A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors
Abstract
:1. Introduction
2. Risk Factor Analysis and Theoretical Basis of Workers’ Unsafe Behavior
2.1. Risk Factors of Workers’ Unsafe Behavior and Their Uncertainty Analysis
2.2. Cascading Failure and Entropy Flow Model
3. The Effective Path Identification Method for the Risk of Unsafe Worker Behaviors
3.1. Construction of a Risk Network Model for Workers’ Unsafe Behavior
3.1.1. Risk Factor Identification
3.1.2. Risk Network Modeling
3.2. Construction of a Cascading Trigger Model for Workers’ Unsafe Behavior
3.2.1. The Calculation Method for Risk Occurrence Probability and Impact Range
- (1)
- Expert scoring method based on confidence index
- (2)
- The collection and data processing of expert scoring results
3.2.2. Calculation of Conditional Probability of Risk Factors Based on Association Strength
3.2.3. Constructing a Cascading Trigger Model for Risk Factors of Unsafe Worker Behaviors
- (1)
- Upper Limit of Load Capacity
- (2)
- Trigger conditions for initial nodes
- (3)
- The rule for redistributing loads of failed nodes
- (4)
- Trigger conditions for non-initial nodes
- (5)
- Effective Pathways
4. Simulation Operation and Result Analysis of Risk of Unsafe Worker Behaviors
4.1. Simulation Strategy for Risk of Unsafe Worker Behaviors
- (1)
- Attack on a single deep factor (assuming only one factor is triggered)
- Attack every single deep factor separately, totaling 18 situations (The reason is that this paper identified 22 risk factors, 18 of which are deep factors and 4 are surface factors. Risks are transmitted from deep factors to surface factors, leading to unsafe behaviors. Surface factors do not appear without cause; they are induced by deep factors. Therefore, only the scenarios in which the 18 deep factors occur independently are simulated);
- Output the effective path after attacking each node (In the risk network, we assume that certain risk nodes have already occurred, and this assumed state is referred to as attacking the nodes).
- (2)
- Attack on randomly combined and simultaneously occurring deep factors
- Use the simulation results obtained in 4.1.(1) as the control group;
- Perform permutations and combinations of all deep factors, resulting in a total of 4029 combinations (153 combinations of two factors, 816 combinations of three factors, and 3060 combinations of four factors):
- Attack these combination nodes separately and output the effective paths.
- Compare the simulation results of the combination nodes with the control group results.
4.2. Analysis of Simulation Results
- (1)
- Network failure rate
- (2)
- Changes in effective action paths
- (3)
- Node distribution
4.3. Analysis of the Causes Behind the Simulation Results
- (1)
- As the number of nodes in the combinations increases, the failure rate of the risk network also increases. This phenomenon occurs because more node combinations introduce more potential paths for risk propagation. Each additional node combination increases the likelihood of risk spreading. Consequently, when multiple risk nodes coexist, the probability of triggering certain nodes in the network increases, leading to an increased failure rate.
- (2)
- The number of effective paths increases with the number of node combinations. This is because combining more nodes amplifies the cumulative risk effect. A single node’s risk may not be sufficient to surpass the threshold of a critical node. However, when multiple nodes are combined, their cumulative risk may exceed the threshold, triggering the critical node and activating more potential paths, thereby increasing the number of effective paths.
- (3)
- Regarding the statistical results on node distribution in Section 4.2 (3), the reasons behind these findings are further discussed. Deep factors serve as the root causes, influencing surface factors through complex transmission paths. Understanding and identifying these deep factors are crucial for preventing and reducing unsafe behaviors triggered by surface factors. By deeply studying the relationship between deep and surface factors, more effective strategies can be developed to reduce the occurrence of unsafe behaviors among workers. Given that surface factors are directly triggered by deep factors, this study focuses on the causes of the prominent deep factors identified in the results.
- Individual factors are the most prevalent, including physical fatigue, negative emotions, conformity, and risk-taking mentality. In Chinese construction workers, physical fatigue and negative emotions are common. The Chinese construction industry is known for its high efficiency, with frequent overtime work. Long hours of intense labor and insufficient rest, coupled with the pressure of the work environment, often lead to fatigue and negative emotions among workers. Additionally, conformity and a risk-taking mentality are also common. Due to generally low levels of education and weak safety awareness, workers are easily influenced by the unsafe behaviors of their peers, imitating non-compliance with safety regulations. Furthermore, a risk-taking mentality causes workers to underestimate risks, become overconfident, and believe that accidents will not happen to them, further increasing the risk of unsafe behavior.
- At the organizational level, poor group cohesion and ineffective internal communication are significant factors. These issues are prevalent in large construction projects, where the workforce is diverse and highly mobile, often leading to inadequate teamwork and coordination. This directly results in poor internal communication and a lack of cohesion within the group, making workers more reliant on individual judgment rather than collective rules, thereby increasing the likelihood of unsafe behaviors.
