Algorithm for quantitative analysis of close call events and personalized feedback in construction safety
In many of the developed countries about 15–25% of all fatal construction workplace accidents relate to a too close proximity of pedestrian workers to construction equipment or hazardous materials. Extracting knowledge from data on near hits (aka. close calls) might warrant better understanding on t...
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Veröffentlicht in: | Automation in construction 2019-03, Vol.99, p.206-222 |
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description | In many of the developed countries about 15–25% of all fatal construction workplace accidents relate to a too close proximity of pedestrian workers to construction equipment or hazardous materials. Extracting knowledge from data on near hits (aka. close calls) might warrant better understanding on the root causes that lead to such incidents and eliminate them early in the risk mitigation process. While a close call is a subtle event where workers are in close proximity to a hazard, its frequency depends–among other factors–on poor site layout, a worker's willingness to take risks, limited safety education, and pure coincidence. For these reasons, pioneering organizations have recognized the potential of gathering and analyzing leading indicator data on close calls. However, mostly manual approaches are infrequently performed, subjective due to situational assessment, imprecise in level of detail, and importantly, reactive or inconsistent in effective or timely follow-ups by management. While existing predictive analytics research targets change at strategic levels in the hierarchy of organizations, personalized feedback to strengthen an individual worker's hazard recognition and avoidance skill set is yet missing. This study tackles the bottom of Heinrich's safety pyramid by providing an in-depth quantitative analysis of close calls. Modern positioning technology records trajectory data, whereas computational algorithms automatically generate previously unavailable details to close call events. The derived information is embedded in simplified geometric information models that users on a construction site can retrieve, easily understand, and adapt in existing preventative hazard recognition and control processes. Results from scientific and field experiments demonstrate that the developed system works successfully under the constraints of currently available positioning technology.
[Display omitted]
•Close calls are frequent events – their data potentially leads to safer construction.•Reporting and management processes are unavailable or insufficient at work sites.•New sensing and computing technology assists in precise data tracking and analysis.•Predictive analytics uses information for proactive rather than reactive mitigation.•Personalized feedback has potential to empower humans and enhance their skill sets. |
doi_str_mv | 10.1016/j.autcon.2018.11.014 |
format | Article |
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[Display omitted]
•Close calls are frequent events – their data potentially leads to safer construction.•Reporting and management processes are unavailable or insufficient at work sites.•New sensing and computing technology assists in precise data tracking and analysis.•Predictive analytics uses information for proactive rather than reactive mitigation.•Personalized feedback has potential to empower humans and enhance their skill sets.</description><identifier>ISSN: 0926-5805</identifier><identifier>EISSN: 1872-7891</identifier><identifier>DOI: 10.1016/j.autcon.2018.11.014</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Accident investigation ; Algorithms ; Analytics ; Building information modeling ; Close call ; Construction accidents & safety ; Construction equipment ; Construction safety ; Construction site accidents ; Data recording, visualization, data mining ; Education and training ; Feedback ; Hazard identification ; Hazardous materials ; Location tracking ; Near miss ; Occupational safety ; Organizations ; Predictive analytics ; Quantitative analysis ; Recognition ; Workplace accidents</subject><ispartof>Automation in construction, 2019-03, Vol.99, p.206-222</ispartof><rights>2018 Elsevier B.V.