A material or environmental factor can decrease the durability of a concrete structure. If the decreased durability is not caused by environmentally induced deterioration, it may signify a problem with the material itself, which is a serious issue affecting the entire structure. The evaluation and prediction of the durability of a concrete structure is considered a critical research area. Existing evaluation and prediction methods using non-destructive inspection or core sampling have limitations. Some studies applied image processing techniques to overcome these shortcomings; nonetheless, a problem remains in analyzing the obtained images. To solve this problem, a performance evaluation system model for concrete structures is proposed. The model is based on a deep convolutional neural network (DCNN). This model is intended to establish a foundation for solving difficult problems in the construction industry. The proposed concrete performance evaluation model defines a concrete surface image as a parameter of input data and uses a DCNN-based machine learning algorithm to produce the performance evaluation results as output data. This model consists of a database construction phase, a DCNN-based algorithm design phase, and an algorithm implementation phase. The complete development of this model is expected to resolve many problems of structure maintenance systems, which are the products of socio-environmental changes in the Republic of Korea. Furthermore, various data collected during the whole life cycle of a structure can be utilized to apply the proposed deep learning technique to various construction areas.
|Number of pages||11|
|Journal||International Journal of Sustainable Building Technology and Urban Development|
|Publication status||Published - 2017 Jan 1|
All Science Journal Classification (ASJC) codes
- Building and Construction