Artificial Neural Networks and Adaptive Neuro-Fuzzy Models to Predict Remaining Useful Life of Water Pipelines

Tavakoli, Razieh; Sharifara, Ali; Najafi, Mohammad

doi:10.1061/9780784482988.019

Cited by 18 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Winter can cause water to freeze in some locations, which contributes to damage and cracked pipes (Barton et al , 2019). Further, rain can cause the pipes of the hot-water system to corrode and cause leaking and contamination of the water (Tavakoli, 2018). A polar climate is prevalent in regions that are above 70 degrees latitude in the Northern Hemisphere and below 65 degrees latitude in the Southern Hemisphere (UNDP, 2023).…”

Section: Resultsmentioning

confidence: 99%

Plumbing defects in residential buildings: analysis of anomalies and their causes

Gurmu

Mudiyanselage

2023

View full text Add to dashboard Cite

Purpose Most residential building owners often report problems associated with the plumbing systems. If identified at the early stages, plumbing-related defects can be easily repaired. However, if unnoticed for a long period of time, they could lead to major damages and incur a significant cost to repair. Despite the problems, studies investigating plumbing anomalies and their root causes in residential buildings are limited. This study aims to explore plumbing defects and their potential causes, diagnosis methods and repair techniques in residential buildings. Design/methodology/approach This research used data collected through an extensive survey of both academic and grey literature. Through the content analysis, plumbing defects and the associated causes have been identified and presented in tabular format. Findings The study investigated the anomalies and causes in the residential plumbing system under five key sub-systems: water supply system; sanitary plumbing system; roof drainage system; heating, ventilation, air conditioning and gas system; and swimming pool. Accordingly, some of the identified plumbing defects include leakages, corrosion, water penetration, slow drainage and cracks. Damaged pipes, faulty equipment and installations are some of the common causes of the anomalies. Visual inspection, hydrostatic pressure test, thermography, high-tech pipe cameras, infrared cameras, leak noise correlators and leak loggers are techniques used for diagnosing anomalies. Reactive, preventive, predictive and reliability-centred maintenance strategies are identified to control or prevent anomalies. Originality/value The findings of this research can be used as a useful tool or guideline for contractors, plumbers, facilities managers and building surveyors to identify and rectify plumbing system-related defects in residential buildings.

show abstract

Section: Resultsmentioning

confidence: 99%

Plumbing defects in residential buildings: analysis of anomalies and their causes

Gurmu

Mudiyanselage

2023

View full text Add to dashboard Cite

show abstract

“…The experimental data related to a WDN in Montreal, Canada, was used in this study. The data was extracted from the study by Tavakoli (Tavakoli, 2018). The size of the network is 5338.6km, consisting of pipes made with CI, DI, asbestos (AC), and steel.…”

Section: Data Collectionmentioning

confidence: 99%

Predicting Wall Thickness Loss in Water Pipes Using Machine Learning Techniques

Taiwo,

Seghier,

Zayed

2023

ce papers

View full text Add to dashboard Cite

Wall thickness loss in water pipes has been found to be positively correlated with water pipe failure. The reliability of water pipes reduces as their wall thickness loss increases. Although previous studies have investigated pipe failure modeling using historical failure data, however, indirect failure modeling via wall thickness loss is yet to be explored. Hence, this study develops machine learning (ML) models to predict wall thickness loss in water pipes. Random Forest (RF) and Gradient Boosting Machine (GBM) are chosen as the base models and are integrated with Bayesian Optimization (BO) algorithm for hyperparameters selection. The predictive models are evaluated using root mean square error (RMSE), mean absolute error (MEA), mean absolute percentage error (MAPE), and coefficient of determination (R2). Based on the evaluation metrics, the hybrid models (i.e., RF+ BO and GBM+BO) outperformed the base models (RF and GBM), showing the importance of the systematic selection of hyperparameters. The best model (RF + BO) achieved an RMSE, MAE, MAPE, and R2 value of 3.212, 2.494, 11.506, and 0.910, respectively. These metrics show the high predictive capability of the model, which can be used by water infrastructure management to predict wall thickness loss in water pipes.

show abstract

“…Machine learning was also successfully used for identification of the significant factors that impact the prediction of remaining useful life of water pipelines. In [34] Artificial Neural Networks and Adaptive Neuro-Fuzzy Models were applied to predict remaining useful life of water pipelines. The presented approach could be also adjusted to be useful for other types of pipelines, e.g., gas pipelines.…”

Section: Review Of Literature and Limitations Of The Previous Workmentioning

confidence: 99%

Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning

Krechowicz

2021

Energies

View full text Add to dashboard Cite

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions.

show abstract

Artificial Neural Networks and Adaptive Neuro-Fuzzy Models to Predict Remaining Useful Life of Water Pipelines

Cited by 18 publications

References 17 publications

Plumbing defects in residential buildings: analysis of anomalies and their causes

Plumbing defects in residential buildings: analysis of anomalies and their causes

Predicting Wall Thickness Loss in Water Pipes Using Machine Learning Techniques

Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning

Contact Info

Product

Resources

About