Roadway Asset Inspection Sampling Using High‐Dimensional Clustering and Locality‐Sensitivity Hashing

Abstract

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

Drone scheduling for construction site surveillance

“…Model [P] is challenging to solve because it has complex nonlinear constraints () and () and cannot be solved by off‐the‐shelf solvers (Adeli & Karim, 2014; García‐Nieves, Ponz‐Tienda, Salcedo‐Bernal, & Pellicer, 2018; Jiang & Adeli, 2003; Tang, Liu, Wang, Sun, & Kandil, 2018; Wang, Yan, & Qu, 2019; Xie, Lei, & Ouyang, 2018). One approach is to apply nonlinear optimization methods (Arcaro & Adeli, 2019; Bie, Xiong, Yan, & Qu, 2020; Z. Chen & Liu, 2019; Pu et al., 2019; Qu, Yu, Zhou, Lin, & Wang, 2020; Zavadskas, Antucheviciene, Turskis, & Adeli, 2016; Zavadskas, Antucheviciene, Vilutiene, & Adeli, 2018; Zhang et al., 2019; Zhou, Yu, & Qu, 2020), such as Newton method or quasi‐Newton method (Branam, Arcaro, & Adeli, 2019), but they do not guarantee global optimality. Another approach is to use metaheuristics, such as neural networks (Adeli & Karim, 1997; Rokibul Alam, Siddique, & Adeli, 2019), spider monkey optimization (Akhand, Ayon, Shahriyar, Siddique, & Adeli, 2020), and particle swarm optimization (Hossain, Akhand, Shuvo, Siddique, & Adeli, 2019), but these methods do not even guarantee local optimality.…”

An Advanced Statistical Method for Point Process Modelling With Missing Event Histories

“…Due to the lack of data, physical deterioration models for infrastructure assets are still not common, especially for underground facilities. To solve this issue, researchers have developed various techniques of data collection for infrastructure, e.g., Cha et al [2], Yang et al [3], Li et al [4], Chen and Liu [5], Nayyeri et al [6].…”

Statistical Distance-Based Travel-Time Reliability Measurement for Freeway Bottleneck Identification and Ranking

“…However, as a result of the limited spatial coverage of loop detectors and the quality issues with the data (for example, inconsistency and missing values), the identification results are compromised. To improve the identification results, methods to refine loop detector data and the application of new data sources in bottleneck identification have been studied intensively (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Tang et al (11) proposed a fusing method that identifies bottlenecks based on loop detector data of multiple temporal granularities.…”