Mechanical response and failure pressure prediction of cracked PVC-UH buried thin-walled pipes
Source abstract
Abstract
High-performance unplasticized polyvinyl chloride (PVC-UH) buried water supply pipelines may develop cracks during service. It is essential to understand their mechanical response to loads during construction and service, identifying the parameters most likely to cause burst failure, establishing a calculation method or a prediction model for the failure pressure of cracked buried pipelines. Static loading and burst tests, combined with numerical simulations, were conducted to obtain the mechanical response and burst pressure of buried cracked pipes under load during service. Orthogonal experiment design method was used to evaluate data sets with different parameters and levels, and PEEQMAX for failure judgement of each data sets is obtained by numerical simulation. Then, parameters sensitivity was analyzed using the Gray Correlation Analysis. Based on the theory of elastic mechanics and elastic foundation beam, a formula for calculating the hoop stress of buried pipelines under internal pressure is obtained. Finally, a calculation formula and a machine learning-based model have been established to predict the failure pressure of cracked buried pipelines in engineering. The results indicate that the surrounding soil increases the failure pressure of buried pipelines by 0.08–0.34 MPa. The failure pressure calculated for buried pipelines with axial surface cracks aligns well with experimental values, exhibiting a prediction error within 10 %. Additionally, the predictive accuracy of the WOA-SVR model reaches 0.9791. The research is helpful to further understand the failure mechanisms and processes, improving the safety and reliability of municipal water supply pipelines.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Geotechnical Engineering and Underground Structures · Geophysical Methods and Applications · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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