Experimental and Numerical Study on Mechanical Analysis of Buried Corroded Concrete Pipes under Static Traffic Loads

Fang, Hongyuan; Yang, Ke; Li, Bin; Tan, Peiling; Wang, Fu; Du, Xueming

doi:10.3390/app9235002

Cited by 19 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a long time, research on soil collapse has been mainly based on the mechanical analysis of the effect of soil collapse on buried pipelines [30,31], lacking the necessary dynamic evolution process of buried pipeline collapse. Soil collapse simulation techniques mainly include three types: manual removal of collapse areas, application of displacement loads, and unit life and death techniques.…”

Section: Discussion On Soil Collapse Simulation Research Methodsmentioning

confidence: 99%

Buried Pipeline Collapse Dynamic Evolution Processes and Their Settlement Prediction Based on PSO-LSTM

Zhou,

Teng,

Chi

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Based on the unit life and death technology, the dynamic evolution process of soil loss is considered, and a pipe-soil nonlinear coupling model of buried pipelines passing through the collapse area is constructed. The analysis shows that after the third layer of soil is lost, the existence of the “pipe-soil separation” phenomenon can be confirmed, which then supplements the assumption that “pipe-soil is always in contact” in the elastic foundation beam theory. Calculation of settlement deformation of buried pipelines It needs to be divided into two stages: cooperative deformation and non-cooperative deformation. Taking the settlement prediction of buried pipelines as the goal, the particle swarm algorithm (PSO) was used to optimize the number of neurons, Dropout, and Batch-size in the long short-term memory network (LSTM) structure. The optimization results were 60, 0.001, and 100, respectively. The PSO-LSTM model proposed in this article can accurately describe the dynamic evolution process of buried pipelines and has better prediction accuracy than the modified Gaussian curve method and LSTM neural network model. The use of this model can provide a reference for safety risk management, disaster early warning, and intelligent monitoring when buried pipelines suffer from soil collapse disasters.

show abstract

Section: Discussion On Soil Collapse Simulation Research Methodsmentioning

confidence: 99%

Buried Pipeline Collapse Dynamic Evolution Processes and Their Settlement Prediction Based on PSO-LSTM

Zhou,

Teng,

Chi

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Moreover, corrosion depth has a greater impact on the pipeline than the corrosion length and corrosion width [20]. Fang et al [21] showed that as the corrosion depth increases, the maximum principal stress and strain of the pipeline also increase, and the probability of pipe failure increases. Wang et al [22] investigated the causes of cast iron pipe failure and established a new corrosion pit depth prediction model.…”

Section: B Predicting the Depth Of Corrosion In Pipelinesmentioning

confidence: 99%

Multi-Parameter Maximum Corrosion Depth Prediction Model for Buried Pipelines Based on GSCV-XGBoost

Wang,

Song,

Fang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Corrosion is one of the most common types of damage in buried oil and gas pipelines. Corrosion leaks can cause serious accidents and can be harmful to pipelines during service. The maximum corrosion depth of an oil and gas pipeline is an important indicator for assessing the remaining strength of the pipeline. An accurate prediction of the maximum corrosion depth is important for the safe operation of pipelines. Machine learning has been shown to perform well in predictive assessment efforts. However, previous studies have rarely considered the effects of corrosion characterization and parameter optimization simultaneously. In this study, a multi-parameter maximum corrosion depth prediction model for pipelines based on GSCV-XGBoost is proposed, which can be applied to real projects. The model performs feature extraction on the pipeline dataset through Pearson correlation analysis, identifies the parameters that contribute more to the maximum corrosion depth, and predicts the maximum corrosion depth of the pipeline using an optimized machine learning model. The machine learning model used in this study was obtained by optimizing the XGBoost model using the GirdSearchCV method. That is, the optimal hyperparameter combination of the model was obtained by 10-fold cross-validation and grid searching. The prediction results were compared with those of five common machine learning models. The conclusions show that the GSCV-XGBoost model performs the best in predicting the maximum corrosion depth of the pipeline with the smallest error. The R 2 and Root Mean Square Error(RMSE) scores for the test set were 0.9886 and 0.2057, respectively. The prediction accuracy was improved by 34.59% over that of the conventional XGBoost model.INDEX TERMS Oil and gas pipelines, maximum corrosion depth prediction, machine learning, correlation analysis, XGBoost

