Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

Autor: Laura Inzerillo, G. Giancontieri, Gaetano Di Mino, Ronald Roberts
Přispěvatelé: Roberts R., Giancontieri G., Inzerillo L., Di Mino G.
Rok vydání: 2020
Předmět:
Damage detection
Computer science
0211 other engineering and technologies
02 engineering and technology
lcsh:Technology
lcsh:Chemistry
Transport engineering
Automated detection
Severity assessment
Road networks
021105 building & construction
low-cost technologies
0202 electrical engineering
electronic engineering
information engineering
Settore ICAR/04 - Strade
Ferrovie Ed Aeroporti
General Materials Science
Road pavement distresses
lcsh:QH301-705.5
Instrumentation
Pavement management system
Fluid Flow and Transfer Processes
lcsh:T
business.industry
Process Chemistry and Technology
Deep learning
Low-cost technologie
General Engineering
Pavement management
Urban road
Integrated approach
lcsh:QC1-999
Computer Science Applications
Workflow
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
lcsh:Physics
Pavement condition monitoring
Zdroj: Applied Sciences
Applied Sciences, Vol 10, Iss 1, p 319 (2020)
Volume 10
Issue 1
ISSN: 2076-3417
DOI: 10.3390/app10010319
Popis: Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.
Databáze: OpenAIRE
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