- At the management level, abusive leadership style and insufficient/low-quality safety education and training are key factors. In the Chinese construction industry, an authoritarian management style is relatively common, especially in more traditional enterprises. This style can lead to low employee morale, frustration, and even resistance, neglecting the psychological well-being and work motivation of employees, thereby increasing safety risks. Additionally, in many small and medium-sized enterprises and temporary construction sites, safety training often becomes a formality or is difficult to implement effectively due to limited resources. Many workers come from rural areas with low educational levels, making it difficult for them to understand complex safety regulations and technical requirements, resulting in the poor application of training content in actual work. Thus, although safety training has become a necessary measure in the Chinese construction industry, its effectiveness is greatly reduced due to poor implementation and insufficient resources, making it difficult to effectively prevent unsafe behaviors.
5. Conclusions
- Through the literature research method and expert scoring method, this paper identifies the risk factors of workers’ unsafe behavior from the four dimensions of individual, organization, management, and environment and solves the problem of multidimensional identification of workers’ unsafe behavior risk factors.
- In this paper, the relationship between risk factors is evaluated by the expert scoring method, the risk network of workers’ unsafe behavior is built, the cascade failure phenomenon is introduced, the entropy flow model is adopted to accurately quantify the risk, the risk quantification formula is formulated to accurately measure the risk propagation, and the trigger conditions of each risk node are set up, and the cascade trigger model is built. The problem of risk transmission and transferability is solved.
- The study also utilizes Visual Studio Code for coding and combines it with JavaScript to build a simulation platform that models the uncertainty of risk occurrences. This involves simulating the evolution paths of various risk nodes in random combinations when triggered, thereby closely replicating the uncertainty of risk occurrences.
- According to the simulation results, as the number of nodes in the combination category increases, the network failure rate also increases. By statistically analyzing the 1083 identified effective paths, this paper determines the key paths and nodes of unsafe worker behaviors. The deep factors include individual dimensions (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimensions (poor cohesion, poor internal communication), and managerial dimensions (abusive leadership style, insufficient/low-quality safety education and training). Surface factors mainly exist in organizational dimensions (poor safety climate) and individual dimensions (unsafe motivation, weak risk perception ability, weak safety awareness). These findings can help managers effectively control key risk factors and block risk propagation paths.
- The theoretical significance of this paper lies in the introduction of a risk identification method based on cascading failure and entropy flow models, offering a new perspective for analyzing the evolution of unsafe behaviors. This theoretical framework not only helps deepen the understanding of the root causes of unsafe behaviors but also provides a new foundation for future research, particularly in multidimensional and multilayered risk management studies, addressing some gaps in the field of construction site safety management. The practical significance of this paper is that its findings provide practical guidance for safety management in the construction industry. By identifying and analyzing the key risk factors and pathways of unsafe behaviors among construction workers, this research can help managers more effectively prevent and reduce the occurrence of unsafe behaviors in actual construction processes.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dimension | Risk Factor |
---|---|
Individual Dimension | Physical Fatigue R1 |
Negative Emotions R2 | |
Paranoia and Hostility R3 | |
Fluke Mindset R4 | |
Herd Mentality R5 | |
Young Age R6 | |
Low Educational Level R7 | |
Short Work Experience R8 | |
Low-Risk Perception Ability of Workers R9 | |
Weak Safety Awareness of Workers R10 | |
