August 2025 Vol. 80 No. 8
Features
Promoting research, development and training in underground infrastructure construction, renewal technologies
The Center for Underground Infrastructure Research and Education (CUIRE) at the University of Texas at Arlington (UTA) has been actively involved in advancing technologies such as trenchless rehabilitation methods, underground infrastructure renewal and supporting government and industry in innovation within this field.
CUIRE has been assisting the Trenchless Technology & Pipe Conference (TTP) for the Regional South- Central Chapter of NASTT (SC-NASTT) in organizing an annual conference. CUIRE also plays a significant role in the Underground Infrastructure Construction (UIC) conference by organizing CUIRE schools every year.
CUIRE is involved in groundbreaking research, with over 16 PhD students actively contributing to its projects. Recent research endeavors include the use of trenchless technologies for comprehensive asset management of culverts and drainage structures, real-time monitoring systems for water assets, and the development of innovative pipeline renewal methods. In addition, research efforts include the development of polymeric spray- applied pipe linings (SAPLs) applications for high-pressure pipelines, gravity pipes, and culvert systems; product evaluations for underground stormwater storage, infiltration chambers, and modules under highway truck loading; and leading a nationwide study to assess innovative technologies for monitoring water assets.
In addition, CUIRE is involved in air monitoring of the Cured-in-Place-Pipe (CIPP) installation, which was a significant study necessary for our people to feel safe with new technologies. By driving innovation and providing practical applications, these projects are helping to address critical infrastructure challenges in communities nationwide.
In 2024, Dr. Mohammad Najafi, director of CUIRE at The University of Texas at Arlington, has been honored with the Stephen D. Bechtel Pipeline Engineering Award at the UESI Pipelines 2024 Conference held in Calgary, Canada. This highly respected award, presented by the Utility Engineering and Surveying Institute (UESI) of ASCE, recognizes Najafi’s exceptional contributions to pipeline engineering, research and education.
Najafi is a nationally and internationally recognized expert in trenchless technology and underground infrastructure. With over $3 million in competitive research grants secured at UT Arlington, he has led numerous projects focused on developing pipeline design guidelines, asset management strategies, and innovative rehabilitation techniques. His research has been widely published and cited, shaping practices in both academia and industry.
He is the founding chief editor of the ASCE Journal of Pipeline Systems – Engineering and Practice, current chair of the AWWA Pipeline Rehabilitation Standard Committee, and a long-standing member of several ASCE and ASTM technical committees. In 2013, he chaired the ASCE International Pipelines Conference in Fort Worth, which brought together more than 800 pipeline professionals from around the world.
Advancement in SAPL: Full-Scale Short-Term Hole Spanning Testing
A short-term hole-spanning test was performed at CUIRE for a 30-inch diameter host pipe with hole-spanning sizes of 4 and 6 inches, and the length of these pipe samples was four feet. The six-foot spigot ends were used at both ends of the pipe samples to create a fully enclosed assembly with a total length of 15 feet. The liner thickness for the pipe sample with four- and 6-inch holes was designed using a hole-spanning design equation provided in the AWWA Structural Classification of Lining Systems (2019).
In addition to the experimental study, Finite Element Modeling (FEM) was utilized to simulate and analyze the behavior of the SAPL linear under hydrostatic pressure in a hole spanning corroded scenarios. A three-dimensional (3D) visual of the FE model was created to show strain distributions, deformation patterns, and stress concentration zones at the hole spanning liner system. This visualization helps identify the areas most affected by the pressure.
The FEM results confirmed the experimental findings, showing that the liner can withstand hydrostatic pressures of 500 psi without cracking or failure. The combination of experimental and FEM data offers a solid foundation for improving design standards and increasing the reliability of SAPL systems under high-pressure conditions.
Full-scale external buckling testing
Additionally, evaluating the buckling effects caused by groundwater hydrostatic pressure on polymeric SAPL liners due to water within the annular space between the liner and the host pipe is an important criterion for the AWWA Class III structural classification of the lining system. Figure 4 shows the external buckling test setup.
