Presentation 1: Longitudinal Bending Performance of Cured-in-Place Pipe Liners Spanning Across Reinforced Concrete Pipe Joints
With the increasing use of trenchless technologies, particularly Cured-in-Place Pipe (CIPP) liners, for the rehabilitation of aging wastewater infrastructure, there remains a critical need to evaluate their mechanical performance under various conditions. Existing standards provide insufficient guidance on the longitudinal behavior of CIPP liners at pipe joints, highlighting a gap in current knowledge. This study investigates the longitudinal bending performance of CIPP liners spanning reinforced concrete pipe joints through experimental testing on 900 mm reinforced concrete pipes subjected to AASHTO live loads at varying burial depths. Results indicate that CIPP liners effectively reduce joint rotation by more than 50% compared to unlined pipes. Longitudinal strain measurements show increasing strain magnitudes as burial depth decreases. Furthermore, the study validates Zhai and Moore’s (2024) design equation, with predicted strain values closely aligning with measured results, demonstrating its applicability for assessing liner behavior under joint rotation. These findings provide a better understanding of the ability of CIPP liners to reduce joint rotation and improve the structural response of rehabilitated pipelines at the joint.
Presentation 2: Leveraging Deep Learning to Understand Climate Change Effects on Urban Water Main Failures
The optimal annual replacement of deteriorating water mains is a critical task for water utilities. Pipe failure prediction models serve as essential tools in the strategic planning of rehabilitation efforts for urban water distribution infrastructure. However, climate change introduces new challenges to this process which may significantly increase the risk of water main failures. As climate patterns shift, predicting how these changes may impact water main integrity is crucial for sustainable urban water management. Current failure prediction methods typically overlook the impact of long-term climate change scenarios, limiting their accuracy and applicability. Therefore, there is a pressing need for advanced predictive models that can incorporate both historical data and climate projections to better forecast water main failures. This study aims to develop a robust predictive framework for water main breaks, accounting for climate change. Deep learning algorithms, particularly Long Short-Term Memory (LSTM) networks were applied. Different types of data comprising water main inventory and break records, along with climate data encompassing temperature and rainfall records were utilized. The sensitivity of results to various climate change scenarios was also explored. The methodology was validated with a case study of Saskatoon, Canada, serving as a real-world testbed for evaluating its effectiveness. Results indicated that cast iron pipes are more vulnerable to future climate scenarios with colder temperatures, while the overall system and asbestos cement pipes are likely to face increased failures in scenarios with higher temperatures. The findings of this study offer insights for decision-makers regarding the effective management of water distribution networks in the face of climate change.