Horizontal displacement monitoring method of deep foundation pit based on laser image recognition technology

Liu, Peng; Xie, Shulin; Zhou, Guobing; Zhang, Lixiao; Zhang, Guoxian; Zhao, Xuefeng

doi:10.1063/1.5061703

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…As the construction sector is gradually entering the digital age, digital technologies have been widely used in all stages of a construction project (Chen et al, 2015;Turner et al, 2020). Facing the serious problems of deformation monitoring during deep foundation pit excavation, Liu et al (2018) applied laser image recognition technology to monitor the horizontal displacement of deep foundation pit. Wu et al (2021) used unmanned aerial vehicle (UVA) images to monitor and reflect the safety situation of deep foundation pit.…”

Section: Related Workmentioning

confidence: 99%

A digital twin-driven deformation monitoring system for deep foundation pit excavation

Chen¹,

Liu²,

Hu³

et al. 2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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Deformation happening in deep foundation pit excavation is a growing concern to underground construction, threatening not only the construction safety but also the adjacent environment. Traditional deformation monitoring is conducted once a day through the method of manual measurement. Such monitoring strategy has been blamed for inefficiency and error-prone, and also the monitoring results cannot be timely received by on-site managers for safety management. Therefore, the study proposes a digital twin-driven deformation monitoring system to support accurate risk-preventing decisions. The proposed system comprises four interconnected components for data acquisition, data processing, result visualization, and risk warning, respectively. A real-life tunnelling construction project – Lianghu tunnelling construction in Wuhan, China – is used to illustrate the functionality of the proposed system. Findings show that the system will be a valuable step for implementing digital twin to deep foundation pit excavation from concept to practice.

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Section: Related Workmentioning

confidence: 99%

A digital twin-driven deformation monitoring system for deep foundation pit excavation

Chen¹,

Liu²,

Hu³

et al. 2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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“…3 Advances in Mathematical Physics (1) The k nearest neighbors of each sample point are obtained by the linear analysis (LDA) algorithm. The process of finding the nearest neighbor of the sample points is as follows: firstly, the LDA subspace A is obtained by reducing the dimension of the training set X.…”

Section: Commodity Price Recognition Based On Imagementioning

confidence: 99%

“…In order to solve the problem of dimensionality disaster and effectively deal with high-dimensional data, data dimensionality reduction technology appears. When dealing with highdimensional data, people naturally consider the possibility of projecting these data into low-dimensional subspace without losing important information about some characteristics of the original variables [1]. Data dimensionality reduction technology is a process of mapping data from highdimensional space to low-dimensional space, which can best maintain the structure and compactness of data so as to obtain the low-dimensional representation of highdimensional data.…”

Section: Introductionmentioning

confidence: 99%

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Liu

Wang

Bai

2021

Advances in Mathematical Physics

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Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their respective defects. In order to achieve the ideal effect of high-dimensional nonlinear image recognition, based on the analysis of the traditional dimensionality reduction algorithm and refining its advantages, an image recognition technology based on the nonlinear dimensionality reduction method is proposed. As an effective nonlinear feature extraction method, the nonlinear dimensionality reduction method can find the nonlinear structure of datasets and maintain the intrinsic structure of data. Applying the nonlinear dimensionality reduction method to image recognition is to divide the input image into blocks, take it as a dataset in high-dimensional space, reduce the dimension of its structure, and obtain the low-dimensional expression vector of its eigenstructure so that the problem of image recognition can be carried out in a lower dimension. Thus, the computational complexity can be reduced, the recognition accuracy can be improved, and it is convenient for further processing such as image recognition and search. The defects of traditional algorithms are solved, and the commodity price recognition and simulation experiments are carried out, which verifies the feasibility of image recognition technology based on the nonlinear dimensionality reduction method in commodity price recognition.

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“…However, such models can incur the effects of scale [9][10][11]. Data obtained from field monitoring are the most accurate of the three methods above but can be collected at only a limited number of measurement points, require a long time, and are expensive to obtain [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Deformation and Stability of Ultra-Deep Pit in Plateau Alluvial–Lacustrine Gravel Strata

Guo,

Liu

2024

Processes

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Deformation of ultra-deep pit walls and surrounding geotechnical bodies due to engineering disturbances typically shows intricate spatiotemporal patterns. In this study, deformations at critical steps of the construction process were first numerically simulated by Midas GTS NX, and this was followed by lab-scale geophysical model tests of the entire process of the pit construction. Data on deformation obtained from numerical simulations and lab-scale geophysical model tests were compared with those obtained from a dynamic monitoring scheme in the field to analyze the characteristics of the deformation and evolution of the pit wall. This was used to derive a generally applicable theoretical expression to predict variations in the horizontal displacements.

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Horizontal displacement monitoring method of deep foundation pit based on laser image recognition technology

Cited by 7 publications

References 19 publications

A digital twin-driven deformation monitoring system for deep foundation pit excavation

A digital twin-driven deformation monitoring system for deep foundation pit excavation

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Characteristics of Deformation and Stability of Ultra-Deep Pit in Plateau Alluvial–Lacustrine Gravel Strata

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