Event Title

Data-Driven Risk-Informed Bridge Asset Management and Prioritization across Transportation Networks

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Description

This project will take a data-driven approach, building deep learning neural network models to detect and characterize bridge deterioration. Based on the observed bridge conditions, outcomes will be defined, serving as the dataset outputs. Deep learning algorithms will then be employed to build models to translate the collected imagery data into classification and characterization of bridge condition and deterioration. To do this, we will undertake four main tasks: (1) Define the dataset outputs based on observed bridge conditions across the set of bridges analyzed; (2) Build learning models to use collected imagery data to classify multiple types of deterioration; (3) Build learning models to use collected imagery data to characterize levels of deterioration across deterioration types; (4) Evaluate generalizability of the models to translate collected imagery data into assessments of bridge deterioration. Outcomes will enable the comparison of assessments across multiple bridges and facilitate the prioritization of resources for maintenance, repair, and rehabilitation decisions across a transportation network.

Presentation Date

10 Aug 2020, 4:00 pm - 4:30 pm

Meeting Name

INSPIRE-UTC 2021 Annual Meeting

Department(s)

Civil, Architectural and Environmental Engineering

Document Type

Presentation

Document Version

Final Version

File Type

text

Language(s)

English

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Aug 10th, 4:00 PM Aug 10th, 4:30 PM

Data-Driven Risk-Informed Bridge Asset Management and Prioritization across Transportation Networks

This project will take a data-driven approach, building deep learning neural network models to detect and characterize bridge deterioration. Based on the observed bridge conditions, outcomes will be defined, serving as the dataset outputs. Deep learning algorithms will then be employed to build models to translate the collected imagery data into classification and characterization of bridge condition and deterioration. To do this, we will undertake four main tasks: (1) Define the dataset outputs based on observed bridge conditions across the set of bridges analyzed; (2) Build learning models to use collected imagery data to classify multiple types of deterioration; (3) Build learning models to use collected imagery data to characterize levels of deterioration across deterioration types; (4) Evaluate generalizability of the models to translate collected imagery data into assessments of bridge deterioration. Outcomes will enable the comparison of assessments across multiple bridges and facilitate the prioritization of resources for maintenance, repair, and rehabilitation decisions across a transportation network.