Extracting dimensional information from steel reinforcing bars in concrete using neural networks trained on data from an inductive sensor

Zaid, Muhammad; Gaydecki, Patrick; Quek, Sung; Miller, Graham; Fernandes, Bosco

doi:10.1016/j.ndteint.2004.02.005

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…To reduce these uncertainties several different techniques can be combined to compute a performance indicator. As an alternative, the combination of several NDT parameters obtained with the same technique (Sbartaï, et al, 2012), (Zaid, et al, 2004), can confirm an information (for example the diagnosis about a certain damage of a certain severity) or reduce the measurement noise. (Villain, et al, 2012).…”

Section: Performance Assessmentmentioning

confidence: 99%

Lifecycle Management, Monitoring and Assessment for Safe Large-Scale Infrastructures: Challenges and Needs

Limongelli

Previtali

Cantini

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Many European infrastructures dating back to '50 and '60 of the last century like bridges and viaducts are approaching the end of their design lifetime. In most European countries costs related to maintenance of infrastructures reach a quite high percentage of the construction budget and additional costs in terms of traffic delay are due to downtime related to the inspection and maintenance interventions. In the last 30 years, the rate of deterioration of these infrastructures has increased due to increased traffic loads, climate change related events and man-made hazards. A sustainable approach to infrastructures management over their lifecycle plays a key role in reducing the impact of mobility on safety (over 50 000 fatalities in EU per year) and the impact of greenhouse gases emission related to fossil fuels. The events related to the recent collapse of the Morandi bridge in Italy tragically highlighted the sheer need to improve resilience of aging transport infrastructures, in order to increase the safety for people and goods and to reduce losses of functionality and the related consequences. In this focus Structural Health Monitoring (SHM) is one of the key strategies with a great potential to provide a new approach to performance assessment and maintenance over the life cycle for an efficient, safe, resilient and sustainable management of the infrastructures. In this paper research efforts, needs and challenges in terms of performance monitoring, assessment and standardization are described and discussed.

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Section: Performance Assessmentmentioning

confidence: 99%

Lifecycle Management, Monitoring and Assessment for Safe Large-Scale Infrastructures: Challenges and Needs

Limongelli

Previtali

Cantini

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…ANN has been widely applied for civil engineering problems in recent days. Two back feed propagation algorithms aimed at determining the bar diameter and depth were proposed by [13]. The neural network model for evaluating compressive strength of concrete based on integration of various nondestructive approaches was presented by [14].…”

Section: Introductionmentioning

confidence: 99%

Assessment of core strength of concrete by artificial neural networks

2021

JCE

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The proposed work deals with the use of Ultrasonic pulse velocity technique as an alternative method to identify compressive strength of the core concrete. The use of non-destructive technique without causing damages to the structure is tedious with interpretation of results influenced by various factors. Hence, an empirical relationship is developed using artificial neural network model for creating a regression between pulse velocity and compressive strength of concrete core specimens. Tests were conducted on reinforced concrete cylinders at various orientation angles (0°, 45°, 90°). The tests were conducted based on the design of experiment using the Box-Behnken model. These results were trained using the Levenberg-Marquardt back propagation model with hidden layers. Results indicate that the prediction of core compressive strength for the grade mixes is nearer for the two-level factorial design with R2 = 0.897, and the sum of squared error is found to be 0.9968.

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“…By acquiring two EMI readings at different measurement heights or employing two vertically-spaced coils, it is possible to simultaneously estimate the rebar diameter and cover thickness, but it is often inconvenient to scan in congested metal work areas and difficult to avoid the mutual interference of two sets of coils [ 10 ]. Neural networks were trained to estimate rebar diameter and cover thickness respectively, and the estimation accuracy satisfies the industrial standards [ 11 ]. Ultrasonic echo was combined with EMI measurement for mapping the meshed reinforced concrete and estimating the cover thickness, where better results were obtained than those from an alone EMI survey [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Rebar Diameter and Cover Thickness by a GPR-EMI Dual Sensor

Zhou

Chen

Hai

et al. 2018

Sensors

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Precise characterization of reinforcing bars (rebars) in a concrete structure is of significant importance for construction quality control and post-disaster safety evaluation. This paper integrates ground-penetrating radar (GPR) and electromagnetic induction (EMI) methods for simultaneous estimation of rebar diameter and cover thickness. A prototype of GPR-EMI dual sensor is developed, and a calibration experiment is conducted to collect a standard EMI dataset corresponding to various rebar diameters and cover thicknesses. The handheld testing cart can synchronously collect both GPR and EMI data when moving on the concrete surface, from which a data processing algorithm is proposed to simultaneously estimate the rebar diameter and cover thickness. Firstly, by extracting the apex of the hyperbolic reflection from the rebar in the preprocessed GPR profile, the rebar position is determined and further used to extract the effective EMI curve. Then, the rebar diameter and cover thickness are simultaneously estimated from the minimum mean square error between the measured and calibrated EMI data under the constraint of the GPR-estimated cover thickness. A laboratory experiment is performed using four casted concrete specimens with 11 embedded steel rebars. The results show that the diameters of 10 rebars are correctly estimated out of the 11 rebars, and the maximum estimation error for the cover thickness is 6.7%. A field trial is carried out in a newly-constructed building, and the diameters of four tested rebars are all accurately estimated while the estimation errors of the cover thickness are less than 5%. It is concluded that the developed GPR-EMI dual sensor and the proposed algorithm can estimate the rebar diameter and cover thickness accurately by a single scan.

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Extracting dimensional information from steel reinforcing bars in concrete using neural networks trained on data from an inductive sensor

Cited by 15 publications

References 5 publications

Lifecycle Management, Monitoring and Assessment for Safe Large-Scale Infrastructures: Challenges and Needs

Lifecycle Management, Monitoring and Assessment for Safe Large-Scale Infrastructures: Challenges and Needs

Assessment of core strength of concrete by artificial neural networks

Simultaneous Estimation of Rebar Diameter and Cover Thickness by a GPR-EMI Dual Sensor

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