A case study of using a support vector machine on bridge inspection data

In Japan, regional governments manage 70% of all road bridges (over 700,000 road bridges), and half of these bridges will be in their service life in the near future. However, regional governments lack the resources (in finances, specialists, and technologies) that are needed to maintain this many bridges. Therefore, it is necessary to develop a practical method of bridge integrity evaluation to assess the degree of a bridge superstructure's structural health. This paper proposes a bridge integrity evaluation method by applying support vector machines (SVMs) of artificial intelligence (AI) techniques. This paper proposes that bridge integrity evaluation methods provide bridge inspectors and engineers with a more reliable and efficient bridge structural health assessment approach.