The influence of condition assessment uncertainties on sewer deterioration modelling-Reference-Cited by-同舟云学术

The influence of condition assessment uncertainties on sewer deterioration modelling

Published:2019-08-21 Issue:2 Volume:16 Page:287-296
ISSN:1573-2479
Container-title:Structure and Infrastructure Engineering
language:en
Short-container-title:Structure and Infrastructure Engineering

Author:

Caradot Nicolas¹²,Riechel Mathias¹,Rouault Pascale¹,Caradot Antoine³,Lengemann Nic⁴,Eckert Elke⁴,Ringe Alexander⁴,Clemens François⁵⁶,Cherqui Frédéric²

Affiliation:

1. Kompetenzzentrum Wasser Berlin, Berlin, Germany;

2. DEEP, INSA-LYON, Université Claude Bernard Lyon 1, Villeurbanne, France;

3. Institut Camille Jordan, Université Claude Bernard Lyon 1, Villeurbanne, France;

4. Berliner Wasserbetriebe, Berlin, Germany;

5. Section Sanitary Engineering, Faculty of Civil Engineering and Geosciences, University of Technology, Delft, The Netherlands;

6. Department of Hydraulic Engineering, Deltares, Delft, The Netherlands

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Ocean Engineering,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15732479.2019.1653938

Reference51 articles.

1. Influence of available data on sewer inspection program efficiency

2. Benefits of using basic, imprecise or uncertain data for elaborating sewer inspection programmes

3. Sewer asset management: impact of sample size and its characteristics on the calibration outcomes of a decision-making multivariate model

4. Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods

5. The management of sediment in combined sewers

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid knowledge and data driven approach for prioritizing sewer sediment cleaning;Automation in Construction;2024-09

2. Edge-based graph centrality measures with spatial analytics to support vulnerability assessment and maintenance planning in sewer networks;Journal of Hydroinformatics;2024-07-31

3. Wastewater network assets' lifespan prediction: can we modify the machine learning algorithms to fit the purpose?;2024

4. A parsimonious approach to predict regions affected by sewer-borne contaminants in urban aquifers;Environmental Monitoring and Assessment;2023-11-23

5. Predicting sewer structural condition using hybrid machine learning algorithms;Urban Water Journal;2023-05-30