Bayesian Decision Tool for the Analysis of Occupational Accidents in the Construction of Embankments

AbstractInstability and poor construction practices are responsible for the high accident rate in embankment construction in Spain. Applying a methodology based on data mining and attribute selection and using a 6-year database of accidents, key attributes in accidents associated with the construction of embankments were analyzed. Once the main predictors were identified, Bayesian networks in order to quantify the specific causes of different types of accidents were built. Thus, the main reasons for accidents as a preliminary phase to enhancing safety and embankment stability in mining and civil engineering works can be accurately identified and quantified.

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  F. G. Bastante,et al.  ROFRAQ: A statistics-based empirical method for assessing accident risk from rockfalls in quarries. International Journal of Rock Mechanics and Mining Sciences. , 2008 .

[3]  James K. C. Chen,et al.  Managing occupational health and safety in the mining industry , 2013 .

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[5]  Marek J. Druzdzel,et al.  SMILE: Structural Modeling, Inference, and Learning Engine and GeNIE: A Development Environment for Graphical Decision-Theoretic Models , 1999, AAAI/IAAI.

[6]  Clandia Maffini Gomes,et al.  Management for sustainability in companies of the mining sector: an analysis of the main factors related with the business performance , 2014 .

[7]  Javier Taboada,et al.  A Bayesian network analysis of workplace accidents caused by falls from a height , 2009 .

[8]  Lin Chen,et al.  Empirical analysis on contribution share of safety investment to economic growth: A case study of Chinese mining industry , 2012 .

[9]  Lluís Sanmiquel,et al.  Analysis of work related accidents in the Spanish mining sector from 1982-2006. , 2010, Journal of safety research.

[10]  José M. Matías,et al.  A machine learning methodology for the analysis of workplace accidents , 2008, Int. J. Comput. Math..

[11]  Patrick J Coleman,et al.  Measuring mining safety with injury statistics: lost workdays as indicators of risk. , 2007, Journal of safety research.

[12]  X. Li,et al.  Finite element analysis of slope stability using a nonlinear failure criterion , 2007 .

[13]  Miroslaw J. Skibniewski,et al.  Bayesian-network-based safety risk analysis in construction projects , 2014, Reliab. Eng. Syst. Saf..

[14]  Stefano Mainardi Earnings and work accident risk: a panel data analysis on mining , 2005 .

[15]  Werner Lienhart,et al.  Case studies of high-sensitivity monitoring of natural and engineered slopes , 2015 .

[16]  Sou-Sen Leu,et al.  Bayesian-network-based safety risk assessment for steel construction projects. , 2013, Accident; analysis and prevention.

[17]  Shu-Hsien Liao,et al.  Data mining techniques and applications - A decade review from 2000 to 2011 , 2012, Expert Syst. Appl..

[18]  George L. Germain,et al.  Practical loss control leadership , 1996 .

[19]  Miroslaw J. Skibniewski,et al.  A dynamic Bayesian network based approach to safety decision support in tunnel construction , 2015, Reliab. Eng. Syst. Saf..

[20]  Jhareswar Maiti,et al.  The role of behavioral factors on safety management in underground mines , 2007 .

[21]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[22]  Eugene Charniak,et al.  Bayesian Networks without Tears , 1991, AI Mag..

[23]  Josep M. Rossell,et al.  Study of Spanish mining accidents using data mining techniques , 2015 .

[24]  Alberto Fonseca,et al.  Sustainability reporting among mining corporations: a constructive critique of the GRI approach , 2014 .

[25]  Paul Swuste,et al.  Is it possible to influence safety in the building sector? A literature review extending from 1980 until the present , 2012 .