Fracture monitoring of steel and <scp>GFRP</scp> reinforced concrete beams using acoustic emission and digital image correlation techniques

Sharma, Gaurav; Sharma, Shruti; Sharma, Sandeep

doi:10.1002/suco.202000650

Cited by 14 publications

(6 citation statements)

References 47 publications

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“…The inspection of FRP-RC elements is limited to detecting the initiation of FRP bars–concrete debonding [ 31 , 32 ] or the initiation of fractures in the FRP [ 33 , 34 ] rather than detecting the damage in the bars themselves. This is in most part because it was believed that FRP bars are undetectable or have low detectability, making it impossible to spot them effectively during an inspection.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in FRP-Reinforced Concrete Elements

Malla,

Khedmatgozar Dolati,

Ortiz

et al. 2024

Materials

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Fiber-Reinforced Polymer (FRP) composites have emerged as a promising alternative to conventional steel reinforcements in concrete structures owing to their benefits of corrosion resistance, higher strength-to-weight ratio, reduced maintenance cost, extended service life, and superior durability. However, there has been limited research on non-destructive testing (NDT) methods applicable for identifying damage in FRP-reinforced concrete (FRP-RC) elements. This knowledge gap has often limited its application in the construction industry. Engineers and owners often lack confidence in utilizing this relatively new construction material due to the challenge of assessing its condition. Thus, the main objective of this study is to determine the applicability of two of the most common NDT methods: the Ground-Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU) methods for the detection of damage in FRP-RC elements. Three slab specimens with variations in FRP type (glass-, carbon- and basalt-FRP, i.e., GFRP, CFRP, and BFRP, respectively), bar diameter, bar depths, and defect types were investigated to determine the limitations and detection capabilities of these two NDT methods. The results show that GPR could detect damage in GFRP bars and CFRP strands, but PAU was limited to damage detection in CFRP strands. The findings of this study show the applicability of conventional NDT methods to FRP-RC and at the same time identify the areas with a need for further research.

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Section: Introductionmentioning

confidence: 99%

Damage Detection in FRP-Reinforced Concrete Elements

Malla,

Khedmatgozar Dolati,

Ortiz

et al. 2024

Materials

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“…For elderly bone fracture patients with chronic diseases, medical splints serve as highly effective conservative treatment methods [1][2][3] . Nevertheless, traditional splints lack pressure detection functionality, resulting in doctors having to rely solely on experience when applying and adjusting the splints [4] .…”

Section: Introductionmentioning

confidence: 99%

Design of intelligent medical fracture splint with pressure detection and cloud database

Li,

Yan,

Feng

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Medical splint is a widely used conservative treatment method for fractures, offering numerous advantages such as low risk, reduced pain, and cost-effectiveness. However, the absence of pressure detection capability in traditional splints poses several limitations and challenges in the management of bone fractures. To address this issue, an innovative intelligent medical splint integrating pressure signal acquisition and cloud storage capabilities has been developed. The intelligent medical splint incorporates resistance strain gauges as pressure sensors, enabling accurate measurement of clamp pressure values. An amplification circuit is employed to enhance the accuracy of sampled signals. Simultaneously, the integration of database systems and internet cloud technology allows for the storage of collected pressure values in the cloud. These values are presented to users through a user-friendly graphical interface accessible via various devices such as computers and mobile phones. This ensures convenient access to patient data anytime and anywhere. To guarantee the long-term stability of the equipment, a watchdog circuit is implemented, significantly enhancing its reliability. Extensive human wearing experiments and rigorous testing have validated the functional efficiency and notable advantages of this design, demonstrating its compatibility with the requirements of fracture patients.

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“…The computer vision-based automated damage evaluation procedure in RC components starts with detecting and tracking the surface damages, including cracks, concrete spalling, and crushing. [22][23][24][25][26][27][28] Deep learning techniques are now frequently employed for the damage detection phase. [29][30][31][32][33] The objective of the second phase is to find a correlation between image-extracted features and structural damage.…”

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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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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Fracture monitoring of steel and GFRP reinforced concrete beams using acoustic emission and digital image correlation techniques

Cited by 14 publications

References 47 publications

Damage Detection in FRP-Reinforced Concrete Elements

Damage Detection in FRP-Reinforced Concrete Elements

Design of intelligent medical fracture splint with pressure detection and cloud database

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

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