Methods to monitor the defects of the drainage pipe network: a review

Field trials to detect drainage pipe networks using thermal and RGB data from unmanned aircraft

Research on intelligent control theory and strategy of gas drainage pipe network based on graph theory

Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

The core of comprehensive treatment effect of water environment in river basin lies in pipe network system. The blockage of urban drainage pipe network is one of the main reasons affecting the healthy operation of urban drainage network system. So the intelligent analysis method of urban drainage pipe network blockage diagnosis can quickly find the problems of urban drainage pipe network and improve the operation and maintenance efficiency of urban drainage pipe network. In this paper, through the analysis of rainfall data, rainfall and sewage pipe network flow variation regular pattern, the change range, the essay obtains the rain and sewage pipe network blockage diagnosis threshold. Combined with the urban intelligent drainage pipe network system, through the customized setting of pipe network blockage threshold, the rapid diagnosis and early warning of the blockage problem of the drainage pipe network are realized, and the healthy operation of the urban drainage network is guaranteed.

Urban waterlogging control: A novel method to urban drainage pipes reconstruction, systematic and automated

In drainage engineering design, particularly, in analysis, check and hydraulic calculation of drainage pipe network, recognition and acquisition of drainage pipe network is the first and foremost task (Li et al., 2007).To implement the auto-generation of drainage pipe network drawing, this paper applies the graph theory to model the composition and data structure of a drainage pipe network. An effective method is proposed to automatically generate the directed drainage pipe network given a sink. The Breadth-first Search algorithm, Incidence-edge calculation algorithm and Transpose-graph algorithm from Boost Graph Library (BGL) are applied to implement the algorithm for the proposed method. The method provides greater automation and more efficiency but requires less inputs in the process of obtaining the practical drainage pipe network than other commonly used software do. It also lays a foundation for drainage pipe network analysis and related to hydraulic calculations. The accuracy and efficiency of the method is verified by the engineering practices described in this paper.

Based on the one-dimensional channel conservancy model, this paper considers the factor of urban pipe network drainage, and couples the pipe network model with the one-dimensional channel conservancy model to fully analyse the interaction between the channel water level and the drainage pipe network displacement. Taking a coastal city as an example, the model simulation results show that when the water level of the river is high, it will cause the drainage of the pipe network; the lower part of the terrain will become waterlogged because of the aging of the urban pipe and rainstorm. It is impossible to effectively solve the problem of flood control and drainage by widening the river channel. It is necessary to adopt the coordinated improvement of the pipe network and the river channel to achieve effective targets.

Quantitative simulation of urban waterlogging using computer models is an effective technical means for urban storm water management, especially for predicting and preventing waterlogging. In this study, a city in the northern Anhui Plain, China, was selected as the study site. The Storm Water Management Model was applied to simulate the dynamic changes in the pipeline overload, node overflow, and discharge port runoff characteristics from three perspectives: surface runoff, pipe network transmission, and flow control of low-impact development. The operation of the rainwater pipe network under different return periods and the real-time operation of the rainwater pipe network were simulated to seek solutions to urban waterlogging problems caused by flat terrain and slow drainage. The results revealed that surface runoff is the primary source of rainfall in the study area, with a runoff coefficient of 0.599. The drainage pipe network was optimized by expanding the diameter of the pipe from ≤1.5 mm to ≥2 mm. The water reduction rate was more than 50%, and overload did not occur after optimization. Therefore, sinking green space technology and optimization methods for expanding a pipe diameter can reduce urban waterlogging.

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

In order to achieve the governance of urban water environment systems and ensure the normal operation of urban drainage systems, it is necessary to regularly inspect the drainage pipe network to detect and identify defects. Traditional methods of manually identifying defects in Close Circuit Television (CCTV) data and annotating them are labor-intensive and inefficient. To address this issue, a method for automatic identification of pipe network defects based on convolutional neural network (CNN) and transformer is proposed. The model is named RFCBAM-CGA-RTDETR and has been applied to detect 13 types of pipe network defects. Experimental results indicate an evaluation accuracy of 61.8%, demonstrating its effectiveness and reliability. This model outperforms RT-DETR and YOLOv8 models by 6.6% and 10%, respectively, providing a novel approach for drainage pipe network defect detection.

This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.

Despite urban drainage pipe networks being of great significance to urban flood control, few studies have focused on the influence of urban drainage pipe network structure on hydrological response. We studied two areas in downtown Dongying, Shandong Province, China, which have dendritic and loop drainage pipe networks, respectively. The loop network was converted to a dendritic system for scenario analysis using numerical simulation. Comparing the hydrological response of the original drainage system and the converted system showed that although the dendritic network could reduce node flood volume, the dendritic pipe overload situation was inferior to the loop network. Runoff in the former network was predominantly concentrated in the main pipes and near the outlets and the discharge-peak at the outlet was 47.0% greater than that of the loop. Thus, the loop drainage pipe network is more likely to reduce peak runoff at the outlet, which helps decrease flood risk.

In the daily life of the city, the normal operation of underground drainage pipes is a necessary condition to ensure the normal life of residents. However, with the increase of the service life of the drainpipe and the improvement of the function of water transmission and sewage, it is particularly important to evaluate the state of the drainpipe. However, the traditional pipe network detection methods such as CCTV and periscope detection are not only inefficient but also cost high. Nowadays, the Object Detection technology is becoming more and more mature, and the application of image detection technology to the defect detection of drainage pipe network is also a hot research direction. Therefore, an improved YOLOv5 Object Detection method was selected in this paper to realize the defect detection of drainage pipe network. In addition, in order to better complete the detection task in the complex image background of the waterway, the multi-head attention mechanism was incorporated into the backbone network of YOLOv5, and the FPN+PAN structure of YOLOv5 was replaced by BiFPN structure. Finally, through simulation experiments, the Precision(P) of the YOLOv5-TB model used in this paper reached 93.1%, the Recall(R) reached 85.5%, and the Mean Average Precision(mAP) reached 88.4%. Moreover, the mAP increased by 1.1% on the basis of YOLOv5. The simulation results show that the model used in this paper can well complete the task of drainage network defect detection.

Predicting the spread of contamination in water distribution networks laid on sloping terrains

Increasing urbanisation and climate change have had a significant impact on urban infrastructure due to the expansion of impervious surfaces. These surfaces lead to excessive runoff, which strains urban drainage systems beyond capacity and often causes damage. Traditional rainfall-runoff models often fail to adequately reflect the specific characteristics and limitations of urban pipe networks. These models typically use the Curve Number (CN) method to categorise land into eight types, including urban areas. This method primarily addresses surface and subsurface runoff without considering important urban infrastructure components such as drainage pipes and storage facilities. Recognising these shortcomings, this paper presents a new concept of distributed rainfall-runoff model, called S-RAT Urban, which incorporates the urban pipe network in the watershed analysis. The model incorporates both temporal and spatial variability of hydrological processes to improve the accuracy of runoff prediction. The model uses a distributed approach that applies the curve number method to better represent urban environments, including the effects of overlooks. The model uses a grid-based input system that uses Digital Elevation Models (DEMs), land use and soil type data to generate flow directions. The traditional two-dimensional (2D) flow direction mapping used in urban areas is converted to a one-dimensional (1D) pipe network model. This conversion is critical to realistically simulate flow through urban drainage systems. In addition, flow within the pipe network is calculated using continuous and impulse equations to provide a dynamic and realistic representation of the urban hydrological response under different weather conditions. This method not only identifies the variability of the hydrological cycle under natural conditions, but also incorporates critical urban infrastructure into the distributed model, providing a more comprehensive and practical tool for urban catchment management.