Localization of cracks in cementitious materials under uniaxial tension with electrical resistance tomography

Zhou, Xiaoyong; Bhat, Prakash; Ouyang, Hui; Yu, Jiagan

doi:10.1016/j.conbuildmat.2017.01.128

Cited by 12 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detailed procedure of current injection and voltage measurement for the 16-electrode ERT system can be found in ref. 30 The working principle of ERT is shown in Figure 4b. It consists of a data collection system and a data processing system.…”

Section: The Data Processing Is Mainly Based On An Open-source Softwa...mentioning

confidence: 99%

“…The detailed procedure of current injection and voltage measurement for the 16-electrode ERT system can be found in ref. 30…”

Section: Specimen Preparation Configuration and Instrumentationmentioning

confidence: 99%

“…Gupta et al 29 designed and validated distributed strain monitoring using a patterned nanocomposite “sensor network” combined with electrical impedance tomography (EIT). Our previous work 30 has demonstrated that the difference ERT could be used to detect the position and approximate range of cracks that emerge in concrete during uniaxial tensile progress.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Damage detection in cementitious materials with optimized absolute electrical resistance tomography

Zhou

Wang

et al. 2022

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

Electrical resistance tomography (ERT) serves as a non-invasive, non-destructive, non-radioactive imaging technique. It has potential applications in industrial and biological imaging. This paper presents an optimized inverse algorithm, named Newton’s Constrained Reconstruction Method (NCRM), to detect damage in cementitious materials. Several constraints were utilized in the proposed algorithm to optimize initial parameters. The range and spatial distribution of conductivities within the sample were chosen as two main constraints. Two sets of numerical and a set of experimental voltage data were used to reconstruct conductivity distribution images based on this algorithm. To evaluate the quality of reconstructed images, two image quality evaluation indicators, correlation coefficient and position error were used. Results show that the proposed algorithm NCRM has the ability to enhance the reconstructed image quality with fewer artifacts and has better positioning accuracy.

show abstract

Section: The Data Processing Is Mainly Based On An Open-source Softwa...mentioning

confidence: 99%

“…The detailed procedure of current injection and voltage measurement for the 16-electrode ERT system can be found in ref. 30…”

Section: Specimen Preparation Configuration and Instrumentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Damage detection in cementitious materials with optimized absolute electrical resistance tomography

Zhou

Wang

et al. 2022

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, previous studies have shown that electric impedance spectroscopy (EIS) is relatively inexpensive and can be applied on concrete elements to detect cracks to include their width/depth, reinforcement and internal moisture. 10,11 On the other hand, electrical tomography, more specifically electrical resistance tomography (ERT, a specific electrical tomography modality), has been recently demonstrated as an effective modality for detecting simple and complex cracking patterns in concrete elements 9,[12][13][14] ; meanwhile, ERT has low experimental costs, energy consumption, fast data collection, high temporal resolution and potential of continuous spatial monitoring. 15 However, the potential disadvantages of ERT include its lower spatial resolution compared with other contemporary modalities and (traditionally) high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic cracking prediction via deep learned electrical tomography

Chen

Gallet

Huang

et al. 2021

Structural Health Monitoring

View full text Add to dashboard Cite

In recent years, electrical tomography, namely, electrical resistance tomography (ERT), has emerged as a viable approach to detecting, localizing and reconstructing structural cracking patterns in concrete structures. High-fidelity ERT reconstructions, however, often require computationally expensive optimization regimes and complex constraining and regularization schemes, which impedes pragmatic implementation in Structural Health Monitoring frameworks. To address this challenge, this article proposes the use of predictive deep neural networks to directly and rapidly solve an analogous ERT inverse problem. Specifically, the use of cross-entropy loss is used in optimizing networks forming a nonlinear mapping from ERT voltage measurements to binary probabilistic spatial crack distributions (cracked/not cracked). In this effort, artificial neural networks and convolutional neural networks are first trained using simulated electrical data. Following, the feasibility of the predictive networks is tested and affirmed using experimental and simulated data considering flexural and shear cracking patterns observed from reinforced concrete elements.

show abstract

“…Hou et al utilized the cementitious concrete with polymeric fibers to detect and localize cracks by employing an electrical impedance tomography method [40], and Downey et al demonstrated the applicability of crack detection and localization using nanocomposite cement-based beams [41,42]. Also, Zhou et al presented an electrical resistance tomography technique for localizing and detecting cracks in a concrete structure [43]. Since the electrical response is, however, sensitively affected by environmental variation [44], the only crack-induced feature should be carefully extracted from the measured electrical responses.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Electrical Impedance-Based Crack Localization of Fiber-Reinforced Cementitious Composites under Bending Conditions

et al. 2018

View full text Add to dashboard Cite

This study proposes a multi-channel electrical impedance-based crack localization technique of fiber-reinforced cementitious composites (FRCCs) under bending conditions. FRCCs have a self-sensing capability by adding conductive steel fibers into nonconductive cementitious composites, making it possible to measure electrical impedance without sensor installation. Moreover, FRCCs materials can be used as a structural member thanks to its own enhanced structural ductility as well as stiffness. In a structural health monitoring point of view, these characteristics make FRCCs suitable for monitoring structural hot spots, particularly where the crack is most likely to be initiated. Since the electrical impedance obtained from FRCCs is typically sensitive to environmental and operational conditions, false alarms are often triggered. The proposed technique can minimize the false alarms by using currently measured multi-path data as well as localize a crack within the sensing range. To examine the feasibility of crack localization in FRCCs, an instantaneous multi-channel electrical impedance acquisition system and a crack localization algorithm are developed. Subsequently, three-point bending tests are carried out under various temperature conditions. The validation test results reveal that cracks are successfully identified and localized even under varying temperature conditions.

show abstract

Localization of cracks in cementitious materials under uniaxial tension with electrical resistance tomography

Cited by 12 publications

References 23 publications

Damage detection in cementitious materials with optimized absolute electrical resistance tomography

Damage detection in cementitious materials with optimized absolute electrical resistance tomography

Probabilistic cracking prediction via deep learned electrical tomography

Multi-Channel Electrical Impedance-Based Crack Localization of Fiber-Reinforced Cementitious Composites under Bending Conditions

Contact Info

Product

Resources

About