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Abstract

The construction industry has long been seen as a high-risk industry, and the risk evaluation method is the core of safety risk management. Complex construction environments can lead to risk evolution over time, leading to uncertainty in risk assessment. Therefore, it is necessary to establish a risk evaluation method for multi-period group decision, which can also deal with uncertain information reliably. This study defines the risk evaluation indicators for construction safety and adopts the cloud model to deal with the uncertain information of experts’ evaluations. A cloud-based aggregation algorithm is also employed for group decision. Then, a cloud-based Minkowski distance function is proposed to enhance the ability of TOPSIS to deal with the uncertain information. Finally, an optimization algorithm is used to calculate the multi-period comprehensive evaluation value to define the risk priority. A real case is used for demonstration and the results show that the proposed method can effectively deal with the risk evaluation problem of multi-project, multi-period and group decision with uncertain information.

Keywords

Construction safety risk cloud model TOPSIS uncertain information

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References

C.P.

Barros and

Wanke , An analysis of African airlines efficiency with two-stage TOPSIS and neural networks, Journal of Air Transport Management 44(45) (2015), 90–102.

H-b

Chou

Life-cycle risk management of urban large-scale public building, China Construction Industry Press, Beijing, China. (127) (2017).

Colak ,

Kaya and

Erdogan , A Fuzzy Based Risk Evaluation Model for Industry 4.0 Transition Process, Transition Process. In: Calisir

, Cevikcan

, Camgoz Akdag

(eds) Industrial Engineering in the Big Data Era. (2019) 201–215.

F.J.

Forteza ,

Sesé and

J.M.

Carretero-Gómez , CON-SRAT. Construction sites risk assessment tool, Safety Science 89 (2016), 338–354.

G.K.

Koulinas ,

P.K.

Marhavilas ,

O.E.

Demesouka ,

A.P.

Vavatsikos and

D.E.

Koulouriotis , Risk analysis and assessment in the worksites using the fuzzy-analytical hierarchy process and a quantitative technique - A case study for the Greek construction sector, Safety Science 112 (2019), 96–104.

C-L

Gao ,

S-C

Li ,

Wang ,

L-P

Li and

Lin , The Risk Assessment of Tunnels Based on Grey Correlation and Entropy Weight Method, Geotechnical and Geological Engineering 36(3) (2017), 1621–1631.

Ginting , Fadlina, Mesran,

A.P.U.

Siahaan and

Rahim , Technical Approach of TOPSIS in Decision Making, International Journal of Recent Trends in Engineering & Research 3 (2017), 58–64.

Gul and

M.F.

Ak , A comparative outline for quantifying risk ratings in occupational health and safety risk assessment, Journal of Cleaner Production 196 (2018), 653–664.

Gunduz and

Ahsan , Construction safety factors assessment through Frequency Adjusted Importance Index, International Journal of Industrial Ergonomics 64 (2018), 155–162.

10.

Gunduz ,

Talat Birgonul and

Özdemir , Development of a safety performance index assessment tool by using a fuzzy structural equation model for construction sites, Automation in Construction 85 (2018), 124–134.

11.

A.F.

Guneri ,

Gul and

Ozgurler , A fuzzy AHP methodology for selection of risk assessment methods in occupational safety, Int. J. Risk Assessment and Management 18 (2015).

12.

Hinze ,

Thurman and

Wehle , Leading indicators of construction safety performance, Safety Science 51(1) (2013), 23–28.

13.

Huang ,

Sun and

Zhang , Safety Evaluation of Construction Based on the Improved AHP-Grey Model, Wireless Personal Communications 103(1) (2018), 209–219.

14.

C.-L.

Hwang and

Yoon , Methods for Multiple Attribute Decision Making, In: Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems 186 (1981).

15.

S.-S.

Leu and

C.-M.

Chang , Bayesian-network-based safety risk assessment for steel construction projects, Accident Analysis and Prevention 54 (2013), 122–133.

16.

C.-B.

Li ,

Z.-Q.

Qi and

Feng , A multi-risks group evaluation method for the informatization project under linguistic environment, Journal of Intelligent & Fuzzy Systems 26(3) (2014), 1581–1592.

17.

Y.-H.

Lin ,

P.-C.

Lee and

H.-I.

Ting , Dynamic multi-attribute decision making model with grey number evaluations, Expert Systems with Applications 35(4) (2008), 1638–1644.

18.

Lingard ,

Hallowell ,

Salas and

Pirzadeh , Leading or lagging? Temporal analysis of safety indicators on a large infrastructure construction project, Safety Science 91 (2017), 206–220.

19.

G.F.

Liu ,

Zhou and

M.Z.

Xu , Interval Grey Fuzzy Uncertain Linguistic Sets and Their Application to Ranking of Construction Project Risk Factors, Journal of Highway and Transportation Research and Development 12(2) (2018).

20.

H-C

Liu ,

L-E

Wang ,

Li and

Y-P

Hu , Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method, IEEE Transactions on Fuzzy Systems 27(1) (2019), 84–95.

21.

Liu ,

Gong ,

Wang and

Teng , Cloud Model-Based Safety Performance Evaluation of Prefabricated Building Project in China, Wireless Personal Communications 102(3021-3039) (2018).

22.

Mete , Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment, Human and Ecological Risk Assessment: An International Journal (2018).

23.

M.A.

Mir ,

P.T.

Ghazvinei ,

N.M.N.

Sulaiman ,

N.E.A.

Basri ,

Saheri ,

N.Z.

Mahmood ,

Jahan ,

R.A.