FEM was an important part of the external hydrostatic buckling test study. It performed as a strong tool to validate experimental results and provide additional insights into the buckling behavior of SAPL under external hydrostatic pressure. The integration of FEM allowed for detailed analysis of material behavior, geometric imperfections, and structural failure modes that are challenging to capture through experimental testing alone.
Three- dimensional (3D) FEM was employed using ABAQUS software to simulate and analyze the critical buckling pressures and deformation patterns of SAPLs. Figure 5 shows the different buckling modes of the liner pipe sample under external hydrostatic pressure.
ABOUT THE AUTHORS
Dr. Kawalpreet Kaur
Research Associate
Dr.Kawalpreet Kaur, Ph.D., is a postdoctoral research associate with a research interest in trenchless technology, pipeline rehabilitation, and asset management. Her current research focuses on designing, testing, and evaluating spray-applied pipe linings (SAPL) and underground stormwater storage infrastructure materials. She conducts various experimental tests to assess their suitability for field applications.
Ehsan Rajaie
PhD Student
exr2899@mavs.uta.edu
Ehsan Rajaie, a Ph.D. candidate at the University of Texas at Arlington and infrastructure engineering assistant at Tarrant Regional Water District, has received the prestigious 2024 AWWA Scholarship, selected from over 800 applicants across the US and Canada. He is also the recipient of the 2025 NASTT Argent Memorial Award and the Outstanding Ph.D. Student Award in 2023 and 2025 from UTA’s Civil Engineering Department.
Three Edge Bearing Test for SAPL Liner for Application in Culverts and Gravity Pipes
This project focuses on evaluating the structural performance of polymer-lined reinforced concrete (RC) pipes using the Three-Edge Bearing Test method as outlined in ASTM C497 – 2020. The test assesses how different lining thicknesses of the polymer lining system affect the RC pipes’ load- bearing capacity and crack resistance under applied vertical loads. This research supports collaborative efforts between CUIRE, the City of Dallas, PPG Protective & Marine Coatings, and Thompson Pipe Group.
The test objectives were: 1) To determine whether the polymeric lining enhances structural performance, specifically the pipe’s resistance to initial cracking and its ultimate load capacity. 2) To provide guidance on material selection and lining thickness decisions for future infrastructure applications involving reinforced concrete pipelines.
A total of seven, 36-inch diameter RC pipes, each 8 feet in length, were used for testing. One pipe serves as a control pipe sample, while the remaining six were internally lined with a protective polymeric Spray Applied Pipe Lining (SAPL) at three different thicknesses: 0.25 in., 0.5 in., and 1 in. (two pipes per SAPL thickness). Figure 6 represents a polymeric-lined RC pipe. The lining system was applied using a spin-cast machine to achieve the required thickness. During the test, different sets of data, such as load applied with the three-edge bearing test machine, deflection at the crown of the lined pipe, and strain data at different critical points of the lined pipe, were collected and analyzed to determine the structural performance of the lined pipes. Figure 7 illustrates the instrumentation setup in a polymeric-lined pipe. Figure 8 represents the pipe samples under loading conditions.
The results demonstrated that the polymer lining significantly enhances structural performance, particularly the pipe’s resistance to initial cracking and its ultimate load- bearing capacity. The ultimate load capacity of the pipe sample with a liner system was increased at varied thicknesses of the lining system provided. Therefore, the SAPL lining installation increases the load capacity of the RC pipe samples. These findings not only validate the improvement in the structural capacity of RC pipes lined with a polymeric system but also provide valuable data to inform future improvements and innovations in the SAPL rehabilitation process for underground infrastructure and pipelines.
Authors: Dr. Mo Najafi P.E., F. ASCE, BC.PLW, Kawalpreet Kaur, Ph.D., Ehsan Rajaie S.M. ASCE, and Paria Hamidzadeh S.M. ASCE
New CIPP Solutions Reduce Environmental Impacts
Many resins used in CIPP contain styrene, a volatile organic compound (VOC) that can evaporate into the air during the tube installation and especially during the curing process. While low levels may not be harmful, high exposure can affect workers and even people nearby. That’s why it’s crucial to find better ways to manage or even prevent these emissions.
