Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method

Peng, Sen; Cheng, Jing; Wu, Xingqi; Xu, Fang; Wu, Qing

doi:10.3390/w14020150

Cited by 12 publications

(1 citation statement)

References 23 publications

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“…A pressure sensor is an apparatus that has the capability to sense pressure signals and convert them into electrical signals that can be utilized following certain rules. Pressure sensors are commonly composed of pressure-sensitive elements and signal processing units, and are extensively employed [24]- [26]. Based on the type of pressure being tested, pressure sensors can be categorized into gauge pressure sensors, differential pressure sensors and absolute pressure sensors.…”

Section: Analysis Of Rotary Jet Pile and Key Sensor Technologymentioning

confidence: 99%

Research on Digital Monitoring Technology for Airport High-Pressure Rotary Jet Piles

Tang,

Cheng,

Wang

et al. 2023

IEEE Access

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The high-pressure rotary jet pile construction method has been widely used in airport projects, particularly in regions with soft soil foundations, such as coastal areas. The method's primary aim is to establish a sound foundation bearing capacity. However, the conventional machinery used in constructing high-pressure rotary jet piles lacks automation. To achieve real-time quality control in high-pressure rotary jet pile construction, the development of high-pressure rotary jet pile construction data acquisition and monitoring equipment is made possible by integrating key technologies such as the global navigation satellite system (GNSS), the Internet of Things and massive data processing. The acquisition and storage of crucial construction parameters, such as the drilling position, drill rod verticality, drilled hole depth, grouting pressure, grouting lifting speed and grouting volume for high-pressure rotary jet piles is successfully implemented in this paper. A large amount of actual construction data is collected through an on-site application, and the construction quality of the high-pressure rotary jet pile machine is monitored and analyzed using geographic information system (GIS) technology. Actual applications demonstrate that the proposed digital monitoring model and method for rotary jet pile construction quality control is viable, improves the on-site management level and efficiency and has significant future value.INDEX TERMS High-pressure rotary jet pile, digital monitoring, Internet of Things, global navigation satellite system (GNSS), geographic information system (GIS).

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Section: Analysis Of Rotary Jet Pile and Key Sensor Technologymentioning

confidence: 99%

Research on Digital Monitoring Technology for Airport High-Pressure Rotary Jet Piles

Tang,

Cheng,

Wang

et al. 2023

IEEE Access

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Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches

Bloemheuvel,

van den Hoogen,

Atzmueller

2023

Int J Data Sci Anal

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Graph neural networks (GNNs) haven proven to be an indispensable approach in modeling complex data, in particular spatial temporal data, e.g., relating to sensor data given as time series with according spatial information. Although GNNs provide powerful modeling capabilities on such kind of data, they require adequate input data in terms of both signal and the underlying graph structures. However, typically the according graphs are not automatically available or even predefined, such that typically an ad hoc graph representation needs to be constructed. However, often the construction of the underlying graph structure is given insufficient attention. Therefore, this paper performs an in-depth analysis of several methods for constructing graphs from a set of sensors attributed with spatial information, i.e., geographical coordinates, or using their respective attached signal data. We apply a diverse set of standard methods for estimating groups and similarities between graph nodes as location-based as well as signal-driven approaches on multiple benchmark datasets for evaluation and assessment. Here, for both areas, we specifically include distance-based, clustering-based, as well as correlation-based approaches for estimating the relationships between nodes for subsequent graph construction. In addition, we consider two different GNN approaches, i.e., regression and forecasting in order to enable a broader experimental assessment. Typically, no predefined graph is given, such that (ad hoc) graph creation is necessary. Here, our results indicate the criticality of factoring in the crucial step of graph construction into GNN-based research on spatial temporal data. Overall, in our experimentation no single approach for graph construction emerged as a clear winner. However, in our analysis we are able to provide specific indications based on the obtained results, for a specific class of methods. Collectively, the findings highlight the need for researchers to carefully consider graph construction when employing GNNs in the analysis of spatial temporal data.

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Graph convolutional network for water network partitioning

Chen,

Hu,

et al. 2025

Expert Systems with Applications

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Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method

Cited by 12 publications

References 23 publications

Research on Digital Monitoring Technology for Airport High-Pressure Rotary Jet Piles

Research on Digital Monitoring Technology for Airport High-Pressure Rotary Jet Piles

Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches

Graph convolutional network for water network partitioning

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