Abstract
Due to aging and corrosion, especially structures, which are part of the traffic infrastructure, have to be inspected regularly. In the case of lamp posts Austrian Codes require an inspection interval of six years. The onsite inspection consists of measurements and visual inspection whereupon the latter evidently is partly subjective. As the conservative way of assessment involves experts who combine measurement analysis and visual inspection to conduct a classification of thousands of lamp posts a year, it is obvious that a certain amount of subjectivity influences the result. Moreover, the time between contract award and delivering results is short. Increased objectivity hand in hand with less time consumption will increase result-quality and reduce costs.
In order to reach higher result-objectivity paired with less time-effort, automated processes become a must. This contribution describes a system which is based on the methodology Case-based Reasoning (CBR) for an automated evaluation of comparably simple structures, as lamp posts are. CBR can be used to find solutions for new problems without starting a new problem solving process, but by using solutions from past problems/cases for the new case. Consequently, beside cost reduction and time saving, comparably short and simple knowledge gathering processes are main benefits of the system.
Original language | English |
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Title of host publication | Proceedings of the 7th International Workshop on Structural Health Monitoring 2009 |
Number of pages | 8 |
Publication status | Published - 2009 |
Fields of science
- 102001 Artificial intelligence
- 102006 Computer supported cooperative work (CSCW)
- 102010 Database systems
- 102014 Information design
- 102015 Information systems
- 102016 IT security
- 102028 Knowledge engineering
- 102019 Machine learning
- 102022 Software development
- 102025 Distributed systems
- 502007 E-commerce
- 505002 Data protection
- 506002 E-government
- 509018 Knowledge management
- 202007 Computer integrated manufacturing (CIM)
- 102033 Data mining
- 102035 Data science