Towards a comprehensive review of the deterioration factors and modeling for sewer pipelines: A hybrid of bibliometric, scientometric, and meta-analysis approach

Salihu, Comfort; Hussein, Mohamed; Mohandes, Saeed Reza; Zayed, Tarek

doi:10.1016/j.jclepro.2022.131460

Cited by 29 publications

(8 citation statements)

References 77 publications

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“…Although pipelines are considered one of the safest and most economical modes of transportation, a breach in the integrity of the pipeline network can lead to catastrophic consequences [28]. Shang & Shenb found that, according to statistics from the Pipeline and Hazardous Materials Safety Administration (PHMSA), more than 12,500 pipeline incidents were recorded from 2001 to 2020 [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Flow-Measuring Hydropneumatic Bench for Testing Pipeline Valves

et al. 2023

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Pipe fittings are an important element of any pipeline network, ensuring stable and safe operation by regulating the flow of the working medium. To control the performance of pipeline valves, it is necessary to conduct various tests, the main ones of which are hydraulic and pneumatic. It is important to expand testing capabilities and reduce time costs. The purpose of this work is to combine hydraulic and pneumatic tests into one test complex, which will reduce the time of the test complex due to the absence of the need for reinstallation and reconfiguration. The subject of the study is the determination of the design, technical, and operational characteristics of such a stand, as well as the simulation of operating conditions to confirm its operability. During the development, methods of solid and surface modeling, the finite element method, and analytical calculation methods were used. The results of the stand design are presented, and the features of the process of its development are described, including the analysis of the stress-strain state and the analysis of reliability and durability indicators. The obtained values of the distribution of equivalent stresses, deformations, and displacements of the structure elements do not exceed the maximum allowable values and do not lead to destruction. The analysis shows that the developed stand has improved capabilities compared to those previously used. Doi: 10.28991/CEJ-2023-09-01-013 Full Text: PDF

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Section: Literature Reviewmentioning

confidence: 99%

Development of a Flow-Measuring Hydropneumatic Bench for Testing Pipeline Valves

et al. 2023

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“…This step was implemented using the raster calculation function in GIS software. • Step 2: Computing the value for each pixel using formula (11) to create the groundwater level map at time 𝑡. According to Worldometer [87], the annual population change (APC) in Ålesund city is 0.62% (2020-2021).…”

Section: F Generation Of Structural Condition Mapmentioning

confidence: 99%

“…Structural condition models can be classified into physical, statistical, and machine learning [11][12][13][14]. In the physical models, parameters related to structures of the sewer pipes (e.g., material, diameter, type of effluent, etc.,) are employed to fit mathematical equations to the sewer's status [15].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

2022

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Assessment of the structural condition of sewer pipes is one of the critical steps in asset management and support investment decisions; therefore, structural condition models with high accuracy are important that can help the utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of advanced machine learning (ML) for predicting the structural condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater level, traffic volume, distance from the road, and soil type) were used to assess the sewer structural condition employing seventeen ML models. After processing the sewer inspections, 1,159 of 1,449 individual pipelines were used to train the structural condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the RF model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.

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“…The pipeline ground-penetrating radar (PPR) detection device is capable of effectively detecting cavities in the vicinity of the pipeline. This capability represents one of the fundamental technologies employed in urban underground space security systems [ 1 ]. The research object of this study is the three-channel drainage pipeline radar detection device [ 2 , 3 ], which has been developed by the China University of Mining and Technology (Beijing) and is depicted in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm

Xu,

Yang,

Fang

et al. 2024

Sensors

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The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.

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Towards a comprehensive review of the deterioration factors and modeling for sewer pipelines: A hybrid of bibliometric, scientometric, and meta-analysis approach

Cited by 29 publications

References 77 publications

Development of a Flow-Measuring Hydropneumatic Bench for Testing Pipeline Valves

Development of a Flow-Measuring Hydropneumatic Bench for Testing Pipeline Valves

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm

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