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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 78
Edited by: B.H.V. Topping
Paper 22

Crane Failure Analysis using Fault Tree and Fuzzy Logic

C. Wong+, F.C. Hadipriono+, J.W. Duane+, R.E. Larew+ and D.H. Barker*

+Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, Columbus, Ohio, USA
*Division of Civil Engineering, Environmental Health and Safety Management, School of Property and Construction, Nottingham Trent University, Nottingham, UK

Full Bibliographic Reference for this paper
C. Wong, F.C. Hadipriono, J.W. Duane, R.E. Larew, D.H. Barker, "Crane Failure Analysis using Fault Tree and Fuzzy Logic", in B.H.V. Topping, (Editor), "Proceedings of the Seventh International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 22, 2003. doi:10.4203/ccp.78.22
Keywords: crane accidents, crane failure, fault tree analysis, fuzzy fault tree analysis, fuzzy logic, safety.

Even though current construction technology in the United States has become quite sophisticated, the number of injuries and deaths in construction industry has not proportionally decreased. No construction company wants to see its workers injure or lose their lives, but beyond humanitarian consideration, accidents cut into profits and construction companies must minimise their rate of accident to be competitive. One of the most serious accidents, crane accidents, happens very frequently and often results in workers' deaths and serious injuries. The Occupational Safety and Health Administration (OSHA) reported that more than 500 United States construction workers died in crane accidents from 1984 to 1994. On July 14, 1999, a Big Blue crane came crashing down upon the construction site when it was under operation in a windy condition. Unfortunately, three ironworkers could not escape in time and were struck by the crane and died.

To develop safety procedures to prevent crane accidents from happening, it is essential to investigate, analyse the accident, and study every factor leading to crane failure. A crane failure database was produced by the authors to contain general information about causes of different crane failures over the period beginning in 1993 and extending to 2002. The information in the database was collected mainly from the online newspaper collection database-Academic Universe LexisNexis(TM) (LexisNexis(TM) 2001), engineering journals and magazines such as Engineering News Record (ENR 1993-2002), Civil Engineering (CE 1993-2002), and Internet resources. By collecting data regarding crane accidents and analysing them, this study can serve as an informational source in order to promote safety and minimise future accident frequency. To fully understand information provided by this study, it is important to understand how and why crane failures.

The database provides reports of the causes of crane failures that can be used by construction related personnel such as crane operators, safety engineer, site managers, and workers on site. It indicates that the most common cause for crane failure is unknown cause since reporting is usually unavailable after OSHA investigation. The second most common cause is enabling cause, such as operational error, maintenance error, and overloading, in decreasing order.

In this study, the fault tree analysis is focused on mobile cranes. Mobile crane failures are classified into failure of boom, failure of jib and failure of outrigger. Outrigger is affected by the following factors: enabling causes - overloading when the outrigger is not fully extended and problems with outrigger connections; triggering causes - impact forces on outrigger; loss of support (cribbing failure) - inadequate cribbing, and loss of support of outrigger due to soil failure. The authors limited the software to outrigger causes. However, this software establishes a template for boom and jib safety evaluations.

A fault tree analysis is a systems safety engineering technique used to identify possible modes of occurrence of a specified undesired top event. Fault tree analysis is both a prognostic tool and a diagnostic tool. The fault tree analysis provides a deductive process to identify all the possible modes of occurrence of a specified undesired top event. The top undesired event in the fault tree analysis is "crane failure." With the use of a fault tree, one can find all the primary cause events. After the fault tree analysis is established, fuzzy logic is used for further analysis of the crane failure.

The development of fuzzy logic software with a user friendly graphic interface is necessary so that both advanced users such as experts in crane, and novice users such as crane operators, site managers, workers on site will be able to assess crane safety.

The software shows safety performance of the mobile crane under various operating conditions. The program applied the fault tree and fuzzy set concepts to capture expert knowledge about enabling, triggering and loss of support events by permitting experts to generate implication rules for crane failures. The expert knowledge of crane failures captured by the software can be used by non-experts, such as crane operators, site managers, other construction workers on site, to evaluate site soil conditions, crane operating procedures and crane conditions that might lead to outrigger failure. The overall outrigger consequence will be determined. Rotational and translational fuzzy models are used to determine the overall performance of the outrigger.

The software includes figures of the mobile crane model and the fault trees concerning the crane failure. The software contains a fuzzy fault tree analysis, fuzzy fault tree analysis rotational model, and a fuzzy fault tree analysis translational model. Help statements from the drop-down menu are also provided in the software so that user can learn how to use the software under various conditions, understand the graphical results, and the safety index results of the software. Both models show consistent results. However, the authors concluded that fuzzy fault tree translational model is easier to understand than the fuzzy fault tree rotational model. In addition, the linguistic terms of the former model do not need to undergo a defuzzification process. Furthermore, the authors believe that the software could provide a template for evaluating other crane components such as the boom and the jib.

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