Search

Using pipe lines to rehabilitate concrete drainage pipes is a popular trenchless technique. In this paper, the concrete pipe and liner are equivalent to a symmetrical structural mechanics problem, and the mechanical solution of the composite structure is obtained using the force method. Subsequently, the analytical solutions are compared with the test and finite element results. The distribution law of axial force, bending moment, and shear force of the composite structure are obtained, and the circumferential strain law of the inner and outer surfaces of the host pipe is analyzed. Lastly, the influence of pipe diameter and liner thickness on the bearing capacity of the host pipe is examined. The results indicate that the axial and shear force distribution of the composite structure remains the same for different pipe diameters, as the distribution diagrams of shear and axial force along the circumferential direction coincide. While the elastic modulus and thickness of the liner have no effect on the internal force distribution of the composite structure, they do directly affect the stress state of the host pipe. Additionally, the bottom support causes abrupt changes in the bending moments and strains of the host pipe. [ABSTRACT FROM AUTHOR]

Flow resistance is a critical calculation parameter in hydraulic engineering. In this study, the roughness coefficient of a circular drainage pipe with sedimentation was investigated under open-channel flow conditions using experimental measurements and numerical calculations. The ratio of the height of the sediment placed in the pipe to the diameter was defined as the sedimentation degree. The Manning roughness coefficient (n) was determined for pipes with different sedimentation degrees. Moreover, numerical simulations were performed for circular pipes with diameters of 0.152 m and a slope of 0.003, with discharges varying between 1 and 8 L/s. The results of the numerical calculations were validated in terms of the water surface and Manning's n using the corresponding experimental data. Accordingly, an equation in the form of the Moody formula was proposed to estimate the roughness coefficient. Subsequently, the predicted Manning roughness coefficient obtained through the proposed equation was compared with independent experimental results. The results indicate that the equation accurately predicts Manning's n in a pipe with sedimentation, which can provide a theoretical basis for the design of drainage pipelines. [ABSTRACT FROM AUTHOR]

Under the coupling effect of the defects inside and around pipes, the shear displacement of the joints of old concrete pipes fallen into disrepair usually shows an accelerated deterioration trend, and its excessive value can lead to structural damage or fluid leakage. Therefore, developing a prediction model for the maximum shear displacement of concrete drainage pipe joints with preexisting defects is of crucial significance. To this end, this work selects internal corrosion and erosion voids as the typical defects inside and around old pipes, establishes 300 sets of three‐dimensional fine finite element (FE) models of the concrete drainage pipes with corrosion and void, and verifies the reliability of the developed FE model by the full‐scale tests. Based on the verified FE data, a prediction model is successfully proposed for the assessment of the maximum shear displacement of the concrete drainage pipe joints with corrosion and void defects. Moreover, parameter sensitivity analysis is carried out to reveal their relative contribution to the maximum shear displacement of pipe joints. The results show that the model proposed herein has the ability to accurately predict the maximum shear displacement of the pipe joints. The maximum and minimum relative error between the value predicted by the proposed model and the one computed by the FE model is also 12.4% and 4.8% respectively. Moreover, the traffic load with a contribution of 28.7% has the greatest impact on the maximum shear displacement of pipe joints. [ABSTRACT FROM AUTHOR]

