Sustainability and Resilience of Water Sector Pipeline Infrastructure Systems

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 2399

Special Issue Editor


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Guest Editor
College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Interests: pipeline, asset management; data analytics; drinking water pipeline; wastewater pipeline; decision support system; artificial intelligence

Special Issue Information

Dear Colleagues,

As a society, we are critically dependent upon miles of pipeline (lifeline) infrastructure systems to transport water and collect wastewater. Across the world, countries install approximately 500,000 miles of pipelines annually with a market value of over 50 billion dollars. Pipelines cross our communities near our homes and schools, yet little attention is paid to this critical infrastructure until disaster strikes. Approaching pipeline system installation, operation, and retrofitting by continuing to use the same 20th-century processes, practices, technologies, and materials will likely yield the same results: increasing instances of service disruptions, higher operating and repair costs, and the possibility of catastrophic, cascading failures. Our pipeline infrastructure systems were created in an era of inexpensive fossil fuel, a stable climate, and a growing economy. Unfortunately, the pipeline infrastructure is aging and already operating outside its design limits. How a nation operates, retrofits, and expands its pipeline infrastructure will help determine the quality of life for future generations and that nation’s competitiveness in the global economy. If a nation is to meet the important challenges of the 21st century, a new paradigm for the building and retrofitting of critical water pipeline infrastructure systems is required, one that addresses the conflicting goals of diverse economic, environmental, societal, and policy interests.

This Special Issue invites the submission of original research papers or review papers covering the latest findings and progress in the water sector (drinking water, wastewater, stormwater) pipeline infrastructrue systems. We are keen to receive contributions related to a range of topics, including performance-based pipeline design, pipeline lifecycle data collection, modeling and advanced analytics (including artificial intelligence application), decision support systems for pipeline renewal planning, risk and resiliency management during normal and extreme conditions, applications of artificial intelligence, and other related applications and results. Contributions related to next-generation pipeline infrastructure systems, advanced sensor networks, leak detection, and new renewal engineering technologies will be also welcomed.

By sharing knowledge and advancing the state of the art in water pipeline infrastructure systems, contributors can help ensure that these critical systems remain efficient, reliable, and sustainable for generations to come. We look forward to receiving your submissions and contributing to this important field of study.

Prof. Dr. Sunil Sinha
Guest Editor

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Keywords

  • pipeline, asset management
  • data analytics
  • drinking water pipeline
  • wastewater pipeline
  • decision support system
  • artificial intelligence

Published Papers (2 papers)

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Research

18 pages, 5925 KiB  
Article
Failure Prevention in Large-Diameter Water Pipelines Using Reliability-Centered Maintenance
by James Geisbush and Samuel T. Ariaratnam
Water 2023, 15(24), 4283; https://doi.org/10.3390/w15244283 - 15 Dec 2023
Viewed by 977
Abstract
The consequences of failures from large-diameter water pipelines can be severe. Results can include significant property damage, adjacent damage to infrastructure such as roads and bridges resulting in transportation delays or shutdowns, adjacent structural damage to buildings resulting in loss of business, service [...] Read more.
The consequences of failures from large-diameter water pipelines can be severe. Results can include significant property damage, adjacent damage to infrastructure such as roads and bridges resulting in transportation delays or shutdowns, adjacent structural damage to buildings resulting in loss of business, service disruption to a significant number of customers, loss of water, costly emergency repairs, and even loss of life. The Washington Suburban Sanitary Commission (WSSC) in the United States found that flooding was the greatest concern due to its potential duration, the potential for broad geographic impact, and its role in crater creation. Public safety, property damage, social and economic consequences, and loss of water service and for how long is also of paramount concern. The American Water Works Association’s (AWWA) 2020 “State of the Water Industry” report states that the top issue facing the water industry since 2016 is aging infrastructure, with the second being financing for improvements. The industry needs to find novel ways of extending asset life and reducing maintenance expenditures. While there are many different assets that comprise the water/wastewater industry, pipelines are a major component and are often neglected because they are typically buried. Reliability-Centered Maintenance (RCM) is a process used to determine the most effective maintenance strategy for an asset, with the ultimate goal being to establish the required function of the asset considering the required reliability and availability at the lowest cost. The RCM philosophy considers Preventive Maintenance, Predictive Maintenance, Condition Based Monitoring, Reactive Maintenance, and Proactive Maintenance techniques in an integrated manner to increase the probability an asset will perform its designed function throughout its design life with minimal maintenance. RCM requires maintenance decisions be based on maintenance requirements supported by sound technical and economic justification. However, one industry where principles of RCM are in its infancy is the water/wastewater industry. This paper provides a case example and numeric modeling for use in RCM analyses for developing maintenance strategies for large-diameter water pipelines, particularly prestressed concrete pipelines, and proposes an approach for determining the most effective and efficient maintenance activities for large-diameter prestressed concrete water pipelines. The case study discussed in this paper analyzed wire breaks over time to predict when certain thresholds would be reached. The intent of this study is to predict when a specified threshold will be reached. From the RCM, a threshold was set to begin planning, budgeting, and scheduling maintenance activities when 55% of the wires in a frame or two adjoining frames are distressed or when 65% of the wires in non-adjacent frames are distressed. The results from the numeric model predict the 55% threshold may be reached in August 2025 for the most distressed pipe segment. Full article
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15 pages, 2613 KiB  
Article
Asset Management of Wastewater Interceptors Adjacent to Bodies of Water
by Mohammad Damen Bani Fawwaz, Mohammad Najafi and Vinayak Kaushal
Water 2023, 15(23), 4176; https://doi.org/10.3390/w15234176 - 02 Dec 2023
Viewed by 891
Abstract
Pipeline asset management derives from pipelines’ physical conditions, condition rating, and serviceability through investigating, monitoring, and analyzing the rupture history. The remaining asset life and structural condition of the pipeline network running near and under bodies of water are often hard to predict. [...] Read more.
Pipeline asset management derives from pipelines’ physical conditions, condition rating, and serviceability through investigating, monitoring, and analyzing the rupture history. The remaining asset life and structural condition of the pipeline network running near and under bodies of water are often hard to predict. In case of a pipeline failure, major damage may occur to the surrounding environment, adding up to disruptions in service and repair costs. This paper develops multinomial logistic regression (MLR) and binary logistic regression models to predict how the bodies of water could affect the soil surrounding wastewater interceptors. The models were developed based on data from the City of Fort Worth, Texas. This study concludes that the pipe diameter, pipe age, location of the pipeline with reference to bodies of water (far or near), and the pipe material are the most significant variables that affect the surrounding conditions and remaining life of wastewater interceptors. In future, a clearer perception through increased software development and machine learning for managing pipeline asset management would provide impacts on different parameters on pipelines’ expected life. Full article
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