Detection of road cavities in urban cities by 3D ground-penetrating radar

Liu, Hai; Shi, Zhenshi; Li, Jianhui; Liu, Chao; Meng, Xu; Du, Yanliang; Chen, Jie

doi:10.1190/geo2020-0384.1

Cited by 82 publications

(36 citation statements)

References 35 publications

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“…However, the implementation is slow because key factors have been overlooked, such as cost and deployment [ 25 , 26 , 27 ]. Besides, laser scanners are effective in construction activities monitoring by laser signals from a rotating laser photon source [ 28 , 29 ]. However, the deployment is restricted by factors containing a clear line of sight, long data processing time, and high data storage capacity.…”

Section: Introductionmentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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Sensing technologies present great improvements in construction performance including the safety, productivity, and quality. However, the corresponding applications in real projects are far behind compared with the academically research. This research aims to discover dominate influence factors in the sensing technologies adoption and ultimately develop a governance framework facilitating adoption processes. The framework is dedicated on general sensing technologies rather than single sensor in previous framework studies. To begin with, the influence factors of sensing technologies and other similar emerging technologies are summarised through a review. Then, a mixed methods design was employed to collect quantitative data through an online survey, and qualitative data through semi-structured interviews. Findings of the quantitative method reveal that the most widely implemented sensing technologies are GPS and visual sensing technology, but they’re still not adopted by all construction companies. Partial Least Squares Structural Equation Modelling reveals that supplier characteristics have the highest effect in all influence factors. Qualitative method was adopted to investigate perceptions of construction stakeholders on the major decision-making considerations in the adoption process. Ultimately, a triangulation analysis of findings from the literature review, online survey and interviews resulted in the governance framework development. The overarching contribution of this research focus on the general adoption of sensing technologies rather than the adoption of a specific sensor. Therefore, the governance framework can assist with the decision-making process of any sensing technology adoption in construction.

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Section: Introductionmentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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“…The cavities and ssures in the thick Quaternary sediments provide a water channel connection for groundwater, so GPR is used for supplementary detection (Dorn et al 2012;Liu et al 2021). The water content greatly affects the dielectric constant of surrounding rock.…”

Section: Discussionmentioning

confidence: 99%

Multi-depth and refined detection method of karst features under thick Quaternary sediments of subway lines

Han

Wang

et al. 2022

Preprint

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When the subway engineerings cross the karst area, the unfavorable geology may bring many potential safety hazards to the subway design, construction and operation. There is a wide range of karst distribution in southwest China. Extremely irregular karst area is distributed in the strata along the subway lines. The Quaternary sediments are thick, which greatly increases the difficulty of detection. A multi-depth refined detection method of karst features under thick Quaternary sediments is proposed in this paper. Firstly, the karst cavities and fractures in the shallow stratum of the study area are determined by ground-penetrating radar (GPR). Secondly, the mid-deep resistivity cross-sections are delineated using surface electrical resistivity tomography (ERT) and transient electromagnetic method (TEM). The principal component analysis (PCA) is used to fuse the resistivity data. The three-dimensional imaging is established to delineate the distribution of karst anomalies. As the information obtained by PCA is more comprehensive, the accuracy of geophysical interpretation is improved. The multi-depth refined detection of karst under thick Quaternary sediments on subway lines is realized through integrated analysis. This method has been applied in Changsha Metro Line 3 and achieved good practical results. It is of great application value for the karst tunnel treatment and the safe excavation.

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“…Instances include improving the quality of life for a certain portion of the city by using IT hardware, software, networks and data on different municipal districts and services. It could include many city components, including natural resources, infrastructure, electricity, transport, education, health, governance and public safety [26][27][28].…”

Section: The Proposed Bda With Iot On Urban Underground Engineering (...mentioning

confidence: 99%

Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications

Liu¹,

Zhang²,

Zhang³

2022

Computer Modeling in Engineering &Amp; Sciences

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A core element of the sustainable approach to global living quality improvement can now become the intensive and organized usage of underground space. There is a growing interest in underground building and growth worldwide. The reduced consumption of electricity, effective preservation of green land, sustainable wastewater and sewage treatment, efficient reverse degradation of the urban environment, and reliable critical infrastructure management can improve the quality of life. At the same time, technological innovations such as artificial intelligence (AI), cloud computing (CC), the internet of things (IoT), and big data analytics (BDA) play a significant role in improved quality of life. Hence, this study aims to integrate the technological innovations in urban underground engineering to ensure a high quality of life. Thus, this study uses big data analytics to carry out the status quo of foundation treatment and proposes a conceptual framework named the BDA with IoT on urban underground engineering (BI0T-UUE). This framework connects hidden features with various high-level sensing sources and practical predictive model characterization to lower building costs, productive infrastructure management, preparedness for disasters, and modern community smart services. The IoT integration gives an optimum opportunity to work towards the functionality of ''digital doubles'' of secret infrastructure, both economical and scalable, with the increasing sophistication and tooling of the underworld. The simulation analysis ensures the highest efficiency and cost-effectiveness of the underground engineering with a value of 96.54% and 97.46%. KEYWORDSUnderground engineering; internet of things; big data analytics; status quo; economic and social; smart city 1 Overview of Urban Underground Engineering using Big Data Analysis for Smart City Applications Smart cities have a strategic plan covering substantial usage of the underground [1]. Wellplanned underwater infrastructure can positively affect land use and development decisions and minimize vehicle usage and related effects [2]. High-density urban centers, however, could rely on centralized services and improve sustainable development [3]. Multiple difficulties connected This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Detection of road cavities in urban cities by 3D ground-penetrating radar

Cited by 82 publications

References 35 publications

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Multi-depth and refined detection method of karst features under thick Quaternary sediments of subway lines

Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications

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