Frequent accidents caused by underground pipeline damage are a widespread societal concern. Trenchless rehabilitation methods, particularly cured-in-place pipe (CIPP) lining, are increasingly used for pipeline repair with great success. Existing research is mainly concerned with practical improvements in rehabilitation and evaluating the performance of rehabilitation. In this study, the model of corroded buried concrete pipeline that had been rehabilitated with CIPP was established using numerical methods, and the Mesh-based parallel-Code Coupling Interface (MpCCI) was used to investigate multifield coupling effects of soil pressure, traffic load, and fluid-structure interactions. Moreover, the influences of corrosion depth, corrosion width, traffic load, cover depth, and water quantity on CIPP wall thickness were compared and analyzed. The result shows that maximum principal stress and vertical displacement of pipeline markedly decreased after CIPP rehabilitation, and thus the new CIPP can carry loads in a deteriorated pipe. Stress and displacement of the composite pipe liner were positively correlated with corrosion depth and negatively correlated with corrosion width. Increase in traffic load rapidly increases von Mises stress of CIPP, and increase in cover depth rapidly increases maximum principal stress of pipeline. Water flow has little effect on the pipe liner, and flow capacity increases slightly after CIPP rehabilitation. CIPP wall thickness was positively correlated with corrosion depth, traffic load, cover depth, and water quantity and negatively correlated with corrosion width. [ABSTRACT FROM AUTHOR]

Concrete pipes are the most widely used municipal drainage pipes in China. When concrete pipes fall into years of disrepair, numerous problems appear. As one of the most common problems of concrete pipes, cracks impact on the deterioration of mechanical properties of pipes, which cannot be ignored. In the current work, normal concrete pipes and those with pre-existing cracks are tested on a full scale under an external compressive load. The effects of the length, depth, and location of cracks on the bearing capacity and mechanical properties of the concrete pipes are quantitatively analyzed. Based on the full-scale tests, three-dimensional finite element models of normal and cracked concrete pipes are developed, and the measured results are compared with the data of the finite element analysis. It is clear that the test measurements are in good agreement with the simulation results; the bearing capacity of a concrete pipe is inversely proportional to the length and depth of the crack, and the maximum circumferential strain of the pipe occurs at the location of the crack. The strain of the concrete pipe also reveals three stages of elasticity, plasticity, and failure as the external load rises. Finally, when the load series reaches the limit of the failure load of the concrete pipe with pre-existing cracks, the pipe breaks along the crack position. [ABSTRACT FROM AUTHOR]

• A series of pipeline bearing capacity test was carried out. • A self organizing map-genetic algorithm-support vector machine was developed. • Accurately assess the influence of wall defects on the pipeline. Corrosion and cracks are common issues in drainage pipelines. To investigate the mechanical properties of pipes with defects, a series of pipeline bearing capacity tests were carried out. In addition, a prediction model using a combination of self-organizing maps, genetic algorithms, and support vector machines (SOM-GA-SVM) was developed to predict the bearing capacity and circumferential strain of the pipeline. The prediction results obtained using this model were compared with those obtained using three other optimization algorithms. Furthermore, the influence of loading speed and defect volume on the prediction accuracy of the model was analyzed. The results indicated that the MAPE of the prediction results was less than 7%, the RMSE was less than 8, and the R2 was greater than 0.98; Additionally, the prediction accuracy of the SOM-GA algorithm was significantly higher than that of the genetic algorithm, particle swarm optimization algorithm, and grid search method; It was found that removing the loading speed from the variables and changing the defect depth and width to defect volume can improve the prediction accuracy of the model. After removing the loading speed, the average MAPE and average RMSE of the prediction model were reduced by 7.357% and 8.385%, respectively. After changing the defect depth and width to defect volume, the average MAPE and average RMSE of the prediction model were reduced by 4.925% and 5.054%, respectively. In order to accurately assess the influence of corrosion and crack defects on the mechanical properties of pipelines, firstly, a series of pipe bearing capacity of pipelines were carried out to obtain test data, then SOM, GA and SVM were combined to establish a SOM-GA-SVM prediction model, and finally, the test data were compared with the predicted data to prove the feasibility and reasonableness of the SOM-GA-SVM model. [Display omitted] [ABSTRACT FROM AUTHOR]