</rights><rights>Copyright Elsevier BV Mar 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c334t-de0671bddba442fe2942b17c344d07904fbb81969254dd0f6ef2efc2642740be3</citedby><cites>FETCH-LOGICAL-c334t-de0671bddba442fe2942b17c344d07904fbb81969254dd0f6ef2efc2642740be3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.autcon.2018.11.014$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Golovina, Olga</creatorcontrib><creatorcontrib>Perschewski, Manuel</creatorcontrib><creatorcontrib>Teizer, Jochen</creatorcontrib><creatorcontrib>König, Markus</creatorcontrib><title>Algorithm for quantitative analysis of close call events and personalized feedback in construction safety</title><title>Automation in construction</title><description>In many of the developed countries about 15–25% of all fatal construction workplace accidents relate to a too close proximity of pedestrian workers to construction equipment or hazardous materials. Extracting knowledge from data on near hits (aka. close calls) might warrant better understanding on the root causes that lead to such incidents and eliminate them early in the risk mitigation process. While a close call is a subtle event where workers are in close proximity to a hazard, its frequency depends–among other factors–on poor site layout, a worker's willingness to take risks, limited safety education, and pure coincidence. For these reasons, pioneering organizations have recognized the potential of gathering and analyzing leading indicator data on close calls. However, mostly manual approaches are infrequently performed, subjective due to situational assessment, imprecise in level of detail, and importantly, reactive or inconsistent in effective or timely follow-ups by management. While existing predictive analytics research targets change at strategic levels in the hierarchy of organizations, personalized feedback to strengthen an individual worker's hazard recognition and avoidance skill set is yet missing. This study tackles the bottom of Heinrich's safety pyramid by providing an in-depth quantitative analysis of close calls. Modern positioning technology records trajectory data, whereas computational algorithms automatically generate previously unavailable details to close call events. The derived information is embedded in simplified geometric information models that users on a construction site can retrieve, easily understand, and adapt in existing preventative hazard recognition and control processes. Results from scientific and field experiments demonstrate that the developed system works successfully under the constraints of currently available positioning technology.
[Display omitted]
•Close calls are frequent events – their data potentially leads to safer construction.•Reporting and management processes are unavailable or insufficient at work sites.•New sensing and computing technology assists in precise data tracking and analysis.•Predictive analytics uses information for proactive rather than reactive mitigation.•Personalized feedback has potential to empower humans and enhance their skill sets.</description><subject>Accident investigation</subject><subject>Algorithms</subject><subject>Analytics</subject><subject>Building information modeling</subject><subject>Close call</subject><subject>Construction accidents & safety</subject><subject>Construction equipment</subject><subject>Construction safety</subject><subject>Construction site accidents</subject><subject>Data recording, visualization, data mining</subject><subject>Education and training</subject><subject>Feedback</subject><subject>Hazard identification</subject><subject>Hazardous materials</subject><subject>Location tracking</subject><subject>Near miss</subject><subject>Occupational safety</subject><subject>Organizations</subject><subject>Predictive analytics</subject><subject>Quantitative analysis</subject><subject>Recognition</subject><subject>Workplace accidents</subject><issn>0926-5805</issn><issn>1872-7891</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKAzEUhoMoWKtv4CLgesacNJ3LRijFGxTc6DpkkhNNnU7aJFOoT29KXbs6i__Cfz5CboGVwKC6X5dqTNoPJWfQlAAlA3FGJtDUvKibFs7JhLW8KuYNm1-SqxjXjLGaVe2EuEX_6YNLXxtqfaC7UQ3JJZXcHqkaVH-ILlJvqe59RKpV31Pc45BiVg3dYog-u9wPGmoRTaf0N3UDzWNiCqNOzg80KovpcE0urOoj3vzdKfl4enxfvhSrt-fX5WJV6NlMpMIgq2roTK4SglvkreAd1HomhGF1y4TtugbaquVzYQyzFVqOVvNK8FqwDmdTcnfq3Qa_GzEmufZjyCOj5FA3wJuqFdklTi4dfIwBrdwGt1HhIIHJI1S5lieo8ghVAsgMNcceTjHMH-wdBhm1w0GjcQF1ksa7_wt-AdhhhEE</recordid><startdate>201903</startdate><enddate>201903</enddate><creator>Golovina, Olga</creator><creator>Perschewski, Manuel</creator><creator>Teizer, Jochen</creator><creator>König, Markus</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201903</creationdate><title>Algorithm for quantitative analysis of close call events and personalized feedback in construction safety</title><author>Golovina, Olga ; Perschewski, Manuel ; Teizer, Jochen ; König, Markus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-de0671bddba442fe2942b17c344d07904fbb81969254dd0f6ef2efc2642740be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accident