Predicting crack widths in FRC/reinforced concrete members using small deformation post‐crack parameters

Bernard, E. S.

doi:10.1002/suco.201900083

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…The development and propagation of surface cracks, along with concrete spalling and crushing which necessitates the use of various MoRs, is the most obvious visual sign of damage in RCSWs. [12][13][14][15][16][17][18][19][20][21] Therefore, crack detection in RCSWs is a crucial phase in figuring out the level of damage in RC buildings after an earthquake.…”

Section: Introductionmentioning

confidence: 99%

“…The damage level detection of RCSWs, as a lateral‐force‐resisting element in reinforced concrete (RC) structures, is important for assessing post‐earthquake stability and serviceability of buildings. The development and propagation of surface cracks, along with concrete spalling and crushing which necessitates the use of various MoRs, is the most obvious visual sign of damage in RCSWs 12–21 . Therefore, crack detection in RCSWs is a crucial phase in figuring out the level of damage in RC buildings after an earthquake.…”

Section: Introductionmentioning

confidence: 99%

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Vision‐based probabilistic post‐earthquake loss estimation for reinforced concrete shear walls

Azhari,

Hamidia,

Rouhani

2024

Structural Concrete

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In this paper, a probabilistic methodology based on image analysis is proposed for earthquake‐induced loss estimation in rectangular reinforced concrete shear walls (RCSWs) in compliance with FEMA P‐58. The methodology is developed using a databank of 285 surface crack patterns gathered from the result of experimental research on 99 rectangular RCSWs with wide‐ranging structural and geometric characteristics. For the complexity extraction, three fractal geometry indices of the damaged specimens are taken into consideration. Fragility functions are generated to determine the probability of exceeding a FEMA P‐58 compliant damage state based on the various methods of repair for specific complexity indices. The fragility functions are obtained using four common probabilistic distributions including lognormal, Weibull, gamma, and beta. The fitness of the fragility curves is examined by two goodness‐of‐fit tests, namely the Kolmogorov–Smirnov test and Lilliefors test. Results show that the Weibull is the best‐fitting distribution for the majority of damage states and succolarity possesses the best results in goodness‐of‐fit tests among fractal geometry indices. Empirical loss indices are also derived in this paper aimed at optimizing the goodness‐of‐fits. Finally, the proposed procedure is utilized for a sample specimen from the databank at various damage levels.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision‐based probabilistic post‐earthquake loss estimation for reinforced concrete shear walls

Azhari,

Hamidia,

Rouhani

2024

Structural Concrete

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“…18,19 The surface crack maps are crucial gauge of the damage for the RC components. [20][21][22][23][24][25][26][27] The automated image-based damage quantification for RC components is categorized into two main steps. In the first step, the surface crack patterns and crushing zones are identified and documented.…”

Section: Introductionmentioning

confidence: 99%

“…The surface crack maps are crucial gauge of the damage for the RC components 20–27 . The automated image‐based damage quantification for RC components is categorized into two main steps.…”

Section: Introductionmentioning

confidence: 99%

Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns

Hamidia

Afzali

Jamshidian

et al. 2023

Structural Concrete

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The seismic damage to reinforced concrete (RC) components is conventionally quantified through various damage indices. In this paper, an image‐based procedure is developed for post‐earthquake residual stiffness quantification of RC columns. The proposed scenario‐based methodology is built upon the multifractal indices of the surface crack maps of the seismically damaged RC columns as the complexity measure for the images. An extensive databank from experiments on RC column specimens with a broad range of structural characteristics are collected and employed for generation and validation of the proposed procedure. Based on the availability of the structural information, five closed‐form regression‐aided equations are offered for estimating the stiffness loss in seismically damaged RC columns using predicting parameters among fractal indices, concrete strength, and the aspect ratio of the column. Results reveal that multifractal dimensions of crack maps are largely correlated with the stiffness degradation in RC columns. A sample specimen at various stiffness‐based damage indices is also presented as a case study to evaluate the predicted versus measured damage indices. The updated stiffness values acquired by the proposed equations can be utilized for stability evaluation, system identification, or further analysis of the seismically damaged RC buildings following an earthquake.

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“…Moreover, existing techniques, for the most part, provide partial solutions to the monitoring requirements of the concrete industry. For example, LVDT sensors will provide insufficient information regarding the cause of the observed displacement (Subramanian and Murugesan, 2019), and microscopes can only observe and measure localised surface deformations without any indication of below surface deformations (Bernard, 2019).…”

Section: Introductionmentioning

confidence: 99%

Real-time structural health monitoring for concrete beams: a cost-effective ‘Industry 4.0’ solution using piezo sensors

Ghosh

Edwards

Hosseini

et al. 2020

IJBPA

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PurposeThis research paper adopts the fundamental tenets of advanced technologies in industry 4.0 to monitor the structural health of concrete beam members using cost-effective non-destructive technologies. In so doing, the work illustrates how a coalescence of low-cost digital technologies can seamlessly integrate to solve practical construction problems.Design/methodology/approachA mixed philosophies epistemological design is adopted to implement the empirical quantitative analysis of “real-time” data collected via sensor-based technologies streamed through a Raspberry Pi and uploaded onto a cloud-based system. Data was analysed using a hybrid approach that combined both vibration-characteristic-based method and linear variable differential transducers (LVDT).FindingsThe research utilises a novel digital research approach for accurately detecting and recording the localisation of structural cracks in concrete beams. This non-destructive low-cost approach was shown to perform with a high degree of accuracy and precision, as verified by the LVDT measurements. This research is testament to the fact that as technological advancements progress at an exponential rate, the cost of implementation continues to reduce to produce higher-accuracy “mass-market” solutions for industry practitioners.Originality/valueAccurate structural health monitoring of concrete structures necessitates expensive equipment, complex signal processing and skilled operator. The concrete industry is in dire need of a simple but reliable technique that can reduce the testing time, cost and complexity of maintenance of structures. This was the first experiment of its kind that seeks to develop an unconventional approach to solve the maintenance problem associated with concrete structures. This study merges industry 4.0 digital technologies with a novel low-cost and automated hybrid analysis for real-time structural health monitoring of concrete beams by fusing several multidisciplinary approaches into one integral technological configuration.

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Predicting crack widths in FRC/reinforced concrete members using small deformation post‐crack parameters

Cited by 5 publications

References 7 publications

Vision‐based probabilistic post‐earthquake loss estimation for reinforced concrete shear walls

Vision‐based probabilistic post‐earthquake loss estimation for reinforced concrete shear walls

Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns

Real-time structural health monitoring for concrete beams: a cost-effective ‘Industry 4.0’ solution using piezo sensors

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