studies have shown that using VR technology for training is more effective than traditional methods [87], and by using VR technology to simulate high-risk scenarios in prefabricated building construction (such as working at heights and component hoisting), workers can experience hazardous situations in a virtual environment, enhancing their risk perception; AR technology can provide real-time operational guidance (such as component installation positioning and grouting volume control), reducing operational errors; additionally, a phased skill certification system and mandatory certifications system should be implemented, integrating digital assessment and certification technologies to strictly enforce professional entry standards.

Institutional development not only regulates workers’ safety behaviors but also enhances enterprises’ safety management capabilities. It is an important means of reducing unsafe behaviors [61]. Specific strategies are as follows: Establish a safety performance assessment system, set up a safety behavior reward fund to incentivize safe behaviors, and guide the standardization of safety practices. Develop emergency plans and response procedures for high-risk aspects of prefabricated building construction (such as working at heights and component hoisting), conduct regular emergency drills, and enhance emergency response capabilities. Introduce smart equipment and real-time monitoring technology to oversee key construction site areas (such as hoisting zones and grouting work zones), enabling timely detection and early warning of unsafe behaviors. Establish a positive leadership behavior transmission system. By leveraging the demonstration effect of leadership, overall safety awareness can be effectively enhanced, fostering a culture where “everyone prioritizes safety and actively participates in safety practices”.

However, as shown in Figure 7, although interventions have significantly reduced the level of unsafe behaviors, it remains difficult to approach zero. Therefore, future research should consider the specific conditions of prefabricated construction projects and the characteristics of construction workers to develop targeted, combined intervention strategies and implement long-term interventions.

First, prefabricated construction involves multiple types of trades, each with distinct tasks and associated risks. As a result, the requirements for skills, knowledge, and safety behaviors vary accordingly. For instance, tower crane and hoisting machine operators face significant risks due to the nature of working at heights and the complexity of mechanical operations [10], requiring workers to possess a specialized knowledge system and strong risk assessment capabilities. Therefore, skill training and enhanced risk awareness in individual interventions may be more effective. Component assembly operations exhibit significant characteristics of group collaboration [11], making the creation of a safety-oriented atmosphere and the establishment of cooperation mechanisms in group interventions more targeted. Consequently, future research is recommended to develop differentiated intervention models and strategies for unsafe behaviors based on the specific characteristics of different trades.

Secondly, this study focuses only on interventions within individual subsystems and has not fully considered the combination and synergy between them. The occurrence of unsafe behavior is the result of multiple factors acting together. Individual factors, safety management, team factors, and the physical environment interact and work together to influence unsafe behavior. Future research could further explore combined intervention strategies for these subsystems. For example, by integrating individual factors with the safety management subsystem, a systematic intervention plan can be developed through multiple strategies, such as enhancing personal skills, fostering a positive safety attitude, strengthening institutional regulations, and improving supervision. This multidimensional and coordinated approach can enhance the effectiveness of interventions.

7. Conclusions

This study analyzes the influencing factors of unsafe behaviors among workers in prefabricated building construction, considering the characteristics of such projects. By combining the entropy weight method with the coefficient of variation method, a comprehensive weighting approach is applied. Based on the principles of system dynamics, an SD intervention model for unsafe behaviors of prefabricated building workers is established and simulated. The study presents the following findings:

(1)

An intervention simulation model for unsafe behaviors of prefabricated building construction workers was developed from four aspects: physical environment, individual factors, team factors, and safety management. The simulation results indicate that implementing a single intervention measure can reduce the level of unsafe behaviors among prefabricated building construction workers. Among them, enhancing individual comprehensive skills had the most significant effect, with a reduction of 37.78%. Although the decrease is considerable, it still falls short of the expected goal of significantly reducing unsafe behaviors. This indicates that single intervention measures have a limited impact on reducing the level of unsafe behaviors among prefabricated building workers.

(2)

Implementing combined interventions can significantly enhance the control effect on unsafe behaviors. The intervention effects of applying combined interventions to the four subsystems for prefabricated building construction workers, ranked in descending order, are as follows: individual factor intervention (68.87% reduction), safety management intervention (59.93% reduction), team factor intervention (46.32% reduction), and physical environment intervention (20.71% reduction). This demonstrates that combined interventions have a significant advantage in terms of intervention effectiveness.

(3)

In the prefabricated building construction process, priority should be given to strengthening individual factors and safety management interventions. This mainly includes enhancing training interventions and institutional development. In terms of training interventions, safety skill levels of construction workers can be improved through customized specialized training systems, the promotion of intelligent training tools (such as VR and AR), and the implementation of phased skill certification and mandatory licensing systems. In terms of institutional development, a comprehensive safety management model involving all personnel can be established through behavioral guidance mechanisms, emergency response mechanisms, supervision and management mechanisms, and a leadership-driven positive reinforcement system to enhance safety awareness and execution.

The simulation results of this study provide valuable insights into the intervention research on unsafe behaviors of prefabricated building construction workers. The proposed intervention strategies have significant practical implications for managing and controlling such behaviors. However, due to limitations in time and space, this study has not conducted an empirical investigation into the impact of these intervention strategies. The intervention of unsafe behaviors among prefabricated building construction workers is a long-term process. Due to the complexity and diversity of influencing factors, future research is recommended to implement unsafe behavior intervention strategies in prefabricated construction enterprises and conduct long-term intervention monitoring. To validate the effectiveness of the strategies, it is necessary to establish a data tracking system and a quantitative evaluation framework to monitor the trends of unsafe behaviors in real time and scientifically assess the intervention outcomes. Additionally, tailored intervention models and strategies for unsafe behaviors should be developed based on the specific job characteristics and risk factors of different trades (such as lifting workers, welders, and installation workers) to enhance the precision and applicability of intervention measures. In addition, further exploration of combined intervention strategies and their effects across different subsystems can be conducted. For example, integrating individual factors with the physical environment subsystem or combining safety management with team factors. By considering the specific characteristics of construction projects and workforce needs, customized safety intervention plans can be developed. Through multidimensional strategy integration, a more comprehensive and effective safety intervention can be achieved, providing scientific evidence and practical guidance for safety management in prefabricated building construction.