Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability

Megri, Ahmed Cherif [North Carolina A&amp;amp;T State University] (ORCID:0009000434183009)

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DOE OSTI · 2570966

Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability

Megri, Ahmed Cherif [North Carolina A&amp;T State University] (ORCID:0009000434183009)

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Abstract

Crack detection in underground reinforced concrete pipes has been essential in determining the state of stormwater infrastructure. Detection models have been implemented for detecting cracks and other defects in pipes using CCTV footage for stormwater drainage systems. In addition, Finite element models have been used to determine optimum shapes and pipe thickness for different boundary conditions such as header pipes in power plants. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint. This paper discusses the integration of both topics by using the cracks detected in underground pipes as boundary conditions for shape optimization of the pipes. A machine learning model has been developed which uses limited data for training and outlines the location of detected cracks. A shape optimization methodology is proposed in which ANSYS modules are used to analyze fluid flow and then optimize the shape of the pipe. The crack detection model developed has been applied to a crack detected in lab setting and machine learning model used has an accuracy of 98% using a random forest algorithm.

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20 FOSSIL-FUELED POWER PLANTS Shape optimization, Machine Learning, Reinforced Concrete Pipes, Crack Detection.

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Megri, Ahmed Cherif [North Carolina A&amp;T State University] (ORCID:0009000434183009). 2025-06-01. Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability. https://www.osti.gov/biblio/2570966

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