Search

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. • The study is to develop a new algorithm, SOM-based Cluster Merging (SOMCM). • The paper is to investigate the relevance for bridge main components and their deterioration types. • The initial result involves 61 clusters being merged to 8 clusters with the predetermined iterations. • The clustering analysis for the final 8 clusters shows 9 major bridge maintenance association rules. [ABSTRACT FROM AUTHOR]

Typhoon Morakot has been the most severe typhoon disaster to strike Taiwan in recent decades causing tremendous damage to bridge surroundings in 2009. However, we still lack a means of assessing post-typhoon damage for follow-up rebuilding. This paper presents an integrated model that automatically measures changes in rivers, areas of damage to bridge surroundings, and changes in vegetation. The proposed model is based on a neurofuzzy mechanism enhanced by the self-organising map optimisation algorithm and also includes the particular functions of dilation, erosion, and skeletonisation to deal with river imagery. High resolution FORMOSAT-2 satellite imagery from before and after the invasion period is adopted. A bridge is randomly selected from the 129 destroyed due to the typhoon for applications of the model. The recognition results show that the river average width has increased 66% with a maximum increase of over 200%. The ruined segment of the bridge is located exactly in the most scoured region. There has also been a nearly 10% reduction in the vegetation coverage. The results yielded by the proposed model demonstrate a pinpoint accuracy rate of 99.94%. This study successfully develops a tool for large-scale damage assessment as well as for precise measurement after disasters. [ABSTRACT FROM AUTHOR]