Begum and

Aghamohammadi , Application of TOPSIS and VIKOR improved versions in a multi criteria decision analysis to develop an optimized municipal solid waste management model, Journal of Environmental Management 166(15) (2016), 109–115.

24.

Nasirzadeh and

M.K.M.

Mir , A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: An application to model construction workers’ safety behavior, International Journal of Construction Management 18(2) (2018), 132–143.

25.

L.D.

Nguyen ,

D.Q.

Tran and

M.P.

Chandrawinata , Predicting Safety Risk of Working at Heights Using Bayesian Networks, Journal of Construction Engineering and Management 142(9) (2016).

26.

N.E.

Oz ,

Mete ,

Serin and

Gul , Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards, Human and Ecological Risk Assessment: An International Journal (2018), 1–18.

27.

Peng ,

Zhou and

Peng , Cloud model based approach to group decision making with uncertain pure linguistic information, Journal of Intelligent & Fuzzy Systems 32(3)(2017), 1959–1968.

28.

H-G

Peng ,

H-Y

Zhang and

J-Q

Wang , Cloud decision support model for selecting hotels on TripAdvisor.com with probabilistic linguistic information, International Journal of Hospitality Management 68 (2018), 124–138.

29.

Peng and

Dai , Approaches to single-valued neu-trosophic MADM based on MABAC, TOPSIS and new similarity measure with score function, Neural Computing and Applications 29(10) (2016), 939–954.

30.

Peng and

Garg , Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure, Computers & Industrial Engineering 119 (2018), 439–452.

31.

Peng and

Liu , Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set, Journal of Intelligent & Fuzzy Systems 32(1) (2017), 955–968.

32.

Peng and

Selvachandran , Pythagorean fuzzy set: State of the art and future directions, Artificial Intelligence Review (2017), 1–55.

33.

Peng and

Yang , Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision making based on regret theory and prospect theory with combined weight, Applied Soft Computing 54 (2017), 415–430.

34.

Pinto , QRAM a Qualitative Occupational Safety Risk Assessment Model for the construction industry that incorporate uncertainties by the use of fuzzy sets, Safety Science 63 (2014), 57–76.

35.

Qiu ,

Shi ,

Teng and

Zhou , Assessment of Water Inrush Risk Using the Fuzzy Delphi Analytic Hierarchy Process and Grey Relational Analysis in the Liangzhuang Coal Mine, China, Mine Water and the Environment 36(1) (2016), 39–50.

36.

Roháčová and

Marková , The Analysis of AHP method and its potential use in logistics, Acta Montanistica Slovaca 14(1) (2009), 103–112.

37.

Taylan ,

A.O.

Bafail ,

R.M.S.

Abdulaal and

M.R.

Kabli , Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies, Applied Soft Computing 17 (2014), 105–116.

38.

Tepe and

Kaya , A fuzzy-based risk assessment model for evaluations of hazards with a real-case study, Human and Ecological Risk Assessment: An International Journal (2019), 1–26.

39.

Ting , A modified TOPSIS with a different ranking index, European Journal of Operational Research 260(1) (2017), 152–160.

40.

Wang ,

Zeng ,

V.P.

Singh ,

Xu ,

Liu ,

Wang ,

Zeng ,

Wu and

Wang , A multidimension cloud modelbased approach for water quality assessment, Environ Res 149 (2016), 113–121.

41.

H-L

Wang and

Y-Q

Feng , On multiple attribute group decision making with linguistic assessment information based on cloud model, Control and Decision (2005).

42.

J-Q

Wang ,

J-J

Peng ,

H-Y

Zhang ,

Liu and

X-H

Chen , An Uncertain Linguistic Multi-criteria Group Decision-Making Method Based on a Cloud Model, Group Decision and Negotiation 24(1) (2014), 171–192.

43.

Wu ,

Liu ,

Zhang ,

M.J.

Skibniewski and

Wang , Prospective safety performance evaluation on construction sites, Accid Anal Prev 78 (2015), 58–72.

44.

Y.Wu,K.

Chen ,

Zeng ,

Yang and

Geng , Cloud-based decision framework for waste-to-energy plant site selec-tion - A case study from China, Waste Manag 48 (2016), 593–603.

45.

Yang ,

Yan and

Zeng , How to handle uncertainties in AHP: The Cloud Delphi hierarchical analysis, Information Sciences 222 (2013), 384–404.

46.

Yurdakul and

Y.T.

İç , Development of a multi-level performance measurement model for manufacturing com-panies using a modified version of the fuzzy TOPSIS approach, Soft Computing 22(22) (2018), 7491–7503.

47.

Zhang ,

Ding ,

Wu and

M.J.

Skibniewski , An improved Dempster-Shafer approach to construction safety risk perception, Knowledge-Based Systems 132 (2017), 30–46.

48.

Zhang ,

M.J.

Skibniewski ,

Wu ,

Chen and

Deng , A probabilistic approach for safety risk analysis in metro construction, Safety Science 63 (2014), 8–17.

49.

Zhang ,

Wu ,

M.J.

Skibniewski ,

Zhong and

Lu , Bayesian-network-based safety risk analysis in construc-tion projects, Reliability Engineering and System Safety 131 (2014), 29–39.

50.

Zhou ,

Y.M.

Goh and

Li , Overview and analysis of safety management studies in the constructionindustry, Safety Science 72 (2015), 337–350.

51.

Zolfaghari and

S.M.

Mousavi , Construction-project risk assessment by a new decision model based on De-Novo multi-approaches analysis and hesitant fuzzy sets under uncertainty, Journal of Intelligent & Fuzzy Systems 35(1) (2018), 639–649.

A multi-period comprehensive evaluation method of construction safety risk based on cloud model

Abstract

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