Assessing the impact of construction industry stakeholders on workers' unsafe behaviours using extended decision making approach

Abstract The construction industry (CI) is one of the most hazardous where the specific behaviours of different stakeholders can be contrary to the conditions and safety behaviours in the construction process. Understanding the nature of this kind of uncertainty can reduce the level of inconsistency that reinforces the safety management system in CI. The purpose of this paper is to present an interval-valued intuitionistic fuzzy-improved score function and weighted divergence based approximation (IVIF-ISF-WDBA) approach for ranking main stakeholders with clear duties that can influence workers' unsafe behaviours as well as risk reduction in final decisions. In this method, a score matrix is compiled based on improved score performance and IVIF decision matrix, and then a linear programming model is developed to determine the weight of unknown criteria. In addition, a divergence measure has been developed to find out the degree of performance of the alternatives. Next, we perform comparative and sensitivity analyses with different proposed methods and different weight criteria sets to determine the stability of the developed approach. The results indicated that the most effective safety factors (ESFs) are safety climate factor3 (SCF3) (safety management rules and regulations) at 45%, and safety perceptual factor2 (SPF2) (workers' preferences and behaviours related to safety category) at 25%. The LP-model ranked client, designer, construction manager, and contractor as the authoritative bodies that should carry out extensive inspections of ESFs, namely, SCFs, SPFs, and workplace condition factors (WCFs) in the preconstruction phase. However, these priorities can change during the construction phase among the contractor, construction manager, supervisor, client, and designer. This study concluded that workers' unsafe behaviours could be linked to stakeholder duties in complex and dynamic conditions of the CI.

[1]  Patrick X.W. Zou,et al.  Fostering a Strong Construction Safety Culture , 2011 .

[2]  Xiaowei Luo,et al.  A proactive workers' safety risk evaluation framework based on position and posture data fusion , 2019, Automation in Construction.

[3]  Jianliang Zhou,et al.  Stakeholders' Effect on Construction Safety Management Based on System Dynamics Model , 2014 .

[4]  Sohag Kabir,et al.  A fuzzy Bayesian network approach for risk analysis in process industries , 2017 .

[5]  Lieyun Ding,et al.  Safety barrier warning system for underground construction sites using Internet-of-Things technologies , 2017 .

[6]  Dongping Fang,et al.  Impact of the Supervisor on Worker Safety Behavior in Construction Projects , 2015 .

[7]  Wenquan Li,et al.  Algorithms for hesitant fuzzy soft decision making based on revised aggregation operators, WDBA and CODAS , 2019, J. Intell. Fuzzy Syst..

[8]  R. Rao,et al.  Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection , 2012 .

[9]  Yuhua Zhu,et al.  Generalized score functions on interval-valued intuitionistic fuzzy sets with preference parameters for different types of decision makers and their application , 2018, Applied Intelligence.

[10]  Aminah Robinson Fayek,et al.  Risk Management in the Construction Industry Using Combined Fuzzy FMEA and Fuzzy AHP , 2010 .

[11]  Selcuk Cebi,et al.  A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system , 2018 .

[12]  Sungjoo Hwang,et al.  What drives construction workers' acceptance of wearable technologies in the workplace?: Indoor localization and wearable health devices for occupational safety and health , 2017 .

[13]  Edmundas Kazimieras Zavadskas,et al.  A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015 , 2017 .

[14]  Muhamad Zameri Mat Saman,et al.  A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments , 2017, Appl. Soft Comput..

[15]  Zhipeng Zhou,et al.  Overview and analysis of safety management studies in the construction industry , 2015 .

[16]  M. Talat Birgonul,et al.  Development of a safety performance index assessment tool by using a fuzzy structural equation model for construction sites , 2018 .

[17]  Bo Xia,et al.  Proactive behavior-based system for controlling safety risks in urban highway construction megaprojects , 2018, Automation in Construction.

[18]  Fanyong Meng,et al.  Interval-valued intuitionistic fuzzy multi-criteria group decision making based on cross entropy and 2-additive measures , 2015, Soft Comput..

[19]  Deng-Feng Li,et al.  TOPSIS-Based Nonlinear-Programming Methodology for Multiattribute Decision Making With Interval-Valued Intuitionistic Fuzzy Sets , 2010, IEEE Transactions on Fuzzy Systems.

[20]  T. Yiu,et al.  A new approach to predict safety outcomes in the construction industry , 2018, Safety Science.

[21]  Abdul Kadir Marsono,et al.  Identifying and assessing the critical factors for effective implementation of safety programs in construction projects , 2018, Safety Science.

[22]  Muhammad Rizal Razman,et al.  Occupational Safety and Health ( Osh ) Management In Construction Industry : A Review , 2017 .

[23]  Kam Shadan.  Construction Project Management Handbook: March 2012 , 2012 .

[24]  Evan A. Nadhim,et al.  Falls from Height in the Construction Industry: A Critical Review of the Scientific Literature , 2016, International journal of environmental research and public health.

[25]  Sou-Sen Leu,et al.  Characteristic analysis of occupational accidents at small construction enterprises , 2010 .

