Abstract:

The paper proposes a new deep structure model, called Densely Connected Cascade Forest-Weighted K Nearest Neighbors (DCCF-WKNNs), to implement the corrosion data modelling and corrosion knowledge-mining. Firstly, we collect 409 outdoor atmospheric corrosion samples of low-alloy steels as experiment datasets. Then, we give the proposed methods process, including random forests-K nearest neighbors (RF-WKNNs) and DCCF-WKNNs. Finally, we use the collected datasets to verify the performance of the proposed method. The results show that compared with commonly used and advanced machine-learning algorithms such as artificial neural network (ANN), support vector regression (SVR), random forests (RF), and cascade forests (cForest), the proposed method can obtain the best prediction results. In addition, the method can predict the corrosion rates with variations of any one single environmental variable, like pH, temperature, relative humidity, SO₂, rainfall or Cl^-. By this way, the threshold of each variable, upon which the corrosion rate may have a large change, can be further obtained.

Key words: Random forests, Deep forest model, Low-alloy steels, Outdoor atmospheric corrosion, Prediction and data-mining