Numerical  Modeling  of  the  Effects  of  Electrode Spacing  and  Multilayered  Concrete  Resistivity  on the  Apparent  Resistivity  Measured  Using  Wenner Method

Thiyagarajan, Karthick; Acharya, Parikshit; Piyathilaka, Lasitha; Kodagoda, Sarath

doi:10.36227/techrxiv.12145302

“…Gausian Process regression modeling approach [21], [22] is used in this work to normalize the noisy data coming from the robotic measurements. The model obtains the distance travelled by the robot D and predicts the RSSI values (R).…”

Section: Methodsmentioning

confidence: 99%

Using UHF-RFID Signals for Robot Localization Inside Pipelines

Gunatilake

¹

,

Galea

²

,

Thiyagarajan

³

et al. 2021

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)

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Underground water pipes are important to any country's infrastructure. Overtime, the metallic pipes are prone to corrosion, which can lead to water leakage and pipe bursts. In order to prolong the service life of those assets, water utilities in Australia apply protective pipe linings. Long-term monitoring and timely intervention are crucial for maintaining those lining assets. However, the water utilities do not possess the comprehensive technology to achieve it. The main reasons for lacking such technology are the unavailability of sensors and accurate robot localization technologies. Feature based localization methods such as SLAM has limited use as the application of liners alters the features and the environment. Encoder based localization is not accurate enough to observe the evolution of defects over a long period of time requiring unique defect correspondence. This motivates us to explore accurate contact-less and wireless based localization methods. We propose a cost-effective localization method using UHF-RFID signals for robot localization inside pipelines based on Gaussian process combined particle filter. Experiments carried out in field extracted pipe samples from the Sydney water pipe network show that using the RSSI and Phase data together in the measurement model with particle filter algorithm improves the localization accuracy up to 15 centimeters precision.

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“…is the model's growth trend expressed as in (6) and S (t) is the model's seasonality expressed as in (7).…”

Section: B Time Series Forecasting Modelsmentioning

confidence: 99%

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Thiyagarajan¹,

Kodagoda²,

Ulapane³

2020

Preprint

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Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

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“…This sensor is an invasive type and needs human traversing in sewers for taking measurements like core sampling. Also, there are on-going studies that focus on the feasibility of estimating the depth of acid permeation through electrical resistivity based sensing technology for predicting the corrosion [6]. This reported work is a preliminary investigation through simulation analyses.…”

Section: Introductionmentioning

confidence: 99%

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Thiyagarajan¹,

Kodagoda²,

Ulapane³

2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

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Numerical Modeling of the Effects of Electrode Spacing and Multilayered Concrete Resistivity on the Apparent Resistivity Measured Using Wenner Method

Cited by 3 publications

References 23 publications

Using UHF-RFID Signals for Robot Localization Inside Pipelines

Using UHF-RFID Signals for Robot Localization Inside Pipelines

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

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