Framework for Automatic Condition Assessment of Culverts

Abstract

A framework for real-time and automated monitoring of the condition of culverts based on identifying internal defects via video inspection is developed. Manual inspection using closed-circuit television has several drawbacks, such as inconsistency and subjectivity due to human evaluation. Analysis of digital video, consisting of thousands of megabytes even at lower resolutions, can be laborious and not suited for real-time implementation. An innovative approach is to extract image frames judiciously from the video and analyze frames to locate and categorize major defects. Rather than analyze all extracted frames, one can skip consecutive frames at a minimal loss of accuracy and bring considerable savings in memory and system requirements. Each frame is preprocessed to enhance contrast, through use of an adaptive scheme, and to reduce dimensionality in pixel-space by implemention of region-based processing. Preprocessing is followed by a two-step image segmentation process, which implements a background elimination procedure in the first step and shape detection in the second step. Fuzzy clustering is used as the underlying segmentation model. Defect shape and depth information after post-processing are used as input to an assessment methodology for automated condition state. A simple formulation based on both the damage area and depth is then used to assess the condition of culverts based on a four-point condition assessment scale. The proposed framework is demonstrated with a test example. Future research would entail consolidating the concept by extensive testing and integration for real-time application.

Integrated Transportation and Land Use Scenario Modeling by Visual Evaluation of Examples

“…The inputs (i.e., the design parameters contained in the design vocabulary) and the results (i.e., public preferences) are coded into fuzzy sets and processed through the Fuzzy Knowledge Builder software. Fuzzy setbased modeling has a long history of application in mechanical and electrical control systems (23) and has recently begun to find application in transportation research (24)(25)(26)(27). Fuzzy set modeling is most useful when it is necessary to characterize a complex system with only partial knowledge, where the sample size is quite small compared with a range of possible solutions.…”

Use of Context-Sensitive Methods to Influence Operating Speeds

“…Fuzzy set-based modeling has a long history of application in mechanical and electrical control systems (13). In recent years, fuzzy set-based modeling has been applied to a wide range of perception questions in the transportation field, including the performance of variable message highway signs ( 14), infrastructure condition assessment (15), vehicle classification from remotely sensed data (16), and car-truck interactions (17 ). These problem domains are similar, in that it is necessary to characterize a complex system under conditions of high uncertainty or partial knowledge; for example, with a relatively small sample size compared with the total size of the problem domain.…”

The Citizen Engineer: Urban Infrastructure Monitoring via Crowd-Sourced Data Analytics

“…In order to train the model, condition ratings from existing evaluation metrics together with expert solicitations can be used. Extensive literature exists on different machine learning models used in infrastructure condition assessment and the reader is referred to them for further reading (Meegoda et al 2006, Saitta et al 2009, Amiri et al 2015, Harris et al 2015, Farrar and Worden 2012, Alipour et al 2016.…”