A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Fuzzy Synthetic Evaluation Approach for Condition Rating of RC Bridge Decks
Present condition assessment (CA) is crucial for any bridge management system. CA helps bridge owners to decide funding for the most deserving bridges for maintenance, repair, or rehabilitation (MR&R) works. Moreover, the reliability of the CA approach depends on the reliability of the inspection data. Generally, CA is done based on the defects identified by visual inspection accompanied by some non-destructive tests (NDTs). But, uncertainties inherently involve in field data as subjectivity in visual inspection or imprecision in NDTs. So, this study endeavors a fuzzy-based synthetic evaluation method for the rating of reinforced concrete (RC) bridge deck condition using field data. The uncertainties in field data are tackled by fuzzy set theory. Here, trapezoidal membership functions (MFs) are used for the fuzzification of parameter values. AHP is applied to calculate the importance weight of evaluation parameters. Four parameters, i.e., concrete quality, carbonated concrete, chloride, and corrosion probability, are considered from the literature for comprehensive condition evaluation of RC bridge decks. Four linguistic scales, excellent (Level IV), good (Level III), fair (Level II), and poor (Level I) are used for the evaluation process. Finally, the present study is applied for rating the condition of an RC bridge deck, situated at Jharkhand, India, as a case example. The rating of the studied bridge deck condition is obtained as ‘fair’ category. The presented model could be used as a rational appraising technique for better fund allotment and more proper selection of MR&R schemes.
A Fuzzy Synthetic Evaluation Approach for Condition Rating of RC Bridge Decks
Present condition assessment (CA) is crucial for any bridge management system. CA helps bridge owners to decide funding for the most deserving bridges for maintenance, repair, or rehabilitation (MR&R) works. Moreover, the reliability of the CA approach depends on the reliability of the inspection data. Generally, CA is done based on the defects identified by visual inspection accompanied by some non-destructive tests (NDTs). But, uncertainties inherently involve in field data as subjectivity in visual inspection or imprecision in NDTs. So, this study endeavors a fuzzy-based synthetic evaluation method for the rating of reinforced concrete (RC) bridge deck condition using field data. The uncertainties in field data are tackled by fuzzy set theory. Here, trapezoidal membership functions (MFs) are used for the fuzzification of parameter values. AHP is applied to calculate the importance weight of evaluation parameters. Four parameters, i.e., concrete quality, carbonated concrete, chloride, and corrosion probability, are considered from the literature for comprehensive condition evaluation of RC bridge decks. Four linguistic scales, excellent (Level IV), good (Level III), fair (Level II), and poor (Level I) are used for the evaluation process. Finally, the present study is applied for rating the condition of an RC bridge deck, situated at Jharkhand, India, as a case example. The rating of the studied bridge deck condition is obtained as ‘fair’ category. The presented model could be used as a rational appraising technique for better fund allotment and more proper selection of MR&R schemes.
A Fuzzy Synthetic Evaluation Approach for Condition Rating of RC Bridge Decks
Lect.Notes Mechanical Engineering
Venkata Rao, Ravipudi (editor) / Taler, Jan (editor) / Das Khan, Sudha (author) / Topdar, Pijush (author) / Datta, Aloke Kumar (author)
Advanced Engineering Optimization Through Intelligent Techniques ; Chapter: 30 ; 319-330
2023-04-08
12 pages
Article/Chapter (Book)
Electronic Resource
English
Integrated Condition Rating Model for Reinforced Concrete Bridge Decks
| Online Contents | 2017
Integrated Condition Rating Model for Reinforced Concrete Bridge Decks
| British Library Online Contents | 2017
| British Library Online Contents | 2015