The consistency of visual sewer inspection data

Abstract

International audienceIn common with most infrastructure systems, sewers are often inspected visually. Currently, the results from these inspections inform decisions for significant investments regarding sewer rehabilitation or replacement. In practice, the quality of the data and its analysis are not questioned although psychological research indicates that, as a consequence of the use of subjective analysis of the collected images, errors are inevitable. This article assesses the quality of the analysis of visual sewer inspection data by analysing data reproducibility; three types of capabilities to subjectively assess data are distinguished: the recognition of defects, the description of defects according to a prescribed coding system and the interpretation of sewer inspection reports. The introduced uncertainty is studied using three types of data: inspector examination results of sewer inspection courses, data gathered in day-to-day practice, and the results of repetitive interpretation of the inspection results. After a thorough analysis of the data it can be concluded that for all cases visual sewer inspection data proved poorly reproducible. For the recognition of defects, it was found that the probability of a false positive is in the order of a few percent, the probability of a false negative is in the order of 25%

Impacts and managerial implications for sewer systems due to recent changes to inputs in domestic wastewater – A review

“…As the impact on solids deposition was found to be more pronounced in the smaller diameter sewers in the upper parts of the sewer networks which are generally hard to access, the classification of separate deposits were in many cases based on subjective judgements from CCTV documentation. Dirksen et al (2013a) have reported that classification of structural deficiencies by CCTV-camera operators were found to be flawed, however, no study has to date assessed the veracity and uncertainty with regard to the reporting of in-sewer deposits.…”

Decision support method to systematically evaluate first-level inspections of the functional status of check dams

“…As shown in Table 5, disagreements can be due to an inspector failing to recognise a defect (FN) and a defect being reported, although there is none (FP). Dirksen et al (2013) indicated for visual inspections in sewage infrastructure that the probability of FN is significantly larger that the probability of FP. In our case, FN was somewhat higher only for parameter A (Damage level).…”

Determination of refurbishment priorities for passes on road network level

“…The cost of the majority of these methods prohibits their regular use in routine bridge condition assessment, executed for all bridge structures within network. Visual inspection is accompanied by condition assessment methodology that translates the identified damages and faults into a quantitative measure; condition rating coefficient, R, is, however, cost effective but less reliable [5,12,13] . The survey of inspection practices on network level, performed for various countries [14], nevertheless shows that worldwide, the majority of road network managing agencies use this method in their daily work.…”