Frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete

© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Electrical resistivity is an important parameter to be monitored for the conditional assessment and health monitoring of aging and new concrete infrastructure. In this paper, we report the design and development of a frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete. Firstly, a sensing system prototype was developed based on the Wenner probe arrangement for the electrical resistivity measurements. This system operates by integrating three major units namely current injection unit, sensing unit and microcontroller unit. Those units govern the overall operations of the sensing system. Secondly, the measurements from the developed unit were compared with the measurements of the commercially available device at set conditions. This experimentation evaluated the measurement performance and demonstrated the effectiveness of the developed sensor prototype. Finally, the influence of rebar and the effect of frequency on the electrical measurements were studied through laboratory experimentation on a concrete sample. Experimental results indicated that the electrical resistivity measurements taken at a closer proximity to the rebar had its influence than the measurements taken away from the rebar in the ideal set condition. Also, the increase in electrical resistivity to the increase in frequency was observed, and then the measurements show lesser variations to higher frequency inputs

A Study on the Trends of Rainfall Patterns in the Intermediate and Dry Zones of Sri Lanka A Comparative Study for the Periods Ranging from 1941-1970 and 1971-2000

Since Sri Lanka is a tropical island unstable nature of the tropics has madeseveral temporal and spatial variations in rainfall throughout the island. Rainfall is oneof the principal factors that has been used to identify the three broad climatic zones inSri Lanka, namely the Wet zone, Intermediate zone and Dry zone. Much of thescientific researches on the rainfall pattern in Sri Lanka have revealed that most of themeteorological stations had recorded decreasing trends of rainfall during the past 100years. The present study attempts to ascertain the validity of these findings withreference to the study area of Intermediate and Dry zones of Sri Lanka (hereafterreferred to as the Intermediate and Dry zones). Considering the agricultural economy,the Dry and the Intermediate zones have been contributing towards more than 90% ofthe islands paddy. However, these two climatic zones show water surpluses in onlythree months (October to December) of the year. With this brief background, the presentstudy aims to identify the trends in rainfall in the Intermediate and the Dry Zones.Micro level framework is used for the selection of rainfall reporting stations andagro-ecological regions of these two zones. Accordigly 14 rainfall reporting stationshave selected for the study. Further, this is a comparative study of two 30 year periodsranging from 1941-1970 (1st period) and 1971-2000 (2nd period) and its seasons (FirstInter Monsoon (FIM), South West Monsoon (SEM), Second Inter Monsoon (SIM) andNorth East Monsoon (NEM). Time series analysis is employed for the identification ofany positive or negative trends of rainfall and the analysis is done on annual andseasonal basis.The results obtained from the analysis revealed that the highest and the lowestpositive trends belong to the 2nd period. It is clear that both highest and lowest negativetrends are apparent in the 1st period. All positive trends of the FIM in the 1st period havechanged into negative trends in the 2nd period. During the SWM, the highest positivetrend is showed in the 2nd period.Keywords: Rainfall Pattern, Intermediate Zone, Dry Zone, Positive Trend, NegativeTren

Gaussian markov random fields for localizing reinforcing bars in concrete infrastructure

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Sensor technologies play a significant role in monitoring the health conditions of urban sewer assets. Currently, the concrete sewer systems are undergoing corrosion due to bacterial activities on the concrete surfaces. Therefore, water utilities use predictive models to estimate the corrosion by using observations such as relative humidity or surface moisture conditions. Surface moisture conditions can be estimated by electrical resistivity based moisture sensing. However, the measurements of such sensors are influenced by the proximal presence of reinforcing bars. To mitigate such e ects, the moisture sensor needs to be optimally oriented on the concrete surface. This paper focuses on developing a machine learning model for localizing the reinforcing bars inside the concrete through non-invasive measurements. This work utilizes a resistivity meter that works based on the Wenner technique to obtain electrical measurements on the concrete sample by taking measurements at di erent angles. Then, the measured data is fed to a Gaussian Markov Random Fields based spatial prediction model. The spatial prediction outcome of the proposed model demonstrated the feasibility of localizing the reinforcing bars with reasonable accuracy for the measurements taken at di erent angles. This information is vital for decision-making while deploying the moisture sensors in sewer systems