Author
J. Dirksen
Bio: J. Dirksen is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Settlement (structural) & Sanitary sewer. The author has an hindex of 8, co-authored 15 publications receiving 248 citations.
Papers
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TL;DR: In this article, the authors assess 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.
Abstract: In 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%.
120 citations
TL;DR: A case study was performed on the modeling of the condition aspect 'surface damage by corrosion or mechanical action' using a Markov model and discusses the problems encountered.
35 citations
TL;DR: Using data from the Eindhoven wastewater system in The Netherlands both dry weather flow and wet weather flow situations have been studied, finding the smallest catchment area consistently shows the largest mean peak factors and vice versa.
24 citations
TL;DR: In this article, the influence of the coding system on quality of inspection data is studied, and it is concluded that the increase in detail does not lead to more information, and that added detail leads to more mistakes.
Abstract: In order to provide information about the decisions on proactive and reactive maintenance, sewers are visually inspected. Previous research showed that the quality of visual inspection data is questionable. A coding system prescribes which and how defects should be recorded. This article studies the influence of the coding system on quality of inspection data. A database with the examinations of the Dutch sewer inspector course is studied. Through time, 10 photos of the inside of a sewer were evaluated according to two different coding systems: the concise NEN3399:1992 and the more detailed and extensive NEN3399:2004.This article compares both coding systems by evaluating candidate responses to photos showing sewers with clearly visible defects. Results show that added detail in the coding system of 2004 leads to more mistakes. Therefore, it can be concluded that the increase in detail does not lead to more information.
24 citations
TL;DR: Sediment bed levels exhibit no residual spatial correlation, indicating that the vulnerability to a blockage is reduced as adjacent gully pots provide a form of redundancy, and the findings may aid to improve maintenance strategies in order to safeguard the performance of gull pots.
16 citations
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TL;DR: This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements.
652 citations
TL;DR: A framework that uses deep convoluted neural networks (CNNs) to classify multiple defects in sewer CCTV images to improve the speed, accuracy, and consistency of sewer defect reporting is presented.
174 citations
TL;DR: The results show that microbial transformation of pharmaceuticals begins in sewer, albeit to different extents for different compounds, and should be assessed especially when their concentrations are used to estimate and refine the estimation of their per capita consumption in a catchment of interest in the sewage epidemiology approach.
121 citations
TL;DR: In this article, the authors assess 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.
Abstract: In 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%.
120 citations
TL;DR: Due to the difficulty to discriminate the defects, the low-level defect classification accuracy still needs improvements, but the proposed network with hierarchical classification also demonstrated superior performance over traditional approaches.
110 citations