• A set of visualized permeable grouting model test devices for sand layer was created. • Anti-seepage reinforcement mechanism of permeable polymer in sand layer is studied. • BP neural network prediction model for grouting effect is constructed. Permeation grouting is one of the important methods of anti-seepage reinforcement when the drainage pipes crosses loose diseases caused by leakage. With the development of grouting materials, permeable polymer slurry is widely used in engineering practice. Due to its low viscosity, fast reaction and micro-expansion, the diffusion and reinforcement law of slurry in loose area of drainage pipeline is more complex. To better understand the effects of water head pressure, grouting pressure, sand particle size and clay content on the anti-seepage and reinforcement effect of permeable polymer grouting in loose area of drainage pipeline, several types of tests, including permeability and uniaxial compressive strength tests, were performed. Then, based on the model test results, a BP neural network prediction model for the anti-seepage reinforcement effect of permeable polymer grouting in loose area of drainage pipeline is constructed, and the research results are applied to the treatment project of loose area of drainage pipeline for verification. The results show that: 1) after grouting, the order of magnitude of permeability coefficient of the consolidated body is reduced to 10-7cm/s, the anti-seepage performance of the sand layer is greatly improved, and the grouting pressure is the main controlling factor affecting the anti-seepage performance of the consolidated body. 2) The compressive strength of the consolidated body is obvious different under different working conditions. The water head pressure is the main controlling factor affecting the strength of the consolidated body. 3) Due to the limitation of test conditions, the relative error between the predicted value of BP neural network model and the actual value is about 20%, which can meet the general prediction needs. [ABSTRACT FROM AUTHOR]

Polymer grouting technology is an economical and efficient trenchless way to repair leakage and settlement of drainage pipeline. In this paper, the mechanical behaviours of drainage pipeline under traffic load before and after polymer grouting and cement grouting trenchless repairing are investigated through three-dimensional (3D) finite element method (FEM). Four different working conditions, including normal pipeline, disengaging pipeline, polymer-repaired pipeline, and cement-repaired pipeline are considered. The effects of load type, load location, buried depth on the mechanics of pipe are discussed in detail. The results show that the traffic load has great impact on both sides of the load position within 4 m, and the dangerous points of the pipeline are located at the bell and spigot joints. The most unfavorable condition occurs when the location of the disengaging corresponding to the load position. The greater influence on the disengaging pipeline with deeper buried depth applied. The disengaging pipeline can be repairing effectively and reliably by both the polymer and the cement grouting technology. However, the polymer material is more valuable than the cement material in terms of the Mises stresses after repairing. [ABSTRACT FROM AUTHOR]

With the age of pipeline and increase in the volume of urban sewage, the pipeline has different degrees of defects, which can cause safety problems such as road collapse and urban flooding. The service life of drainage pipes is closely related to daily maintenance and inspection, so it is very important to inspect the defects and monitor the operation of drainage pipes regularly. However, the existing research lacks quantitative detection and intelligent management of pipeline defect information. Therefore, the depth camera is used as the sensor to quantitatively detect the volume and area of the pit on the concrete pipe, and a defect information management platform is constructed in this paper. Firstly, combined BIM model with 3D point cloud, this paper proposes a 3D defect information management platform of drainage pipeline. Then, the depth camera is used to collect the damage data and preprocess the data, and a method for calculating the damage volume and surface area of drainage pipeline based on 3D mesh reconstruction of the defect point cloud is proposed. The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, and the maximum error is 17.54%, indicating high accuracy. The drainage pipeline information model is created. Finally, the data is uploaded to the information management platform to realize the visualization and informatization of pipeline defects and the later operation and maintenance requirements of the pipeline. [ABSTRACT FROM AUTHOR]

