Combining association rules mining with complex networks to monitor coupled risks

Zhou, Ying; Li, Chenshuang; Ding, Lieyun; Sekuła, Przemysław; Love, Peter E.D.; Zhou, Cheng

doi:10.1016/j.ress.2019.02.013

Cited by 62 publications

(23 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the different nature and value range of data, it is difficult to classify the risk level, so the data is firstly normalized. That is, the value after processing is reserved between [0, 1] [ 50 ]. Firstly, the discrete variables of two-state and three-state were transformed into continuous variables by means of expert rating.…”

Section: Safety Risk Coupling Analysis Methods Of Prefabricated Building Hoistingmentioning

confidence: 99%

“…Under the selection of different thresholds of minimum confidence and minimum support, the number of association rules is shown in Figure 7 . Extreme cases such as “0.1–0.1” and “1–1” will cause significant errors when selecting parameters, and it should be selected from a gentle stage [ 50 ]. In this case, the parameter can be selected as

.…”

Section: Safety Risk Coupling Analysis Methods Of Prefabricated Building Hoistingmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Liu

Meng

Xing

et al. 2021

Sensors

View full text Add to dashboard Cite

Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider the interaction of risk factors. In this paper, a hoisting safety risk management framework based on digital twin (DT) is presented. The digital twin hoisting safety risk coupling model is built. The proposed model integrates the Internet of Things (IoT), Building Information Modeling (BIM), and a security risk analysis method combining the Apriori algorithm and complex network. The real-time perception and virtual–real interaction of multi-source information in the hoisting process are realized, the association rules and coupling relationship among hoisting safety risk factors are mined, and the time-varying data information is visualized. Demonstration in the construction of a large-scale prefabricated building shows that with the proposed framework, it is possible to complete the information fusion between the hoisting site and the virtual model and realize the visual management. The correlative relationship among hoisting construction safety risk factors is analyzed, and the key control factors are found. Moreover, the efficiency of information integration and sharing is improved, the gap of coupling analysis of security risk factors is filled, and effective security management and decision-making are achieved with the proposed approach.

show abstract

Section: Safety Risk Coupling Analysis Methods Of Prefabricated Building Hoistingmentioning

confidence: 99%

.…”

Section: Safety Risk Coupling Analysis Methods Of Prefabricated Building Hoistingmentioning

confidence: 99%

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Liu

Meng

Xing

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…(3) Reliability modeling In the reliability modeling step, the traditional PRA method including the Event Tree (ET) [29,30] and the Fault Tree (FT) [31,32] methods are improved by incorporating complex network theory [33][34][35][36] to describe the risk propagation features. Based on the risk definition, identification and features analysis, the configuration reliability model can be built using the PRPA method.…”

Section: Framework Of Space Station Configuration Reliability Assessmentmentioning

confidence: 99%

Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks

2021

Applied Sciences

View full text Add to dashboard Cite

A space station is a typical phased-mission system, and assessing its reliability during its configuration is an important engineering action. Traditional methods usually require extensive data to carry out a layered reliability assessment from components to the system. These methods suffer from lack of sufficient test data, and the assessment process becomes very difficult, especially in the early stage of the configuration. This paper proposes a reliability assessment method for the space station configuration mission, using multi-layer and multi-type risks. Firstly, the risk layer and the risk type for the space station configuration are defined and identified. Then, the key configuration risks are identified comprehensively, considering their occurrence likelihood and consequence severity. High load risks are identified through risk propagation feature analysis. Finally, the configuration reliability model is built and the state probabilities are computed, based on the probabilistic risk propagation assessment (PRPA) method using the assessment probability data. Two issues are addressed in this paper: (1) how to build the configuration reliability model with three layers and four types of risks in the early stage of the configuration; (2) how to quantitatively assess the configuration mission reliability using data from the existing operational database and data describing the propagation features. The proposed method could be a useful tool for the complex aerospace system reliability assessment in the early stage.

show abstract

“…B is the consequent. ARM has three important indicators (Zhou et al, 2019), including support, confidence and lift. More specifically, support is the percentage of the entire dataset covered by the rule, confidence measures the reliability of the inference of a generated rule, and lift is a measure of the interdependence of the antecedent and consequent in the rule.…”

Section: Highway Construction Accidentsmentioning

confidence: 99%

“…Many scholars have introduced quantitative methods into the accident analysis process, and ARM is one of these methods. The development of safety monitoring systems facilitates access to data in construction process (Zhou et al, 2019). The improvement of the accident reporting system also provides a source of accident information (Shao et al, 2019).…”

Section: Highway Construction Accidentsmentioning

confidence: 99%

Modified accident causation model for highway construction accidents (ACM-HC)

Tong

Zhao

Zhang

et al. 2020

ECAM

View full text Add to dashboard Cite

PurposeThe purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.Design/methodology/approachBased on the 2–4 Model, a framework of the ACM-HC was constructed, and the accident causal factors (CF) were extracted from four aspects of human, material, environment and management. In addition, association rule mining (ARM) was introduced to analyze accident investigation reports to obtain the interrelationships between the factors. Based on the framework, factors and ARM results, the ACM-HC was established. Finally, the ACM-HC was verified with a tunnel collapse accident.FindingsBoth the external and internal causes of contractor cause accidents. The flaws of safety management of other stakeholders are external causes. In terms of the internal causes, there are four stages: direct causes, indirect causes, radical causes and root causes. More specifically, the direct causes refer to the unsafe acts and the unsafe conditions; ineffective safety supervision and poor individual factors of frontline workers constitute the indirect causes; the radical causes lie in the flaws of construction procedures and technical schemes; the root causes are related to the poor individual factors of decision makers and managers.Originality/valueThe ACM-HC expresses the causes, sequence and mechanism of highway construction accidents in a visual way. In addition, this study describes a process of using a qualitative–quantitative hybrid approach to establish a modified ACM, which provides a different perspective for the establishment of an ACM.

show abstract

Combining association rules mining with complex networks to monitor coupled risks

Cited by 62 publications

References 46 publications

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks

Modified accident causation model for highway construction accidents (ACM-HC)

Contact Info

Product

Resources

About