[26]  Edmundas Kazimieras Zavadskas,et al.  VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications , 2016 .

[27]  James S. Vaux,et al.  Relationship Conflict in Construction Management: Performance and Productivity Problem , 2018, Journal of Construction Engineering and Management.

[28]  Arunodaya Raj Mishra,et al.  Interval-Valued Intuitionistic Fuzzy WASPAS Method: Application in Reservoir Flood Control Management Policy , 2018, Group Decision and Negotiation.

[29]  Rebecca R. Langdon,et al.  Construction workers' well-being: What leads to depression, anxiety, and stress? , 2018 .

[30]  D. Fang,et al.  An agent-based modeling approach for understanding the effect of worker-management interactions on construction workers' safety-related behaviors , 2019, Automation in Construction.

[31]  Arshad Noor Siddiquee,et al.  Selection of E-learning websites using a novel Proximity Indexed Value (PIV) MCDM method , 2019, Journal of Computers in Education.

[32]  K. Atanassov,et al.  Interval-Valued Intuitionistic Fuzzy Sets , 2019, Studies in Fuzziness and Soft Computing.

[33]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .

[34]  Don Harris,et al.  Risk Perception and Risk-Taking Behavior of Construction Site Dumper Drivers , 2010, International journal of occupational safety and ergonomics : JOSE.

[35]  Jun Ye,et al.  Fuzzy cross entropy of interval-valued intuitionistic fuzzy sets and its optimal decision-making method based on the weights of alternatives , 2011, Expert Syst. Appl..

[36]  Dongping Fang,et al.  Understanding the Causation of Construction Workers’ Unsafe Behaviors Based on System Dynamics Modeling , 2015 .

[37]  Eddie W.L. Cheng,et al.  Exploring the perceived influence of safety management practices on project performance in the construction industry , 2012 .

[38]  Julie L. Brockman.  Interpersonal Conflict in Construction: Cost, Cause, and Consequence , 2014 .

[39]  Ioannis K. Vlachos,et al.  Intuitionistic fuzzy information - Applications to pattern recognition , 2007, Pattern Recognit. Lett..

[40]  Kenneth H. Rose.  A Guide to the Project Management Body of Knowledge (PMBOK® Guide)—Fifth Edition , 2013 .

[41]  Ibukun Awolusi.  Safety Activity Analysis Framework to Evaluate Safety Performance in Construction , 2017 .

[42]  Z. Xu,et al.  Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making , 2007 .

[43]  Nikhil R. Pal,et al.  Divergence Measures for Intuitionistic Fuzzy Sets , 2015, IEEE Transactions on Fuzzy Systems.

[44]  Brenda McCabe,et al.  Relationship between Individual Resilience, Interpersonal Conflicts at Work, and Safety Outcomes of Construction Workers , 2017 .

[45]  Van-Nam Huynh,et al.  A novel non-parametric method for time series classification based on k-Nearest Neighbors and Dynamic Time Warping Barycenter Averaging , 2019, Eng. Appl. Artif. Intell..

[46]  Khalil Md Nor,et al.  Development of TOPSIS Method to Solve Complicated Decision-Making Problems - An Overview on Developments from 2000 to 2015 , 2016, Int. J. Inf. Technol. Decis. Mak..

[47]  Rakesh Kumar,et al.  Modified fuzzy divergence measure and its applications to medical diagnosis and MCDM , 2017, Risk Decis. Anal..

[48]  K. S. Ravichandran,et al.  Generalized orthopair fuzzy weighted distance‐based approximation (WDBA) algorithm in emergency decision‐making , 2019, Int. J. Intell. Syst..

[49]  Chen Zhao,et al.  A Cognitive Model of Construction Workers’ Unsafe Behaviors , 2016 .

[50]  Harish Garg,et al.  A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems , 2016, Appl. Soft Comput..

[51]  Harish Garg,et al.  Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure , 2018, Computers & Industrial Engineering.

[52]  Kim Lyngby Mikkelsen,et al.  Effects of Firm Size on Risks and Reporting of Elevation Fall Injury in Construction Trades , 2003, Journal of occupational and environmental medicine.

[53]  Osama Abudayyeh,et al.  An investigation of management’s commitment to construction safety , 2006 .

[54]  W. Daniell,et al.  Evaluation of a healthcare safety climate measurement tool. , 2008, Journal of safety research.

[55]  Arunodaya Raj Mishra,et al.  Shapley divergence measures with VIKOR method for multi-attribute decision-making problems , 2017, Neural Computing and Applications.

[56]  Zakaria Harun,et al.  Contractors' perception of the use of costs of quality system in Malaysian building construction projects , 2012 .

[57]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[58]  Muhammet Gul,et al.  A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions , 2018 .

[59]  Xindong Peng,et al.  Algorithms for Interval-Valued Pythagorean Fuzzy Sets in Emergency Decision Making Based on Multiparametric Similarity Measures and WDBA , 2019, IEEE Access.

[60]  Arunodaya Raj Mishra,et al.  Biparametric Information Measures-Based TODIM Technique for Interval-Valued Intuitionistic Fuzzy Environment , 2018 .