show abstract

“…To determine an appropriate element size, element sensitivity analysis was conducted in this section, and five element sizes were chosen for numerical calculations, as shown in Table 3. To assess the influence of the element size on the calculation results, the computed results using different element sizes were extracted and compared with the experimental data of Fang et al [27], as illustrated in Figure 5. In the figure, "C" represents the error at the crown of the pipe, "I" represents the error at the invert of the pipe, and "S" represents the error at the springline of the pipe.…”

Section: Mesh Sensitivity and Convergence Analysismentioning

confidence: 99%

Numerical Study on Mechanical Properties of Corroded Concrete Pipes before and after Cured-in-Place-Pipe Rehabilitation

Zhang

Yang

2023

Sustainability

Self Cite

View full text Add to dashboard Cite

Cured-In-Place-Pipe (CIPP) rehabilitation technology is widely utilized in pipeline rehabilitation projects and has exhibited favorable results. Nevertheless, the mechanical characteristics of pipelines after CIPP rehabilitation and the effectiveness of CIPP rehabilitation in repairing these mechanical characteristics remain unknown. To address these issues, a three-dimensional numerical model of a corroded concrete pipe before and after CIPP rehabilitation was established in the present study. To authenticate the accuracy of the numerical model, the numerical simulation data were compared with the full-scale test data from prior research, and the comparison outcomes show that the numerical model formulated in this study is reasonable and reliable. To appraise the repair effectiveness of CIPP rehabilitation, the mechanical properties of a corroded pipe, a CIPP-repaired pipe, and a normal pipe under traffic load were computed and compared, and the comparison outcomes demonstrate that the stress in the pipe bell, stress in the pipe spigot, vertical displacement of the pipe crown, and vertical displacement of the pipe invert were reduced by 39.8%, 16.7%, 24.7%, and 24.4%, respectively, after CIPP rehabilitation. Moreover, a series of three-dimensional numerical models were constructed to scrutinize the impacts of factors such as corrosion degree, corrosion angle, and traffic load on the mechanical properties of corroded pipelines before and after CIPP rehabilitation. The findings indicate that the stress on the pipe escalates with increasing corrosion degrees and diminishes with increasing corrosion angles; there are no noteworthy differences between the vertical displacement of the pipe and the von Mises stress of the CIPP liner for diverse corrosion degrees and corrosion angles; the amplification of the traffic load will augment the stress and displacement of the pipe and increase the rotation of the pipe, resulting in a significant upsurge in the stress of the CIPP liner at pipe joints. When the traffic load magnitude rises from 0.7 MPa to 1 MPa, the stress and displacement of the pipe and the von Mises stress of the CIPP liner were increased by 18.9%, 42.3%, and 42.1%, respectively.

show abstract

Experimental and Numerical Study on Mechanical Analysis of Buried Corroded Concrete Pipes under Static Traffic Loads

Cited by 19 publications

References 24 publications

Buried Pipeline Collapse Dynamic Evolution Processes and Their Settlement Prediction Based on PSO-LSTM

Buried Pipeline Collapse Dynamic Evolution Processes and Their Settlement Prediction Based on PSO-LSTM

Multi-Parameter Maximum Corrosion Depth Prediction Model for Buried Pipelines Based on GSCV-XGBoost

Numerical Study on Mechanical Properties of Corroded Concrete Pipes before and after Cured-in-Place-Pipe Rehabilitation

Contact Info

Product

Resources

About