Unsafe Motivation of Workers R11 | |
Organizational Dimension | Poor Safety Climate of the Organization R12 |
Poor Organizational Support for Workers R13 | |
Poor Organizational Cohesion R14 | |
Poor Internal Communication and Interaction within the Organization R15 | |
Management Dimension | Abusive Leadership Style R16 |
Laissez-Faire Leadership Style R17 | |
Inconsistent Reward and Punishment Mechanisms R18 | |
Insufficient/Low-Quality Safety Education and Training R19 | |
Environmental Dimension | High Job Demands R20 |
Complex Work Environment R21 | |
Work–Family Conflict R22 |
Number | Expert | Working Years | Educational Background | The Title of a Professional Post |
---|---|---|---|---|
1 | Li ** | 21 | Master | Associate Professor |
2 | Tang ** | 23 | Master | Professor |
3 | Guo ** | 19 | Doctor | Professor |
4 | Xia ** | 21 | Master | Associate Professor |
5 | Chu ** | 6 | Bachelor | Safety Officer |
6 | Chen * | 6 | Bachelor | Safety Officer |
i→j | i→j | i→j | |||
---|---|---|---|---|---|
R1→R2 | 4.5 | R6→R4 | 3 | R17→R4 | 3.15 |
R1→R4 | 2.8 | R6→R10 | 2.6 | R17→R5 | 3.15 |
R1→R5 | 2.9 | R7→R4 | 2.7 | R17→R10 | 3.25 |
R1→R9 | 4 | R7→R9 | 2.55 | R17→R12 | 3.1 |
R1→R10 | 4 | R7→R10 | 3.35 | R17→R14 | 3.2 |
R1→R12 | 4.3 | R8→R9 | 3.6 | R18→R4 | 2.85 |
R1→R22 | 2.65 | R8→R10 | 3.2 | R18→R5 | 3.05 |
R2→R9 | 3.55 | R13→R14 | 2.75 | R18→R10 | 3.55 |
R2→R11 | 3.85 | R13→R15 | 3.5 | R18→R12 | 3 |
R2→R15 | 2.65 | R14→R11 | 2.55 | R19→R4 | 4 |
R3→R11 | 2.9 | R14→R15 | 2.85 | R19→R5 | 3 |
R3→R12 | 4.15 | R15→R11 | 3 | R19→R10 | 3.75 |
R3→R14 | 2.55 | R15→R12 | 2.85 | R19→R12 | 2.9 |
R3→R15 | 2.8 | R15→R14 | 3.6 | R20→R1 | 2.8 |
R4→R10 | 3.75 | R16→R1 | 3.75 | R20→R2 | 3.05 |
R4→R11 | 3.55 | R16→R2 | 4 | R21→R1 | 3.9 |
R5→R10 | 3.15 | R16→R3 | 3.05 | R21→R2 | 3.9 |
R5→R11 | 3 | R16→R11 | 2.85 | R22→R2 | 4.1 |
R5→R12 | 2.85 | R16→R14 | 2.75 | R22→R11 | 2.7 |
Risk Factor | Probability of Occurrence of Risk Factors | Range of Influence of Risk Factors | ||
---|---|---|---|---|
Range of Probability of Occurrence | Confidence Index | Scope of Influence | Confidence Index | |
R1 | Yiu, Kiu | hiu | Yiu, Kiu | hiu |
R2 | Yiu, Kiu | hiu | Yiu, Kiu | hiu |
…… | Yiu, Kiu | hiu | Yiu, Kiu | hiu |
Rn | Yiu, Kiu | hiu | Yiu, Kiu | hiu |
Grading Criteria (W) | Range of Probability of Occurrence of Risk Factors | Scope of Influence of Risk Factors on Workers’ Unsafe Behaviors |
---|---|---|
1 | 0% | 0% |
2 | 0%~0.1% | 0%~0.5% |
3 | 0.1%~0.5% | 0.5%~1% |
4 | 0.5%~1% | 1%~2% |
5 | 1%~5% | 2%~3% |
6 | 5%~10% | 3%~5% |
7 | 10%~20% | 5%~10% |
8 | 20%~30% | 10%~15% |
9 | 30%~40% | 15%~20% |
10 | 40%~50% | 20%~25% |
11 | 50%~60% | 25%~30% |
12 | 60%~70% | 30%~40% |
13 | 70%~80% | 40%~50% |
14 | 80%~90% | 50%~60% |
15 | 90%~100% | 60%~70% |
16 | 70%~80% | |
17 | 80%~90% | |
18 | 90%~100% |
Inter Vrange Δiu /% | Confidence Index hiu | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | |
0.1 | 0 | 0.025 | 0.05 | 0.075 | 0.1 | 0.15 | 0.2 | 0.25 | 0.3 | 0.35 |
0.4 | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.6 | 0.8 | 1 | 1.2 | 1.4 |
0.5 | 0 | 0.125 | 0.25 | 0.375 | 0.5 | 0.75 | 1 | 1.25 | 1.5 | 1.75 |
1 | 0 | 0.25 | 0.5 | 0.75 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 |
2 | 0 | 0.5 | 1 | 1.5 | 2 | 3 | 4 | 5 | 6 | 7 |
4 | 0 | 1 | 2 | 3 | 4 | 6 | 8 | 10 | 12 | 14 |
5 | 0 | 1.25 | 2.5 | 3.75 | 5 | 7.5 | 10 | 12.5 | 15 | 17.5 |
10 | 0 | 2.5 | 5 | 7.5 | 10 | 15 | 20 | 25 | 30 | 35 |
Number | Effective Action Pathways | Frequency of Occurrence | Ranking |
---|---|---|---|
A | x→R12 | 394 | 1 |
B | x→R14→R12 | 85 | 2 |
C | x→R5→R12 | 76 | 3 |
D | x→R11 | 58 | 4 |
E | x→R2→R9 | 57 | 5 |
F | x→R9 | 48 | 6 |
G | x→R15→R14→R11 | 38 | 7 |
H | x→R15→R14→R12 | 38 | 7 |
I | x→R1→R9 | 28 | 8 |
J | x→R1→R2→R15→R14→R11 | 28 | 8 |
K | x→R1→R2→R15→R14→R12 | 28 | 8 |
L | x→R14→R11 | 25 | 9 |
M | x→R2→R15→R11 | 21 | 10 |
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Luo, X.; Tang, Y.; Zhou, J.; Wang, M.; Tian, Y. A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors. Buildings 2024, 14, 2483. https://doi.org/10.3390/buildings14082483
Luo X, Tang Y, Zhou J, Wang M, Tian Y. A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors. Buildings. 2024; 14(8):2483. https://doi.org/10.3390/buildings14082483
Chicago/Turabian StyleLuo, Xin, Yanjuan Tang, Jun Zhou, Mingru Wang, and Yong Tian. 2024. "A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors" Buildings 14, no. 8: 2483. https://doi.org/10.3390/buildings14082483