[61]  Qingwen Xu,et al.  Collaborative information integration for construction safety monitoring , 2019, Automation in Construction.

[62]  Yantao Yu,et al.  Visualization technology-based construction safety management: A review , 2017 .

[63]  Sathyanarayanan Rajendran,et al.  Risk and Financial Impacts of Prevention through Design Solutions , 2013 .

[64]  Ashu Bansal,et al.  Multi-criteria decision making approach for the selection of software effort estimation model , 2017 .

[65]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[66]  Hao-Tien Liu,et al.  A fuzzy risk assessment approach for occupational hazards in the construction industry , 2012 .

[67]  Arunodaya Raj Mishra,et al.  Extended MABAC method based on divergence measures for multi-criteria assessment of programming language with interval-valued intuitionistic fuzzy sets , 2018, Granular Computing.

[68]  Matthew R. Hallowell,et al.  Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices , 2018 .

[69]  Lihua Wu,et al.  Entropy, Similarity Measure, Inclusion Measure of Intuitionistic Fuzzy Sets and Their Relationships , 2012, Int. J. Comput. Intell. Syst..

[70]  Moonseo Park,et al.  Accident Analysis and Prevention , 2014 .

[71]  Christophe Cruz,et al.  Semantic trajectory insights for worker safety in dynamic environments , 2019, Automation in Construction.

[72]  Jon C. Dattorro,et al.  Convex Optimization & Euclidean Distance Geometry , 2004 .

[73]  M. Koskenvuo,et al.  Interpersonal conflict as a predictor of work disability: a follow-up study of 15,348 Finnish employees. , 1996, Journal of psychosomatic research.

[74]  Oi-ling Siu,et al.  Safety climate and safety performance among construction workers in Hong Kong. The role of psychological strains as mediators. , 2004, Accident; analysis and prevention.

[75]  Dov Zohar,et al.  Thirty years of safety climate research: reflections and future directions. , 2010, Accident; analysis and prevention.

[76]  Alan H. S. Chan,et al.  Risk-taking behaviors of Hong Kong construction workers – A thematic study , 2017 .

[77]  Qian Sheng Zhang,et al.  Some Similarity Measures of Interval-Valued Intuitionistic Fuzzy Sets and Application to Pattern Recognition , 2010 .

[78]  Mohd Dilshad Ansari,et al.  New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection , 2017, International Journal of Fuzzy Systems.

[79]  Nikhil R. Pal,et al.  Some new information measures for fuzzy sets , 1993, Inf. Sci..

[80]  Abel Pinto,et al.  Risk Assessment in Construction Industry ? Overview and Reflection , 2011 .

[81]  Julie M Sampson,et al.  [Case Study] CityCenter and Cosmopolitan Construction Projects, Las Vegas, Nevada: lessons learned from the use of multiple sources and mixed methods in a safety needs assessment. , 2010, Journal of safety research.

[82]  Jimmie Hinze,et al.  Leading indicators of construction safety performance , 2013 .

[83]  Arunodaya Raj Mishra,et al.  A novel WASPAS approach for multi-criteria physician selection problem with intuitionistic fuzzy type-2 sets , 2020, Soft Comput..

[84]  Muhammet Gulź,et al.  A state of the art literature review of VIKOR and its fuzzy extensions on applications , 2016 .

[85]  Fanyong Meng,et al.  Interval‐Valued Intuitionistic Fuzzy Multiattribute Group Decision Making Based on Cross Entropy Measure and Choquet Integral , 2013, Int. J. Intell. Syst..

[86]  Dongping Fang,et al.  Leadership improvement and its impact on workplace safety in construction projects: A conceptual model and action research , 2017 .

[87]  Yangming Shi,et al.  Impact assessment of reinforced learning methods on construction workers' fall risk behavior using virtual reality , 2019, Automation in Construction.

[88]  Xindong Peng,et al.  Algorithm for Pythagorean Fuzzy Multi-criteria Decision Making Based on WDBA with New Score Function , 2019, Fundam. Informaticae.

[89]  Arunodaya Raj Mishra,et al.  Intuitionistic fuzzy divergence measure-based multi-criteria decision-making method , 2017, Neural Computing and Applications.

[90]  Rakesh Garg,et al.  Fuzzy multi-attribute decision making evaluation of e-learning websites using FAHP, COPRAS, VIKOR, WDBA , 2017 .

[91]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[92]  Jurgita Antucheviciene,et al.  Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF) , 2014, Appl. Soft Comput..

[93]  Zhi-yong Bai,et al.  An Interval-Valued Intuitionistic Fuzzy TOPSIS Method Based on an Improved Score Function , 2013, TheScientificWorldJournal.

[94]  Z. Xu.  On Similarity Measures of interval-valued Intuitionistic Fuzzy Sets and Their Application to Pattern Recognitions , 2007 .

[95]  J. Kacprzyk,et al.  Ranking of intuitionistic fuzzy alternatives in a multi-criteria decision making problem , 2009, NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society.