Fuzzy risk assessment of a deeply buried tunnel under incomplete information

Risk assessment has always been an important part of safety risk research in tunnel and underground engineering. Owing to the characteristics of tunnel construction, to achieve an expected risk control effect, it is necessary to carry out accurate risk assessment research according to the risk assessment concept based on the entire tunnel construction process. At present, because of the frequent occurrences of safety accidents, a variety of risk assessment models have been proposed for different tunnel projects such as subways and railway tunnels, which can be roughly classified into two types: probability-based and fuzzy set theories. However, the existing models may be more suitable for the construction stage, and the design stage lacks a reliable and practical fuzzy risk assessment method. Therefore, based on fuzzy set theory and similarity measure theory, a risk assessment model is proposed to adapt to the characteristics that the risk information is difficult to quantify the fuzziness in the design phase. Firstly, new ideas of fuzzy risk analysis are proposed to overcome deficiencies in existing methods; secondly, a new similarity measure is constructed; then fusing multi-source fuzzy information based on evidence theory, the relationship between similarity measure and mass function is established. Finally, the new method is applied to the Yuelongmen tunnel. Results show that the concept of risk control and the risk assessment model are feasible.

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A Research on the Fuzzy Risk Assessment Model of Storm Surge Disaster Based on Artificial Neural Network

2010 2nd International Workshop on Database Technology and Applications ◽

10.1109/dbta.2010.5658968 ◽

2010 ◽

Author(s):

Fang Ji ◽

Yijun Hou

Keyword(s):

Neural Network ◽

Risk Assessment ◽

Artificial Neural Network ◽

Storm Surge ◽

Assessment Model ◽

Risk Assessment Model ◽

Artificial Neural ◽

Storm Surge Disaster ◽

Fuzzy Risk Assessment

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A fuzzy risk assessment model for hospital information system implementation

Expert Systems with Applications ◽

10.1016/j.eswa.2011.07.129 ◽

2012 ◽

Vol 39 (1) ◽

pp. 1211-1218 ◽

Cited By ~ 53

Author(s):

Gulcin Yucel ◽

Selcuk Cebi ◽

Bo Hoege ◽

Ahmet F. Ozok

Keyword(s):

Risk Assessment ◽

Information System ◽

Hospital Information System ◽

Assessment Model ◽

Risk Assessment Model ◽

System Implementation ◽

Fuzzy Risk Assessment

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Developing a fuzzy risk assessment model for guaranteed maximum price and target cost contracts in South Australia

Facilities ◽

10.1108/f-08-2012-0063 ◽

2014 ◽

Vol 32 (11/12) ◽

pp. 624-646 ◽

Cited By ~ 6

Author(s):

Daniel W.M. Chan ◽

Joseph H.L. Chan ◽

Tony Ma

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

Construction Projects ◽

South Australia ◽

Assessment Model ◽

Risk Assessment Model ◽

Content Type ◽

Risk Levels ◽

Target Cost ◽

Fuzzy Risk Assessment

Purpose – This paper aims to develop a fuzzy risk assessment model for construction projects procured with target cost contracts and guaranteed maximum price contracts (TCC/GMP) using the fuzzy synthetic evaluation method, based on an empirical questionnaire survey with relevant industrial practitioners in South Australia. Design/methodology/approach – A total of 34 major risk factors inherent with TCC/GMP contracts were identified through an extensive literature review and a series of structured interviews. A questionnaire survey was then launched to solicit the opinions of industrial practitioners on risk assessment of such risk factors. Findings – The most important 14 key risk factors after the computation of normalised values were selected for undertaking fuzzy evaluation analysis. Five key risk groups (KRGs) were then generated in descending order of importance as: physical risks, lack of experience of contracting parties throughout TCC/GMP procurement process, design risks, contractual risks and delayed payment on contracts. These survey findings also revealed that physical risks may be the major hurdle to the success of TCC/GMP projects in South Australia. Practical implications – Although the fuzzy risk assessment model was developed for those new-build construction projects procured by TCC/GMP contracts in this paper, the same research methodology may be applied to other contracts within the wide spectrum of facilities management or building maintenance services under the target cost-based model. Therefore, the contribution from this paper could be extended to the discipline of facilities management as well. Originality/value – An overall risk index associated with TCC/GMP construction projects and the risk indices of individual KRGs can be generated from the model for reference. An objective and a holistic assessment can be achieved. The model has provided a solid platform to measure, evaluate and reduce the risk levels of TCC/GMP projects based on objective evidence instead of subjective judgements. The research methodology could be replicated in other countries or regions to produce similar models for international comparisons, and the assessment of risk levels for different types of TCC/GMP projects (including new-build or maintenance) worldwide.

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