Prediction of Defect Hotspots for Highway Maintenance Management: A Multi-asset Machine Learning Approach

Search for this publication on Google Scholar
Karimzadeh, A. (2020). Prediction of Defect Hotspots for Highway Maintenance Management: A Multi-asset Machine Learning Approach. Unc Charlotte Electronic Theses And Dissertations.
Download PDF

Analytics

65 views ◎
26 downloads ⇓


Abstract

Given multiple budget and revenue constraints that the transportation sector encounters, predictive analytics enables maintenance agencies to make effective decisions, prioritize maintenance tasks, and provide efficient life-cycle planning. To this end, risk-based predictive models have provided promising results in representing the susceptibility of assets to future defects. Hence, the main objective of this study is to provide an integrated framework for predicting the occurrence probability of multiple defects on different highway asset types. Several gaps in previous models were identified, including limitations in predictive frameworks given the inadequate scope of available inspection data, expert-based selection of contributing factors, and ignoring the interrelationships between neighboring assets. Therefore, this study proposes a risk-based method that combines a risk score generator and a Machine Learning (ML) algorithm to predict the hotspots of multiple defects in a given roadway. To find the best fit, the model is chosen from a pool of ML algorithms containing linear and non-linear methods. To measure the efficiency of the proposed model, its performance is investigated on a selected case study. The proposed framework produced results with significant accuracy within the extent of available data in the case study for calculating risk scores of erosion, obstruction, and cracking on paved ditches given historical weather, traffic, maintenance, and inspection data of five selected neighboring assets (flexible pavements, unpaved ditches, slopes, small pipes and box culverts, and under drain pipes and edge drains). Additionally, the contribution of the considered factors was investigated to further study the importance of individual contributors. The framework offers decision-makers a holistic view of degradation risks of multiple assets, which could enable them to prepare an integrated asset management program. Additionally, a similar framework can be applied to other linear infrastructure systems such as sanitary sewers, water networks, and railroads.

Details
Author

Karimzadeh, Arash

Title

Prediction of Defect Hotspots for Highway Maintenance Management: A Multi-asset Machine Learning Approach

Physical Description

1 online resource (129 pages) : PDF

Date

2020

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Given multiple budget and revenue constraints that the transportation sector encounters, predictive analytics enables maintenance agencies to make effective decisions, prioritize maintenance tasks, and provide efficient life-cycle planning. To this end, risk-based predictive models have provided promising results in representing the susceptibility of assets to future defects. Hence, the main objective of this study is to provide an integrated framework for predicting the occurrence probability of multiple defects on different highway asset types. Several gaps in previous models were identified, including limitations in predictive frameworks given the inadequate scope of available inspection data, expert-based selection of contributing factors, and ignoring the interrelationships between neighboring assets. Therefore, this study proposes a risk-based method that combines a risk score generator and a Machine Learning (ML) algorithm to predict the hotspots of multiple defects in a given roadway. To find the best fit, the model is chosen from a pool of ML algorithms containing linear and non-linear methods. To measure the efficiency of the proposed model, its performance is investigated on a selected case study. The proposed framework produced results with significant accuracy within the extent of available data in the case study for calculating risk scores of erosion, obstruction, and cracking on paved ditches given historical weather, traffic, maintenance, and inspection data of five selected neighboring assets (flexible pavements, unpaved ditches, slopes, small pipes and box culverts, and under drain pipes and edge drains). Additionally, the contribution of the considered factors was investigated to further study the importance of individual contributors. The framework offers decision-makers a holistic view of degradation risks of multiple assets, which could enable them to prepare an integrated asset management program. Additionally, a similar framework can be applied to other linear infrastructure systems such as sanitary sewers, water networks, and railroads.

Genre

doctoral dissertations

Subjects--Topics

Civil engineering
Transportation
Artificial intelligence

Degree

Ph.D.

Keywords

Asset Management
Highway Maintenance
Hotspot
Machine Learning
Prediction Model
Predictive Analytics

Subject Area

Infrastructure & Environmental Systems

Advisor(s)

Shoghli, Omidreza

Committee Members

Tabkhivayghan, Hamed
Pulugurtha, Srinivas
Clinton, Sandra
Mayo, Glenda

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2020.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Karimzadeh_uncc_0694D_12594

Permalink

http://hdl.handle.net/20.500.13093/etd:2573