2011
DOI: 10.1108/09727981111175975
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Modeling of post‐construction failure factors of bridges

Abstract: PurposeThe failure of a bridge is a complex phenomenon due to the involvement of a large number of factors. Bridge failure cannot be attributed to a single cause due to the intricacies involved in the relationship among the factors causing failure. The aim of this paper is to identify the various factors causing bridge failure and show an interdependence/relationship among them.Design/methodology/approachInterpretive structural modeling (ISM), which is a tool of structural analysis for interactive learning, ha… Show more

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Cited by 9 publications
(7 citation statements)
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References 18 publications
(15 reference statements)
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“…Chandramowli et al (2011) analyze the barriers to development in landfill communities using ISM in the USA. A study by Wakchaure and Jha (2011) developed the ISM approach to formulate a hierarchical structure of the phenomenon of bridge failure showing the interrelationship of the causative factors. A study by Ansari et al (2013) used a structural model of the barriers to implement solar power installations in India.…”
Section: Ism Methodologymentioning
confidence: 99%
“…Chandramowli et al (2011) analyze the barriers to development in landfill communities using ISM in the USA. A study by Wakchaure and Jha (2011) developed the ISM approach to formulate a hierarchical structure of the phenomenon of bridge failure showing the interrelationship of the causative factors. A study by Ansari et al (2013) used a structural model of the barriers to implement solar power installations in India.…”
Section: Ism Methodologymentioning
confidence: 99%
“…These ten experts/specialists were separated into two groups. For the principle of the study, Mandal and Deshmukh (1994) Vendor selection relationships are identified Sharma and Gupta (1995) Hierarchy of actions was developed for waste management in India Singh et al (2003) Develop interdependence among KM (knowledge management) variables Jharkharia and Shankar (2005) Mutual relationship is developed among IT-supported enablers of SCM To model the reverse logistics variables typically found in computer hardware supply chains To model reverse logistics barriers of an automobile industries Nishat Faisal et al (2006) Risk mitigation enablers mutual relationship model was developed Faisal et al (2007) Model was proposed for Supply chain agility enablers Raj et al (2008) Indian FMS enablers relationship was developed with the help of ISM Wan and Frank Chen (2008) Complex relationship among barriers to energy saving in China Singh and Kant (2008) Develop interdependence among knowledge management (KM) variables Kannan et al (2008) Green suppliers environment performance criteria is analyzed with ISM and AHP Ahuja et al (2009) ICT benefits importance is analyzed for building project management Kannan et al (2009) ISM model for the assortment of reverse logistics provider Mudgal et al (2010) To model and analyze key problem in GSCP Ramesh et al (2010) ISM among the barriers of SC collaboration Yrd and Omur (2010) ISM for barriers encountered in innovation process in Turkey Khurana et al (2010) ISM was developed for building trust in Indian manufacturing industry Talib et al (2011) ISM modeling among the barriers of TQM Sampat Wakchaure and Neeraj Jha (2011) Develop a hierarchical structure of the phenomenon of bridge failure showing the interrelationship of the causative factors Eswarlal et al (2011) ISM of the renewable energy enablers for sustainable development Diabat and Govindan (2011) Modeling for the drivers distressing the accomplishment of GSCM Luthra et al (2011) ISM for barriers to implement GSCM in Indian automobile industry Pfohl et al (2011) ISM was formulated among supply chain risks Alawamleh and Popplewell (2011) Complex relationship among risk sources in a virtual association Diabat et al (2012) Analyses the various risks mixed up in supply chain of food Satapathy and Mishra (2013)...…”
Section: Ism Methodology and Model Developmentmentioning
confidence: 99%
“…This research further utilizes Fuzzy MICMAC analysis to further categorize the CSFs (Ali and Khan 2012;Gorane and Kant 2013).). MICMAC analysis is to sort the variables according to their driving power and dependence (Mandal and Deshmukh 1994;Wakchaure and Jha 2011). It is basically performed for identifying factors, which drive the whole system (Jena et al 2017).…”
Section: Demerits Of Ismmentioning
confidence: 99%