Numerical Simulation-Based Damage Identification in Concrete

Vu, Giao; Timothy, Jithender J.; Singh, Divya Shyam; Saydak, Leslie; Saenger, Erik H.; Meschke, Günther

doi:10.3390/modelling2030019

Cited by 9 publications

(10 citation statements)

References 36 publications

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“…However, in the global space of scientific research and in practice, there is a deficit, expressed in a certain conservatism of the construction industry and the slow pace of the introduction of artificial intelligence methods and other innovative methods in this industry [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…The Convolutional Neural Network (CNN) has found applications in the construction industry, especially for monitoring and inspection purposes, in particular, to identify areas covered by fresh and young concrete [ 25 ] and predict the corresponding concrete damage class [ 20 ]. A novel approach to automatically extract defects in high-precision concrete surface images by combining the advantages of image processing and the deep convolutional neural network (DCNN) and attempting to evaluate concrete quality based on detection results has also shown good performance [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

et al. 2022

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Currently, one of the topical areas of application of artificial intelligence methods in industrial production is neural networks, which allow for predicting the performance properties of products and structures that depend on the characteristics of the initial components and process parameters. The purpose of the study was to develop and train a neural network and an ensemble model to predict the mechanical properties of lightweight fiber-reinforced concrete using the accumulated empirical database and data from construction industry enterprises, and to improve production processes in the construction industry. The study applied deep learning and an ensemble of regression trees. The empirical base is the result of testing a series of experimental compositions of fiber-reinforced concrete. The predicted properties are cubic compressive strength, prismatic compressive strength, flexural tensile strength, and axial tensile strength. The quantitative picture of the accuracy of the applied methods for strength characteristics varies for the deep neural network method from 0.15 to 0.73 (MAE), from 0.17 to 0.89 (RMSE), and from 0.98% to 6.62% (MAPE), and for the ensemble of regression trees, from 0.11 to 0.62 (MAE), from 0.15 to 0.80 (RMSE), and from 1.30% to 3.4% (MAPE). Both methods have shown high efficiency in relation to such a hard-to-predict material as concrete, which is so heterogeneous in structure and depends on many factors. The value of the developed models lies in the possibility of obtaining additional useful information in the process of preparing highly functional lightweight fiber-reinforced concrete without additional experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

et al. 2022

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“…This heterogeneity is particularly important for the propagation of elastic waves in concrete. In the past there have been several numerical studies where elastic wave propagation in concrete-like media have been investigated [1] [2] [3]. The transducers used for the non-destructive tests often are Dry Point Contact (DPC) transducers, which have proven to be very efficient transmitters and receivers for ultrasonic testing of concrete in the past [4].…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of near and far field ultrasound transducer radiation characteristics in concrete

Dethof¹,

Rabe²,

Keßler³

2022

eJNDT

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Concrete is the most frequently used material in civil engineering. Therefore, it also plays an im-portant role for nondestructive testing methods like ultrasonic testing. In the past Dry Point Contact transducers have proven to be very efficient transmitters and receivers for ultrasonic testing of concrete. The individual element consists of a spring-loaded ceramic tip, driven by a piezoelectric bimorph and oscillating either normally or laterally with respect to the component surface. In order to predict the resulting sound fields quantitatively, appropriate modeling is required. Analytic formulas for the wave fields derived from elastic wave theory are available, however, they usually only cover bulk waves (body waves) in the far field and discrepancies between calculated and measured di-rectivity patterns are reported in the literature. We therefore conduct numerical simulations using the staggered-grid finite difference code EFIT to investigate radiation patterns of US transducers cover-ing surface and bulk waves in the near and the far field. The simulations are performed in a strongly heterogeneous, two-phased medium representing the material parameters and the mesostructure of concrete. With this model, the effects of damping as well as scattering at pores and aggregates on radiation characteristics shall be investigated in future. In order to validate EFIT results, measure-ments were conducted on a concrete specimen with an embedded tube as a test feature. As will be shown, a good agreement between measurements and simulations was found. During the evaluation of the simulation and measurement results focus was put on surface waves because a significant part of the wave field’s energy goes into the surface modes. Ultimately, these studies might be used for US transducer sensitivity analysis as well as for US testing reliability assessments in the future.

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“…This approach was successfully used in several works (e.g. [8][9][10][11][12][13][14][15]). The effectiveness of the diffuse ultrasound method for micro-cracking detection in concrete was studied by Ahn et al [11].…”

Section: Introductionmentioning

confidence: 99%

Coda wave interferometry for monitoring the fracture process of concrete beams under bending test

Knak,

Wojtczak,

Rucka

2022

Bulletin of the Polish Academy of Sciences Technical Sciences

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Early detection of damage is necessary for the safe and reliable use of civil engineering structures made of concrete. Recently, the identification of micro-cracks in concrete has become an area of growing interest, especially using wave-based techniques. In this paper, a non-destructive testing approach for the characterization of the fracture process was presented. Experimental tests were made on concrete beams subjected to mechanical degradation in a 3-point bending test. The ultrasonic waves were registered on a specimen surface by piezoelectric transducers located at several points. Then, the signals were processed taking advantage of the wave scattering due to micro-cracks disturbances. For early-stage damage detection, coda wave interferometry was used. The novelty of the work concerns the application of the complex decorrelation matrix and the moving reference trace approach for better distinguishment of sensors located at different parts of a crack zone. To enhance coda wave-based damage identification results, optical imaging of crack development was performed by digital image correlation measurement. Obtained results showed that the coda wave interferometry technique can be successfully used as a quantitative measure of changes in the structure of concrete. The results also indicated that the course of decorrelation coefficient curves enabled the identification of three stages during degradation and it depended on the location of acquisition points regarding the crack zone.

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Numerical Simulation-Based Damage Identification in Concrete

Cited by 9 publications

References 36 publications

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

Numerical investigation of near and far field ultrasound transducer radiation characteristics in concrete

Coda wave interferometry for monitoring the fracture process of concrete beams under bending test

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