Smart bridge maintenance using cluster merging algorithm based on self-organizing map optimization

Jieh Haur Chen, Mu Chun Su, Sheng Kuo Lin, Wei Jen Lin, Masoud Gheisari

Research output: Contribution to journalArticlepeer-review


Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners.

Original languageEnglish
Article number104913
JournalAutomation in Construction
StatePublished - Aug 2023


  • Bridge maintenance
  • Clustering
  • Deterioration
  • Inspection
  • SOM


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