Automated Assessment of Defects in Bridge Structures

Shafei, Behrouz; Odeh, Ibrahim

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Automated Assessment of Defects in Bridge Structures

2025-04-01
By Shafei, Behrouz ; Odeh, Ibrahim

File Language:

English

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Creators:

Shafei, Behrouz ; Odeh, Ibrahim
Corporate Creators:

Iowa State University. Bridge Engineering Center
Corporate Contributors:

Iowa State University. Institute for Transportation (InTrans). ; Iowa. Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:

Artificial Intelligence Bridge Management Systems Condition Surveys Drones Inspection Machine Learning Neural Networks Structural Deterioration And Defects
Publication/ Report Number:

InTrans Project 22-798
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Iowa State University. Institute for Transportation (InTrans).
Abstract:

Effective and timely bridge inspections are crucial for extending bridge lifespans and preventing catastrophic failures. Traditional inspection methods often involve manual visual assessments and can be time-consuming, labor-intensive, and prone to human error. Recent technological advancements in unmanned aerial vehicles (UAVs), artificial intelligence (AI), and machine learning (ML) offer promising solutions to these challenges. When high-quality images captured by UAVs are analyzed using AI and ML algorithms, structural defects can be detected and quantified with greater precision and efficiency than manual inspections.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:54f5c2d55e19c3d25f5c2dae5304693bfc119945d483f0db07f82c3a8c2baab0a8951ee93be5f8a070a592d280d775ed82bf85013e8c8df46b137cd1cd80d7d9
Download URL:

https://rosap.ntl.bts.gov/view/dot/85148/dot_85148_DS1.pdf
File Type:

[PDF - 10.98 MB ]

File Language:

English

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