Design of Real-Time Slope Monitoring System Using Time-Domain Reflectometry With Wireless Sensor Network

Yadav, Devendra Kumar; Karthik, Guntha; Jayanthu, Singam; Das, Santos Kumar

doi:10.1109/lsens.2019.2892435

Cited by 25 publications

(14 citation statements)

References 5 publications

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“…It is precisely because of the variability in space, time, and engineering properties of the rock and soil that constitute the slope; the search for the critical slip surface has evolved into a complex, nonlinear, and nonconvex programming problem. Since the determined safety factor value cannot truly characterize the safety of the slope, it is necessary to consider the randomness of slope analysis and design parameters and apply random theory to evaluate the reliability of slope engineering [19].…”

Section: Results Analysismentioning

confidence: 99%

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

2021

Advances in Civil Engineering

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Mine rock slope stability rating is the process of assessing its stability probability, analyzing its failure mode, and then determining its stability rating by location matching principle and scale matching principle, having important theoretical and practical significance in putting forward reasonable restoration suggestions. In recent years, rapidly developing artificial intelligence methods not only have the characteristics of highly nonlinear mapping, self-organization, and self-adaptation but also mostly apply probability optimal strategy and, hence, bypass the problem of solving function derivatives, having higher efficiency and feasibility in solving the problems of global optimal solution and local optimal solution. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of mine rock slope stability rating, elaborated the development background, current status, and future challenges of artificial intelligence methods, introduced the principles of the limit equilibrium method and finite element method, constructed an artificial intelligence-based mine rock slope stability calculation model, performed slope stability influencing factors’ analysis and optimization model parameter selection, proposed a mine rock slope stability rating algorithm based on artificial intelligence, analyzed the rating algorithm design and rating algorithm implementation, and finally conducted a case analysis and its discussion. The study results show that the artificial intelligence-based mine rock slope stability rating method can make full use of computer advantages to have larger capacity and faster calculation speed and solve to the greatest extent the randomness, timeliness, ambiguity, and other uncertain characteristics of mine rock slope stability by a large number of random sampling for finding the optimal value of the objective function. The study results of this paper provide a reference for further research studies on the stability rating of mine rock slopes based on artificial intelligence.

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Section: Results Analysismentioning

confidence: 99%

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

2021

Advances in Civil Engineering

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“…Guo et al [163] implemented online, inexpensive wireless surveillance application architecture, production and field deployment using Arduino Board ZigBee, and Python for near real-time landslide surveillance, which provides noticeable is alerted of potential landslide default. Linderman et al [164] demonstrated the shortcomings of the Structural Health Monitoring (SHM) Common Analog to Digital Converter (ADC) Framework and provides a firmware approach for delay-tolerant remote end points.…”

Section: Comprehensive Analysis and Findingsmentioning

confidence: 99%

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

et al. 2021

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In today's fast advancing world, sensors are used in various applications to provide complete data about different objects present around us. The sensor data when integrated with Mobile Cloud Computing (MCC) is used for further computation to provide useful results advanced warning, forecasting, planning, and plethora of other applications in this uncertain world. Few works have been proposed where sensor nodes are integrated with Wireless Sensor Networks (WSNs) which give a new direction in modern research for developing new technologies which help the users for fast access of sensory data over mobile devices. Herein, a systematic literature review of both MCC and WSNs are conducted and subsequently assimilated them via an architecture which is efficient and achievable within short time-span for a variety of applications. The MCC applications are reviewed from two main perspectives that are energy and information management. The communication aspect in MCC and WSNs has been discussed and the need for integration between the two has been justified in this work. The work done on mobility management in MCC and WSNs is reviewed. Also, the challenges in MCC-WSNs integration along with the comprehensive analysis and findings of the review are identified. Finally, the conclusion and specific future direction of research in this area are provided. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The software used here provides an outline guide to use the wireless network for measuring slope movements in the x-axis, y- [66] broad ∼seconds 900 m easy no SSR [29] broad ∼minutes 850 m easy yes GPS [16] discrete points ∼seconds -difficult yes TDR [8] discrete points ∼seconds NA difficult yes photogrammetry [56] broad hours <150 moderate no micro seismic [67] broad continuous 300 m easy yes inclinometer [68] discrete points daily NA difficult yes piezometers [69] discrete points daily NA difficult yes crack meters [70] discrete points daily NA easy yes OTDR [65] discrete points ∼seconds NA difficult yes string potentiometer [71] discrete points ∼seconds NA moderate yes Shape Accel Array [72] discrete points ∼seconds NA difficult yes [65] axis and z-axis. The x, y and z coordinated are measured precisely and continuously concerning a farther point Then data is collected in real-time or processed and obtained.…”

Section: Robotic Total Stationmentioning

confidence: 99%

“…This system senses all physical values such as acceleration, thermal moisture and pressure of the slope movement. Slope monitoring is also achieved in real time using time‐domain reflectometry (TDR) with ZigBee as a wireless module [8]. A planar micro‐strip antenna is used with crank slits for circular polarisation for WSN instead of linear antenna due to the influence of wild animals, rainfall and wind [9] this setup is installed at a high position, it is enveloped by a lightening protection cage to protect it [10].…”

Section: Related Workmentioning

confidence: 99%

Critical review on slope monitoring systems with a vision of unifying WSN and IoT

Yadav

Jayanthu

Das

et al. 2019

IET wirel. sens. syst.

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Slope stability is one of the major geological concerns in open-cast mines. The mining area slopes needs to be regularly monitored to detect occurrence of any slope failure prior to any catastrophe. An early warning system solves this purpose preventing loss of significant mass of human lives as well as property. This is a critical scrutiny which renders various techniques and methodologies regarding slope monitoring of open-cast mine. Wireless sensor network (WSN) has been standing out as a productive competent tool for monitoring the tangible environmental structures by sensing the differences, processing this raw data and finally communicating the result to the web from where it can be referred for researches and predicting mechanisms. Internet of Things (IoT) comes into picture for communicating the sensed and processed data from the sensors to the application where it can be further analysed. In recent scenario, power issue is of great concern associated with deploying a real-time monitoring system. This review depicts how WSN can be adopted and preferred to any other technologies used in slope monitoring due to its advantages it brings. Moreover, collaborative operation of WSN with IoT results in a more systematic, robust, energy-efficient, cost-effective real-time monitoring approach.

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Design of Real-Time Slope Monitoring System Using Time-Domain Reflectometry With Wireless Sensor Network

Cited by 25 publications

References 5 publications

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

Critical review on slope monitoring systems with a vision of unifying WSN and IoT

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