Research on Clustering of Material Inspection Data for Highway Construction Projects Based on Agglomerative Hierarchical Clustering

Abstract

Abstract In large-scale highway construction projects, the project implementers will carry out detailed testing of various material indicators used in the project. By processing and analyzing these material testing data, we can reasonably classify large-scale highway construction projects, so that each project implementer can better manage the quality of the materials, which will greatly help all parties to strengthen the quality control of the project and improve the level of project management. This study first preprocesses the material testing data to obtain a structured data set suitable for data analysis. Then statistical features are constructed for the features in the dataset, including maximum, minimum, mean, median and standard deviation, to improve the performance and accuracy of the model. Next, the clustering hierarchical clustering method is applied for classification and the classification results are visualized in the form of dendrograms. Finally, through the comparison of performance analysis and clustering evaluation indexes, it is concluded that the classification of works into 3 or 4 categories according to material performance is in line with the actual level of engineering quality.