Study and Application on Auto-Generation of Drainage Pipe Network Given a Sink

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.

<p>In order to fully consider the influence of the existing facilities and the society on the design of the urban drainage and drainage pipe network, it is necessary to analyze the technical aspects of the drainage structure, elevation, structure, construction technology and other functions and cost influence, through the analysis and calculation of the listed of a number of alternatives to optimize. In the actual transformation of the application, especially for the natural flow through the gravity of the rainwater system, the vault relative to the circular pipe is more appropriate, can be used as the first choice, and according to different circumstances, the parameters of the culvert can be optimized through the calculation has been determined for cost-effective of the program and parameters.</p>

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.

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.

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.

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

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.

At present, there is more storm waterlogging in our urban, one of the main reasons is that there are many disadvantages in rainwater pipe network design, this paper in the pipe network design must be reasonable to determine the ground water runoff coefficient, The ground water time, reduction coefficient, catchment area and position and quantity of gully according to the measured data, and appropriately increasing the recurrence interval according to the actual in engineering, hydraulic calculation as much as possible to select data sets larger diameter, accurate calculation time of flow, to reduce the urban storm waterlogging fundamentally.

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

Small, but fast fluctuating consumptions in a pipe network can lead to severe transient flows. Such fluctuations are stochastic in nature and, cannot be explicitly identified and analyzed for the systems in operation. This study introduces a mathematical model for taking into account the fluctuating consumptions in analysis and design of pipe networks. For this purpose, the fluctuations are simulated by two successive triangular pulses with adjustable geometry. A many-objective optimization problem is developed to find the worst maximum and minimum pressure heads. A new scheme of genetic algorithms is developed to find the extreme values of all pressure heads in only one single simulation run. The proposed model is applied to two water distribution networks. It is found that, the transient flow caused by fast fluctuating consumptions could seriously affect the system’s performance. For instance, analyzing a real pipe network reveals that, when the fluctuations are critically combined, the nodal pressure heads averagely change by 29 % (13 to 88 %) and −43 % (−16 to −177 %) with respect to the initial steady state.

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.

This study introduces a new formulation for hydraulic analysis and design of looped pipe networks. The network is partitioned into two sub-graphs so-called the base and the remainders graphs. The base graph is a spanning tree of the network and, the remainders graph includes pipes hypothetically removed from the network to open the loops. The network governing equations of mass and energy conservation are manipulated so that, pipe flows in the remainders and head losses in the base graph are considered as independent variables to calculate pipe diameters directly. The method is applied to two example networks and the results are discussed. The new approach is found to be computationally efficient and useful for design and optimization of pipe networks.