Modelling concrete deterioration in sewers using theory and field observations

Wells, T.; Melchers, Robert E.

doi:10.1016/j.cemconres.2015.07.003

Cited by 102 publications

(55 citation statements)

References 37 publications

(55 reference statements)

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“…The choice of three cities and two sites in each city enabled the study of different temperatures and different H 2 S levels. Concrete coupons were fixed into the sewers as described (Wells and Melchers, 2015) and then recovered approximately every 6 months in the early stages of the trial and yearly in the later stages. The retrieved coupons were analyzed similarly to the laboratory coupons for measurement of surface pH, sulfur compounds in the corrosion layer and the mass loss due to corrosion.…”

Section: Long-term Corrosion Tests In Real Sewersmentioning

confidence: 99%

Predicting concrete corrosion of sewers using artificial neural network

et al. 2016

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Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

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Section: Long-term Corrosion Tests In Real Sewersmentioning

confidence: 99%

Predicting concrete corrosion of sewers using artificial neural network

et al. 2016

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“…With recent advancements in predictive analytics, researchers in collaboration with Australian water utilities have developed a sensor data-driven model for predicting the corrosion across the sewer pipe [13]. Those models primarily incorporate air temperature, relative humidity, and hydrogen sulphide concentration of sewer air as data inputs to the model [14]. Also, the model takes surface temperature sensor measurements [15], [16] and surface moisture sensor measurements [17]- [19] as additional inputs to reduce prediction uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Thiyagarajan¹,

Kodagoda²,

Ulapane³

et al. 2020

Preprint

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Smart sensor systems play a decisive role in the condition assessment of concrete sewer pipes going through microbial corrosion. Few Australian water utilities adopt a predictive analytic model for estimating the corrosion. They require sensor inputs like sewer air temperature data for corrosion prediction. A sensor system was developed to monitor the daily variation of sewer air temperature inside the harsh sewer environmental conditions. However, a diagnostic tool to evaluate the streaming sensor data is vital for reliable monitoring. In this context, this paper proposes a temporal forecasting driven approach for anomaly detection in sewer air temperature sensor system. Several temporal forecasting models were comprehensively evaluated and adopted Facebook’s Prophet method based forecasting to develop an anomaly detection approach. The proposed approach was evaluated with sewer air temperature sensor data and the results indicate a reasonable anomaly detection performance.

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“…A recent research study has established the reliance on the rate of corrosion (mm year -1 ) as a functional relationship with the concentration of H2S (ppm), relative humidity (%) and temperature (Kelvin) in the sewer atmosphere [8]. The bacterium that dwells on the sewer concrete surface oxidizes the gas phase H2S into aqueous sulphuric acid (H2SO4), which in turn reacts with the cement component of concrete [9].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Since the production of aqueous by-product occurs on the surface of the sewer concrete, it is vital to measure the surface properties such as temperature and moisture of the sewer concrete. The measured data can be used to fit into the corrosion prediction model proposed by [8] as an alternative to relative humidity and gas-phase temperature data.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Analytical Model and Data-driven Approach for Concrete Moisture Prediction

Thiyagarajan¹,

Kodagoda²

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-The advent of smart sensing technologies has opened up new avenues for addressing the billion dollar problem in the wastewater industry of H2S corrosion in concrete sewer pipes, where there is a growing interest in monitoring the environmental properties that govern the rate of corrosion. In this context, this paper proposes a methodology to predict the moisture content of concretes through data-driven approach by using Gaussian Process Regression modeling. The experimental program in this study practices measurements during wetting and drying phases of concrete. The obtained moisture data is used to train the prediction model against interpreted electrical resistivity data. The data of analytical model formulated from Archie's Law is then analyzed with experimental and Gaussian Process prediction data.

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Modelling concrete deterioration in sewers using theory and field observations

Cited by 102 publications

References 37 publications

Predicting concrete corrosion of sewers using artificial neural network

Predicting concrete corrosion of sewers using artificial neural network

A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Analytical Model and Data-driven Approach for Concrete Moisture Prediction

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