Design and Implementation of Concrete Compressive Strength Data Cleaning Based on PCA-RANSAC Algorithm
1. School of Information Engineering, China Jiliang University, Hangzhou 310018, China
2. SINOMA Bonyear (Hangzhou) Intelligent Technology Co. Ltd., Hangzhou 310000, China
| Abstract: | The data used for concrete compressive strength prediction is prone to produce outliers in the process of collection, which will have a certain impact on the accuracy of the model, so it is necessary to remove the abnormal data through data cleaning. In this paper, to address this problem, the random sampling consistency algorithm is introduced, and combined with the principal component analysis dimensionality reduction method, an improved anomalous data cleaning method is proposed. Firstly, the effectiveness of the data cleaning model of the random sampling consistency algorithm is verified, and the performance is compared with the commonly used isolated forest algorithm and K-means algorithm on the public dataset, and the performance indexes of the random sampling consistency algorithm model are obviously leading, and furthermore, the data are downgraded by using the principal component analysis and the selection rules of the optimization of the sampling points, so as to form the principal component analysis-random sampling consistency algorithm data cleaning model. Experiments show that the performance index of principal component analysis-random sampling consistency algorithm has no significant decrease compared with random sampling consistency algorithm, but the number of iterations is reduced to 492, and the time consumed is reduced to 196ms, which is 4.45% and 5.14% of the traditional random sampling consistency algorithm, respectively. The proposed improved method alleviates the volatility of the random sampling consistency algorithm, greatly reduces the number of iterations and time-consuming of the algorithm, and has a significant cleaning effect. |
| Keywords: | Concrete Compressive Strength; Data Cleaning; Random Sampling Consensus; Principal Component Analysis |
| DOI: | 10.57237/j.cst.2023.04.004 |
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Received: 8 September 2023
Accepted: 26 October 2023
Published Online: 28 October 2023
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