investigation</topic><topic>Algorithms</topic><topic>Analytics</topic><topic>Building information modeling</topic><topic>Close call</topic><topic>Construction accidents & safety</topic><topic>Construction equipment</topic><topic>Construction safety</topic><topic>Construction site accidents</topic><topic>Data recording, visualization, data mining</topic><topic>Education and training</topic><topic>Feedback</topic><topic>Hazard identification</topic><topic>Hazardous materials</topic><topic>Location tracking</topic><topic>Near miss</topic><topic>Occupational safety</topic><topic>Organizations</topic><topic>Predictive analytics</topic><topic>Quantitative analysis</topic><topic>Recognition</topic><topic>Workplace accidents</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Golovina, Olga</creatorcontrib><creatorcontrib>Perschewski, Manuel</creatorcontrib><creatorcontrib>Teizer, Jochen</creatorcontrib><creatorcontrib>König, Markus</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Automation in construction</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Golovina, Olga</au><au>Perschewski, Manuel</au><au>Teizer, Jochen</au><au>König, Markus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Algorithm for quantitative analysis of close call events and personalized feedback in construction safety</atitle><jtitle>Automation in construction</jtitle><date>2019-03</date><risdate>2019</risdate><volume>99</volume><spage>206</spage><epage>222</epage><pages>206-222</pages><issn>0926-5805</issn><eissn>1872-7891</eissn><abstract>In many of the developed countries about 15–25% of all fatal construction workplace accidents relate to a too close proximity of pedestrian workers to construction equipment or hazardous materials. Extracting knowledge from data on near hits (aka. close calls) might warrant better understanding on the root causes that lead to such incidents and eliminate them early in the risk mitigation process. While a close call is a subtle event where workers are in close proximity to a hazard, its frequency depends–among other factors–on poor site layout, a worker's willingness to take risks, limited safety education, and pure coincidence. For these reasons, pioneering organizations have recognized the potential of gathering and analyzing leading indicator data on close calls. However, mostly manual approaches are infrequently performed, subjective due to situational assessment, imprecise in level of detail, and importantly, reactive or inconsistent in effective or timely follow-ups by management. While existing predictive analytics research targets change at strategic levels in the hierarchy of organizations, personalized feedback to strengthen an individual worker's hazard recognition and avoidance skill set is yet missing. This study tackles the bottom of Heinrich's safety pyramid by providing an in-depth quantitative analysis of close calls. Modern positioning technology records trajectory data, whereas computational algorithms automatically generate previously unavailable details to close call events. The derived information is embedded in simplified geometric information models that users on a construction site can retrieve, easily understand, and adapt in existing preventative hazard recognition and control processes. Results from scientific and field experiments demonstrate that the developed system works successfully under the constraints of currently available positioning technology.
[Display omitted]
•Close calls are frequent events – their data potentially leads to safer construction.•Reporting and management processes are unavailable or insufficient at work sites.•New sensing and computing technology assists in precise data tracking and analysis.•Predictive analytics uses information for proactive rather than reactive mitigation.•Personalized feedback has potential to empower humans and enhance their skill sets.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.autcon.2018.11.014</doi><tpages>17</tpages></addata></record> |
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subjects | Accident investigation Algorithms Analytics Building information modeling Close call Construction accidents & safety Construction equipment Construction safety Construction site accidents Data recording, visualization, data mining Education and training Feedback Hazard identification Hazardous materials Location tracking Near miss Occupational safety Organizations Predictive analytics Quantitative analysis Recognition Workplace accidents |
title | Algorithm for quantitative analysis of close call events and personalized feedback in construction safety |
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