Communities are right to ask:
- Is there a cleaner way to fix our pipes?
- What makes low-emission liners different?
In this category, EnviroCure is a special liner made by United Felts. What makes it unique is its outer polymer coating. This layer acts like a shield, preventing styrene from escaping into the air while the tube is being installed and cured at high temperatures. The EnviroCure liner is installed just like traditional liners, allowing contractors to use the same CIPP equipment and process with no additional training or equipment required.
To evaluate this innovative liner, a comprehensive air quality monitoring plan was implemented, incorporating both baseline and operational air sampling. Real-time monitoring was conducted using Photoionization Detectors (PIDs), while average concentrations of VOCs were measured using summa canisters and sorbent tubes.
The highest real-time monitoring result was just 0.99 parts per million (ppm). Styrene levels in the air samples, as shown in Table 2, stayed way below the safety limits set by the EPA, NIOSH and OSHA. This means a new type of liner with an extra barrier coating system that effectively traps styrene inside the tube, protecting both the public and the workers.
Authors: Parisa Beigvand, S.M. ASCE, Salar Bavili Nezhad, Ph.D., A.M.ASCE, Sevda Jannatdoust, S.M. ASCE, and Dr. Mo Najafi P.E., F. ASCE, BC.PLW
ABOUT THE AUTHORS
Parisa Beigvand PhD Student
Parisa is a Ph.D. candidate actively involved in research projects funded by the Water Research Foundation (WRF), focusing on air emissions from Cured-in-Place Pipe (CIPP) rehabilitation. Her work aims to better understand and mitigate environmental impacts associated with trenchless pipeline renewal technologies.
Sevda Jannatdoust PhD Student
Sevda is a Ph.D. student who is deeply engaged in research supported by the Water Research Foundation, specifically through projects numbered 5216 and 5195. Her primary focus centers on studying air emissions generated by Cured-in- Place Pipe (CIPP) rehabilitation techniques. By analyzing data from these innovative projects, she strives to unravel the complex environmental impacts related to trenchless pipeline renewal methods, with the ultimate goal of developing strategies to mitigate these issues and advance more sustainable infrastructure solutions.
Optimizing Sensor Networks, Advanced Sensing Techniques for Enhanced Collection Systems Management
Sensors deployed throughout wastewater and stormwater collection systems play a critical role in monitoring physical, hydraulic, and environmental conditions. These sensors include flow meters, depth and pressure sensors, temperature and acoustic devices, as well as water quality probes that measure parameters such as pH, turbidity, chlorine, and dissolved oxygen. Strategically installed at key locations—such as manholes, interceptors, and pump stations— they enable continuous, real-time observation of system performance. This monitoring capability helps utilities promptly detect and respond to issues like blockages, overflows, infiltration, and contamination before they develop into more serious problems (Figure 1). By providing ongoing insight into system behavior, these sensor networks support more proactive and informed infrastructure management.
The continuous data generated by these sensors offers insights that transform utility operations. With real-time feedback, utilities can identify performance issues early, reduce emergency responses, and optimize maintenance planning. This results in lower operational costs, improved compliance, and enhanced environmental and public health protection. The integration of this data into analytics platforms allows for predictive modeling, anomaly detection, and performance forecasting – empowering utilities to shift toward proactive management approaches.
A wide range of stakeholders benefit from real-time monitoring and intelligent decision-support systems. Utility operators leverage sensor data for daily operations and real-time control, while engineers and planners rely on long-term data trends for asset management and capital improvement planning. Information collected through SCADA systems, remote telemetry units, and cloud-based platforms is analyzed using artificial intelligence and machine learning models. These tools generate intuitive outputs— such as intelligent dashboards—that deliver actionable insights, alerts, and operational recommendations tailored to utility needs. Advanced sensor networks, deployed across many utilities, integrated level, flow, and water quality sensors within sewer collection systems (Figure 2). These systems support a shift from reactive to proactive management by enabling predictive analytics and autonomous optimization. Utilities adopting such smart infrastructure have achieved significant improvements, including overflow reduction, enhanced compliance, and substantial cost savings – highlighting the transformative potential of data-driven sewer infrastructure management.
