Statistical Inference of Sewer Pipe Deterioration Using Bayesian Geoadditive Regression Model-Reference-Cited by-同舟云学术

Statistical Inference of Sewer Pipe Deterioration Using Bayesian Geoadditive Regression Model

Published:2019-09 Issue:3 Volume:25 Page:04019021
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Balekelayi Ngandu¹^ORCID,Tesfamariam Solomon²

Affiliation:

1. Ph.D. Candidate, School of Engineering, Univ. of British Columbia, 1137 Alumni Ave., Kelowna, BC, Canada V1V 1V7. ORCID: .

2. Professor, School of Engineering, Univ. of British Columbia, 1137 Alumni Ave., Kelowna, BC, Canada V1V 1V7 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IS.1943-555X.0000500

Reference48 articles.

1. Abraham D. M. and R. Wirahadikusumah. 1999. “Development of prediction models for sewer deterioration.” In Proc. 8th Conf. on Durability of Building Materials and Components edited by M. A. Lacasse and D. J. Vanier. Ottawa: National Research Council.

2. An investigation of the factors influencing sewer structural deterioration

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

4. Assessment of Infrastructure Inspection Needs Using Logistic Models

5. Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems

Cited by 31 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. Global perspectives on groundwater infiltration to sewer networks: A threat to urban sustainability;Water Research;2024-09

2. Advancements in digital twin modeling for underground spaces and lightweight geometric modeling technologies;Automation in Construction;2024-09

3. Data augmentation using conditional generative adversarial network (cGAN): applications for sewer condition classification and testing using different machine learning techniques;Journal of Hydroinformatics;2024-06-13

4. A state-of-the-art review for the prediction of overflow in urban sewer systems;Journal of Cleaner Production;2024-01

5. Assessment of the suitability of degradation models for the planning of CCTV inspections of sewer pipes;Urban Water Journal;2023-11-20