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Roadway Asset Inspection Sampling Using High‐Dimensional Clustering and Locality‐Sensitivity Hashing
Author(s) -
Chen Zhuo,
Liu Xiaoyue Cathy
Publication year - 2019
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12405
Subject(s) - cluster analysis , computer science , data mining , visual inspection , spectral clustering , sampling (signal processing) , similarity (geometry) , sample (material) , hash function , asset (computer security) , artificial intelligence , image (mathematics) , computer vision , filter (signal processing) , chemistry , computer security , chromatography
A high‐dimensional clustering‐based sampling method for roadway asset condition inspection is proposed in this study. The method complements existing literature by selecting sample roadway segments that contain multiple types of assets (e.g., signage, shoulder work, pavement marking, etc.) for the accurate estimation of their respective level of maintenance (LOMs). This is consistent with the standard maintenance procedure as inspection activities are often conducted on roadway segment basis. The proposed method consists of three components: current condition estimation, similarity matrix construction, and stratification. Current condition estimation predicts assets’ “current condition” by considering historical inspection records. Similarity matrix construction represents the core piece of the sampling framework, which employs locality‐sensitive hashing algorithm to define the similarity between segments. The stratification process is implemented with spectral clustering, which assigns segments into clusters based on the similarity matrix. The proposed method outperforms simple random sampling, which is widely used in practice, especially under the circumstances where LOM varies greatly across assets. The main highlight of the proposed method is the ability to select sample segments with multiple types of assets that are representative of their respective LOMs of the full inventory, which directly translates into an efficient maintenance activity management. The method is implemented using asset inspection records in the state of Utah from September, 2014 to March 2016. It represents a potentially useful tool for agencies to effectively conduct asset inspection and can be easily adopted for choosing samples containing multiple features.