Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion.

Construction Materials Corrosion Hydrogen-Ion Concentration Neural Networks, Computer Temperature
Adaptive neuro fuzzy inference system Artificial neural network Data-driven models Service life Sewer corrosion

Journal

Journal of environmental management
ISSN: 1095-8630
Titre abrégé: J Environ Manage
Pays: England
ID NLM: 0401664

Informations de publication

Date de publication:
15 Mar 2019
Historique:
received: 11 09 2018
revised: 12 12 2018
accepted: 26 12 2018
pubmed: 15 1 2019
medline: 26 9 2019
entrez: 15 1 2019
Statut: ppublish

Résumé

Concrete corrosion is one of the most significant failure mechanisms of sewer pipes, and can reduce the sewer service life significantly. To facilitate the management and maintenance of sewers, it is essential to obtain reliable prediction of the expected service life of sewers, especially if that is based on limited environmental conditions. Recently, a long-term study was performed to identify the controlling factors of concrete sewer corrosion using well-controlled laboratory-scale corrosion chambers to vary levels of H

Identifiants

DOI: 10.1016/j.jenvman.2018.12.098 PMID: 30640168
pubmed: 30640168
pii: S0301-4797(18)31520-2
doi: 10.1016/j.jenvman.2018.12.098
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

431-439

Informations de copyright

Copyright © 2018 Elsevier Ltd. All rights reserved.

Auteurs

Xuan Li (X)

Advanced Water Management Centre, The University of Queensland, Australia. Electronic address: xuan.li@awmc.uq.edu.au.

Faezehossadat Khademi (F)

Civil, Architectural and Environmental Engineering Department, Illinois Institute of Technology, USA. Electronic address: faezehossadat_khademi@yahoo.com.

Yiqi Liu (Y)

School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China. Electronic address: aulyq@scut.edu.cn.

Mahmoud Akbari (M)

Civil Engineering Department, University of Kashan, Kashan, Iran. Electronic address: makbari@kashanu.ac.ir.

Chengduan Wang (C)

Department of Chemistry and Chemical Engineering, Sichuan University of Arts and Science, Sichuan, China. Electronic address: wcd@suse.edu.cn.

Philip L Bond (PL)

Advanced Water Management Centre, The University of Queensland, Australia. Electronic address: p.bond@uq.edu.au.

Jurg Keller (J)

Advanced Water Management Centre, The University of Queensland, Australia. Electronic address: j.keller@uq.edu.au.

Guangming Jiang (G)

Advanced Water Management Centre, The University of Queensland, Australia; School of Civil, Mining and Environmental Engineering, University of Wollongong, Wollongong, NSW 2522, Australia. Electronic address: g.jiang@awmc.uq.edu.au.

Articles similaires

Initial pH determines the morphological characteristics and secondary metabolite production in Aspergillus terreus and Streptomyces rimosus cocultures.

Tomasz Boruta, Martyna Foryś, Weronika Pawlikowska et al.
1.00
Aspergillus Hydrogen-Ion Concentration Coculture Techniques Secondary Metabolism Streptomyces rimosus

Unsupervised learning for real-time and continuous gait phase detection.

Dollaporn Anopas, Yodchanan Wongsawat, Jetsada Arnin
1.00
Humans Gait Neural Networks, Computer Unsupervised Machine Learning Walking

Detection, classification, and characterization of proximal humerus fractures on plain radiographs.

Reinier W A Spek, William J Smith, Marat Sverdlov et al.
1.00
Humans Shoulder Fractures Tomography, X-Ray Computed Neural Networks, Computer Female

Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis.

Dinah V Parums
1.00
Humans Artificial Intelligence Neoplasms Prognosis Image Processing, Computer-Assisted

Classifications MeSH

questionsmedicales.fr › Technologie, industrie et agriculture › Produits manufacturés › Matériaux de construction › Evaluation of data-driven models for...
questionsmedicales.fr › Phénomènes chimiques › Corrosion › Evaluation of data-driven models for...
questionsmedicales.fr › Phénomènes chimiques › Concentration en ions d'hydrogène › Evaluation of data-driven models for...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Evaluation of data-driven models for...
questionsmedicales.fr › Environnement et santé publique › Environnement › Concepts météorologiques › Atmosphère › Temps (météorologie) › Température › Evaluation of data-driven models for...