The urban drainage system is an important part of the urban water cycle. However, with the aging of drainage pipelines and other external reasons, damages such as cracks, corrosion, and deformation of underground pipelines can cause serious consequences such as urban waterlogging and road collapse. At present, the detection of underground drainage pipelines mostly focuses on the qualitative identification of pipeline damage, and it is impossible to quantitatively analyze pipeline damage. Therefore, a method to quantify the damage volume of concrete pipes that combines surface segmentation and reconstruction is proposed. An RGB-D sensor is used to collect the damage information of the drainage pipeline, and the collected depth frame is registered to generate the pipeline's surface point cloud. Voxel sampling and Gaussian filtering are used to improve data processing efficiency and reduce noise, respectively, and the RANSAC algorithm is used to remove the pipeline's surface information. The ball-pivoting algorithm is used to reconstruct the surface of the segmented damage data and pipe's surface information, and finally to obtain the damage volume. In order to evaluate, we conducted our research on real-world materials. The measurement results show that the method proposed in this paper measures an average relative error of 7.17% for the external damage volume of concrete pipes and an average relative error of 5.22% for the internal damage measurements of concrete pipes. [ABSTRACT FROM AUTHOR]

• Siltation transport behaviour of pipeline influenced by water flow remains unclear. • Critical bed shear stress model is proposed to solve boundary constraint problem. • Probability settlement function is adopted to assess ease of particle settlement. • Adaptive penalisation mechanism is adopted for intelligent settlement prediction. Siltation of drainage pipelines is a major disaster-causing factor in urban flooding. Owing to critical bed shear stress and the possibility of settlement at the bottom of pipes, there are significant nonlinear differences in the hydraulic transport and retardation response characteristics of siltation particles under different initial flow velocities, siltation degrees, and siltation lengths. Studies on siltation in drainage pipelines lack a comprehensive understanding of the intricate coupling dynamics between siltation particles and water flow, and the regularity governing the siltation transport behaviour under varying water flow conditions remains unclear. To solve these problems, this study constructs computational fluid dynamics and discrete element (CFD-DEM) coupling method and adaptive penalty model for pipeline deposition based on probabilistic settlement function, adaptive genetic algorithm, and bidirectional long short-term memory neural network (PSF-AGA-BLSTM). A three-dimensional transient hydraulic model of pipeline siltation was adopted to clarify the critical bed shear stress of the silt particles, after which an adaptive genetic algorithm with a probability settlement function as fitness was constructed to generate a numerous silt particle sample sets with settlement result labels. Adaptive genetic algorithms were combined with bidirectional long short-term memory neural networks to establish a spatiotemporal adaptive penalisation mechanism for pipeline silt particle transport. The simulation model based on PSF-AGA-BLSTM and CFD-DEM was analysed using simulation and scale tests and was found to have strong robustness and reliability. The results showed that the effects of siltation degree, flow rate, and siltation length on the transport of silt particles were all non-negligible; when the siltation degree exceeded 0.2, the siltation degree had a more significant effect on particle transport than siltation length and flow rate. The transport of silt particles occurred in two phases, rapid growth and steady growth under erosion by water flow. The rate of change in silt length accounted for up to 45% of the reason for silt nudging and silt spreading. The study clarified the hydraulic transport law of silt particles and analyzed the effect of different influencing factors. These efforts provide technical support for subsequent pipe cleaning and maintenance as well as flood prevention and mitigation. [ABSTRACT FROM AUTHOR]

• A 3-D pipe-soil-sewage coupling model of HDPE corrugated pipe was established. • Loose backfill has obvious influence on strain at crest and valley of pipe. • Transporting sewage makes the pipeline more prone to local bending. • Over time, creep of materials increases the probability of pipe failure. A high-density polyethylene (HDPE) double-wall corrugated pipe is a flexible plastic drainage pipe. The backfill compaction, the external load, the sewage in the pipe, and the strength of a pipe determine its strain distribution. In this paper, a real three-dimensional model of a high-density polyethylene double-wall corrugated pipe is established by using ABAQUS and ANSYS, and the coupling simulation of pipe-soil-fluid under real conditions is realized. A full-scale prototype verifies the feasibility of the model. According to the strain distribution of the pipe obtained by numerical simulation and the constitutive structure of the high-density polyethylene material, the local bending and creep development of the pipe are predicted. The results reveal that the deformation of the pipe is dependent on the three factors (backfill compaction, sewage volume, and flow speed) and independent of the sand in the sewage. The backfill compaction determines the approximate distribution of pipe strain, while sewage reduces the strain on the interior wall. The sewage causes the tensile strain of the interior wall near the invert, increasing the possibility of local bending. Over time, the strain of the pipe increases due to creep, and local bending develops rapidly. [ABSTRACT FROM AUTHOR]