Through scalable optimization techniques and AI-based forecasting tools, the research aims to develop practical solutions adaptable to utilities of varying sizes and system complexities. This effort is supported by participating organizations that contribute operational insights, including American Water, Great Lakes Water Authority (GLWA), Loudoun Water, Orange County Government, South Platte Renew, Trinity River Authority of Texas (TRA), Underground Infrastructure Magazine, WaterOne, Anglian Water, and WSSC Water. Project outcomes will be published in late 2026 and will include a guidance document, case studies, technical tools, and outreach materials.
These will be shared through workshops, conferences, and industry platforms to help utilities transition toward smarter infrastructure management. The research team also invites utilities, municipalities, and industry professionals to participate in a survey on sensor deployment, data analytics, and AI integration. Insights collected will directly inform the guidance document and support the development of data-driven management strategies. To participate, contact: cuire@uta.edu
Authors: Marjan Moradi, S.M. ASCE and Dr. Mo Najafi P.E., F. ASCE, BC.PLW
About the Author
Marjan Moradi
Ph.D. candidate
Marjan Moradi is a Ph.D. candidate in Civil Engineering at UT Arlington and a researcher at CUIRE, specializing in sustainable infrastructure and AI-driven sensor networks. With a 4.0 GPA and a background in architecture, urban design, planning, policy, and civil construction management, she contributes to WRF Projects 5239 and 5292, advancing smart utility management.
Pipeline Infrastructure Replacement Cost Guide
Water utilities across North America are under growing pressure to replace aging pipelines that were mostly installed in the mid-20th century. These pipelines are now reaching the end of their service life, resulting in more frequent failures and increasing maintenance costs.
However, many utilities struggle with planning because cost data is either outdated, overly general, or doesn’t reflect real-world conditions. The Pipeline Infrastructure Replacement Costs Guide is being developed to address this gap by offering practical, accurate, and easy-to-use information that utilities can rely on when making decisions about pipeline renewal and replacement.
This guide focuses on estimating direct construction costs including labor, materials, and equipment, for commonly used methods. These include traditional open-cut replacement and a range of trenchless technologies like cured-in- place pipe (CIPP), pipe bursting, horizontal directional drilling (HDD), spray-applied pipe lining (SAPL), and jack and bore. The goal is to provide water utilities with clear and realistic cost data that they can use during the early planning stages of capital projects. The guide is being designed to be regionally adaptable, meaning that users will be able to adjust cost estimates based on local conditions, inflation, project size, and site-specific characteristics. While it doesn’t calculate social or environmental costs directly, these will be discussed to help utilities understand the broader impacts of each method.
One of the biggest benefits of this project is that it uses real bid data and change orders collected from utilities across North America. This helps ensure that the cost curves are grounded in actual market conditions.
The project team will use modeling tools such as regression analysis and artificial neural networks to develop predictive cost models. These models will make it easier for utilities to estimate costs based on factors like pipe diameter, depth, and soil type. This is especially useful in avoiding cost overruns and improving long-term planning.
The guide will serve as a practical tool for utility managers, engineers, and planners. It can help them compare different replacement methods, build reliable budgets, and support funding requests. It also aims to reduce uncertainty during the planning phase and promote the use of trenchless technologies, which are often less disruptive and more cost-effective than open-cut methods especially in urban areas.
To make sure the guide stays useful over time, it will be built as a “living” tool. A web-based version will be developed and maintained by the Center for Underground Infrastructure Research and Education (CUIRE) at the University of Texas at Arlington, in partnership with the Water Research Foundation (WRF). This online version will allow for regular updates as new cost data becomes available. That way, utilities will always have access to the most current and relevant information. The project began in 2025 and is scheduled to run for 18 months. The approximate publication date for the guide is early 2027.
