Time-statistical laws of workers’ unsafe behavior in the construction industry: A case study

Abstract The construction industry is extremely high risk, and the unsafe behavior of workers is thought to be a critical factor in that risk level. Considering the limitation that existing studies merely analyze the relationship between unsafe behavior and the time factor, this paper uses a case study to explore two aspects of the time-statistical laws of workers’ unsafe behavior: (i) the characteristics of interevent time distributions and (ii) association rules for different worker types. First, workers’ unsafe acts at one metro construction site are collected and classified. Second, interevent time distributions of workers’ unsafe behavior from different types are analyzed via a human dynamics approach. Finally, a rule mining database is built, from which association rules concerning unsafe behavior, worker type and construction phase are determined using the Apriori algorithm. The results indicate that the interevent time distributions are fat tailed and show that workers’ unsafe behavior has the characteristics of burstiness and memory. Furthermore, the strong rules ‘construction phase → unsafe behavior’ exist for different worker types. The research presented in this paper can facilitate a better understanding of workers’ unsafe behavioral patterns and can accordingly be used to control frequent and widespread unsafe acts among different types of workers in different construction phases.

[1]  Lieyun Ding,et al.  Application of 4D visualization technology for safety management in metro construction , 2013 .

[2]  John A. Gambatese,et al.  Alignment between Lean Principles and Practices and Worker Safety Behavior , 2017 .

[3]  Wei-Chang Yeh,et al.  Using association rules and particle swarm optimization approach for part change , 2009, Expert Syst. Appl..

[4]  Yantao Yu,et al.  Image-and-Skeleton-Based Parameterized Approach to Real-Time Identification of Construction Workers’ Unsafe Behaviors , 2018, Journal of Construction Engineering and Management.

[5]  Tao Cheng,et al.  Proximity hazard indicator for workers-on-foot near miss interactions with construction equipment and geo-referenced hazard areas , 2015 .

[6]  Bonaventura H.W. Hadikusumo,et al.  A Bayesian Belief Network model of organizational factors for improving safe work behaviors in Thai construction industry , 2016 .

[7]  D. Fang,et al.  Why operatives engage in unsafe work behavior: Investigating factors on construction sites , 2008 .

[8]  Heng Li,et al.  Proactive training system for safe and efficient precast installation , 2015 .

[9]  SangHyun Lee,et al.  Role of Social Norms and Social Identifications in Safety Behavior of Construction Workers. I: Theoretical Model of Safety Behavior under Social Influence , 2017 .

[10]  Miroslaw J. Skibniewski,et al.  Perceiving Interactions on Construction Safety Behaviors: Workers’ Perspective , 2016 .

[11]  Omid Kalatpour,et al.  Constructing a Bayesian network model for improving safety behavior of employees at workplaces. , 2017, Applied ergonomics.

[12]  Alexei Vazquez.  Impact of memory on human dynamics , 2007 .

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

[14]  Sou-Sen Leu,et al.  Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry , 2010 .

[15]  Javier Irizarry,et al.  Using Network Theory to Explore the Complexity of Subway Construction Accident Network (SCAN) for Promoting Safety Management , 2014 .

[16]  Terry E. McSween,et al.  Values-Based Safety Process: Improving Your Safety Culture With Behavior-Based Safety , 1995 .

[17]  Kwang-Il Goh,et al.  Burstiness and memory in complex systems , 2006 .

[18]  Heng Li,et al.  Stochastic state sequence model to predict construction site safety states through Real-Time Location Systems , 2016 .

[19]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[20]  Kamiar Aminian,et al.  Unraveling dynamics of human physical activity patterns in chronic pain conditions , 2013, Scientific Reports.

[21]  Tak Wing Yiu,et al.  Predicting safety behavior in the construction industry: development and test of an integrative model , 2016 .

[22]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[23]  Shlomo Havlin,et al.  Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records. , 2005, Physical review letters.

[24]  L Y Ding,et al.  A Big-Data-based platform of workers' behavior: Observations from the field. , 2016, Accident; analysis and prevention.

[25]  Shengyu Guo,et al.  A real-time control approach based on intelligent video surveillance for violations by construction workers , 2018 .

[26]  Sunita Sarawagi,et al.  Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications , 1998, SIGMOD '98.

[27]  Chia-Wen Liao,et al.  Data mining for occupational injuries in the Taiwan construction industry , 2008 .

[28]  Wen-der Yu,et al.  GA‐based multi-level association rule mining approach for defect analysis in the construction industry , 2015 .

[29]  Tao Zhou,et al.  Scaling and memory in recurrence intervals of Internet traffic , 2009, 0905.3878.

[30]  Han Wang,et al.  An experimental method to study the effect of fatigue on construction workers’ safety performance , 2015 .

[31]  Dan Peterson,et al.  Industrial accident prevention , 2016 .

[32]  Jhareswar Maiti,et al.  Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports , 2014 .

[33]  Mei-yung Leung,et al.  Impact of Job Stressors and Stress on the Safety Behavior and Accidents of Construction Workers , 2016 .

[34]  Zong-Wen Wei,et al.  Empirical analysis on the human dynamics of blogging behavior on GitHub , 2017 .

[35]  Robert K. Yin,et al.  Case Study Research and Applications: Design and Methods , 2017 .

[36]  Noora Nenonen,et al.  Analysing factors related to slipping, stumbling, and falling accidents at work: Application of data mining methods to Finnish occupational accidents and diseases statistics database. , 2013, Applied ergonomics.

[37]  Miroslaw J. Skibniewski,et al.  Characterizing time series of near-miss accidents in metro construction via complex network theory , 2017 .

[38]  Marco S. DiRenzo,et al.  Boundary conditions of the emotional exhaustion-unsafe behavior link: The dark side of group norms and personal control , 2016 .

[39]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[40]  SangUk Han,et al.  A vision-based motion capture and recognition framework for behavior-based safety management , 2013 .

[41]  Bruno Gonçalves,et al.  Human dynamics revealed through Web analytics , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  Huakang Liang,et al.  The Impact of Coworkers’ Safety Violations on an Individual Worker: A Social Contagion Effect within the Construction Crew , 2018, International journal of environmental research and public health.

[43]  Patrick X.W. Zou,et al.  Critical factors and paths influencing construction workers' safety risk tolerances. , 2016, Accident; analysis and prevention.

[44]  Ibrahim Cil,et al.  Consumption universes based supermarket layout through association rule mining and multidimensional scaling , 2012, Expert Syst. Appl..