• A simulation method that integrates the spatial random frequency function and drainage pipe model is presented. • Utilizing attention operators to construct attention mechanisms, a Point Transformer drainage pipe detection model is established. • The study investigates how various parameters affect the model's accuracy. • The effect of different proportions of datasets on the segmentation performance of the model is studied. • The developed model is employed for accurately segmenting corrosion in drainage pipes. Deep learning based Closed Circuit Television (CCTV) image detection has achieved remarkable accuracy in identifying and segmenting concrete pipeline damages. However, the lack of in-depth information and requirement for large amounts of training data are two major drawbacks. Therefore, this study proposes a point cloud data segmentation method that incorporates depth information to address the issue of insufficient training data for depth models, enabling efficient and accurate segmentation. Firstly, 3D simulation technology is employed to construct a simulation dataset. Secondly, an optimized point transformation model is established to identify and segment the point cloud data. The proposed method achieved a test accuracy of 94.36 % by optimizing the network structure and the training strategy. The Mean Intersection over Union (MIoU) increased by 3.62 % and 10.3 %, respectively, compared with the classic PointNet++ and before adding simulation data, reaching 86.31 %. These results demonstrate that the proposed method exhibits high precision defect detection capability on three-dimensional point clouds of drainage pipelines. [ABSTRACT FROM AUTHOR]

• Siltation diagnosing systems of drainage pipes cannot achieve full coverage. • Generative adversarial network solves the small data sample problem. • Guided faster R-CNN and parallel multiscale U-Net improves flow measurement. • Stochastic neighbour embedding is better at compressing key silting features. • Multi-scale sampling improves the accuracy and robustness of siltation diagnosis. The regular detection and diagnosis mechanism for the silting disease of drainage pipelines (SDP) is critical for making dredging decisions and flood forecasting. Simultaneously, there is a complex coupling relationship between the pipeline siltation and the dynamic changes of the inlet and outlet flows. However, the traditional silting detection frameworks of drainage pipelines face difficulties in two aspects, accurate flow rate measurements and efficient and intelligent siltation diagnoses. In this paper, an automatic and integrated self-diagnosing system for the silting disease of drainage pipelines is proposed. First, a gradient penalty generative adversarial network (GPGAN), a target recognition algorithm called guided faster R-CNN (GF R-CNN) and an image segmentation method called parallel multiscale U-Net (PMSU-Net) are introduced to dynamically measure the flow of water in the drainage pipelines. Second, an automatic siltation detection and diagnosis algorithm of SDP is constructed based on a t-SNE stack sparse autoencoder (SSAE-TSNE) and multiscale long short-term memory (MS-LSTM). Then, a full-scale prototype verifies the feasibility of the system. This improved flow measurement algorithm achieves the highest accuracy of 90.32%, and an F1 score of 0.963 in comparison with the other algorithms, and the precision-recall curve was the closest to the top right corner. The error rate of the proposed self-diagnosing system is controlled within 8%, which is far better than the other algorithms. Finally, the system algorithms are embedded in an intelligent platform consisting of an unmanned pipeline measuring device (UPM), an unmanned aerial vehicle (UAV) and a server diagnostic centre. The practical application and test show that the accuracy, precision and response speed of the proposed integrated self-diagnosing system have obvious advantages in the function of siltation detection and diagnosis in drainage pipelines. [ABSTRACT FROM AUTHOR]