Authors: Paria Hamidzadeh , S.M. ASCE, Marjan Moradi, S.M. ASCE and Dr. Mo Najafi P.E., F. ASCE, BC.PLW
About the Author
Paria Hamidzadeh PhD Student
pxh2477@mavs.uta.edu
Paria Hamidzadeh is a Ph.D. candidate in Civil Engineering at the University of Texas at Arlington and a researcher at CUIRE. Her research focuses on trenchless technology, pipeline renewal, and infrastructure design optimization. With a 4.0 GPA, she has been involved in multiple projects related to the design, testing, and evaluation of manufactured in place composite pipe (MICP) and sprayed applied pipe linings (SAPL). She is currently contributing to the WRF 5292 project, where she is developing a cost estimation model for pipeline infrastructure renewal and replacement.
Sewer Pipe Condition Evaluation Using Ensemble Machine Learning
Aging sewer infrastructure and rapid urbanization have placed unprecedented strain on municipal networks, often leading to costly, reactive maintenance. This research proposes a data-driven, ensemble machine learning framework that integrates Decision Trees, K-Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machines (SVM) to predict the condition of sewer pipes with high accuracy. Trained in over 4,800 historical inspection records including both structural and environmental factors our model achieved 74.2 percent accuracy and a 70.6 percent F1 score, outperforming standalone classifiers.
The practical implications for the industry are significant. The ensemble model enables proactive maintenance planning by accurately identifying deteriorating pipes before failures occur. With a recall rate of 74.2 percent, municipalities can reduce emergency repair incidents and optimize their resource allocation, potentially decreasing unnecessary inspections by over 30 percent. Maintenance activities can also be prioritized using predicted condition ratings, allowing infrastructure managers to make more strategic investment decisions and prevent service disruptions.
This study demonstrates how artificial intelligence can address real-world challenges in civil infrastructure. By leveraging a soft- voting ensemble approach and applying robust preprocessing techniques like Min-Max scaling and one-hot encoding, the framework adapts to the complex, nonlinear patterns found in sewer system data. A simulated pilot study further validated the model’s integration with real-time sensor inputs, underscoring its scalability for smart infrastructure management.
Looking ahead, the research lays the groundwork for more advanced, resilient urban systems. Future enhancements will incorporate real- time monitoring, transfer learning, and explainable AI to further improve accuracy, transparency, and adoption. Ultimately, this work offers a transformative path forward for municipalities seeking to modernize their asset management strategies through machine learning.
Authors: Mahnaz Rouhi , S.M. ASCE, Vinayak Kaushal, Ph.D., P.E., M.ASCE, Jin-Zhu Yu, Ph.D. and Dr. Mo Najafi P.E., F. ASCE, BC.PLW
ABOUT THE AUTHOR
Mahnaz Rouhi
Graduate Student
mxr9322@mavs.uta.edu
Mahnaz Rouhi, S.M. ASCE, is a Graduate Research and Teaching Assistant at the Center for Underground Infrastructure Research and Education (CUIRE) within UTA’s Civil Engineering Department. Her scholarly work centers on developing an ensemble machine learning framework integrating classifiers such as Decision Trees, K‑Nearest Neighbors, Logistic Regression, and Support Vector Machines via soft voting to evaluate and predict sewer pipe condition more accurately than individual models.
In this study, Mahnaz applies the framework to a large-scale dataset of over 4,800 sewer pipe records. Using ten structural and environmental features (e.g., pipe age, material, soil pH), advanced preprocessing, and a robust ensemble classifier, the model achieved 74.2 percent accuracy, 67.9 percent precision, 74.2 percent recall, and a 70.6 percent F1 score outperforming baseline models in balancing false positives and negatives.
Her work enables risk-based inspection prioritization, helping municipal utilities, planners, and consultants optimize resources, reduce emergency repairs, and improve lifecycle planning.
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