Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling and Similitude01:12

Modeling and Similitude

378
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
378
Typical Model Studies01:30

Typical Model Studies

487
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
487
Multiple Pipe Systems01:21

Multiple Pipe Systems

928
Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
928
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

383
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
383
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

153
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
153
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

123
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
123

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of incorporation of tricalcium silicate to a universal adhesive on microtensile bond strength to dentin and micromorphological patterns of tooth/restoration interface.

Scientific reports·2026
Same author

A systematic review of climate change impacts on sewer overflow.

Environmental research·2026
Same author

Cross-regional leak detection in water distribution networks through domain transfer.

Water research·2025
Same author

Multi-step time-to-failure predictions in water pipelines using feature engineering and cascading ensembles.

Water research·2025
Same author

Explainable deep learning models for predicting water pipe failures.

Journal of environmental management·2025
Same author

A GPR-based framework for assessing corrosivity of concrete structures using frequency domain approach.

Heliyon·2025

Related Experiment Video

Updated: Oct 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K

Failure modeling of water distribution pipelines using meta-learning algorithms.

Zainab Almheiri1, Mohamed Meguid1, Tarek Zayed2

  • 1Department of Civil Engineering, McGill University, 817 Sherbrooke Street West, Montréal, QC H3A 0C3, Canada.

Water Research
|October 7, 2021
PubMed
Summary

Predicting water pipe failure is crucial for urban infrastructure. A new deep learning model accurately forecasts pipe failure risk, even with limited data, by analyzing factors like chlorine and traffic.

Keywords:
Essential factorsFailureMeta-learningWater pipelines

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K

Related Experiment Videos

Last Updated: Oct 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K

Area of Science:

  • Civil Engineering
  • Environmental Science
  • Data Science

Background:

  • Urbanization and population growth necessitate proactive management of aging water distribution networks.
  • Predicting water pipeline service life is vital for timely replacement, economic efficiency, and ensuring safe drinking water supply.
  • Existing models struggle with insufficient or incomplete data, posing challenges for accurate deterioration assessment.

Purpose of the Study:

  • To introduce an advanced meta-learning paradigm utilizing deep neural networks for predicting water pipe failure risk.
  • To examine the influence of various factors on water pipeline deterioration modeling.
  • To develop a robust model capable of handling limited, high-dimensional, and partially observed data for any water distribution system.

Main Methods:

  • Development of a novel meta-learning framework based on deep neural networks.
  • Analysis of key factors influencing pipe deterioration, including seasonal climate, chlorine content, traffic, pipe material, and spatial characteristics.
  • Application of the model to predict the risk index of pipe failure.

Main Results:

  • The study identified critical factors affecting water pipeline failure, with chlorine residual and traffic lane count being most significant.
  • Other influential factors include road type, spatial characteristics, month, traffic type, precipitation, temperature, number of breaks, and pipe depth.
  • The proposed deep neural network model effectively predicts pipe failure risk across diverse datasets, including those with limited or incomplete information.

Conclusions:

  • The developed meta-learning approach provides an effective solution for predicting water pipe failure risk, even with data limitations.
  • The model's ability to accommodate high-dimensional and partially observed data makes it broadly applicable to various water distribution systems.
  • This research facilitates improved infrastructure management, enabling early replacement and preventing failures in urban water networks.