Long-term ultrasonic monitoring of concrete affected by alkali-silica reaction

Sun, Hongbin; Tang, Yalei; Malone, Clayton; Zhu, Jinying

doi:10.1177/14759217231169000

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…This result is consistent with previous findings for resonance frequency testing on ASR damaged concrete prisms by Malone et al [21], Rivard and Saint-Pierre [22], and Giannini et al [23]. A study of monitoring the relative ultrasonic velocity change in the same group of specimens by Sun et al [24] also indicated saturation at a velocity drop of 20%.…”

Section: Linear Impact-echo Resultssupporting

confidence: 91%

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Malone,

Sun,

Zhu

2023

J Nondestruct Eval

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Nonlinear acoustic methods demonstrate high sensitivity for concrete damage evaluation. Among these methods, nonlinear resonance acoustic methods have been widely used in laboratory tests on small scale test samples. In this study, a nonlinear impact-echo (IE) method was proposed for concrete damage evaluation, in which the IE frequency shift was measured at different impact force levels. The nonlinear IE test is similar to the nonlinear impact resonance acoustic spectroscopy (NIRAS) test in the experimental setup and analysis method. However, the nonlinear IE test excites a local thickness resonance mode of a member instead of the global resonance vibration. Therefore, the analyzed mode is not affected by boundary conditions and can be applied to large concrete structural members. To identify the fundamental IE frequency, multiple impacts at different locations were used. The nonlinear IE method was then validated on seven concrete beam specimens with different levels of alkali-silica reaction (ASR) damage. The nonlinear IE test results demonstrate high sensitivity to concrete damage and allow for quantitative assessment of the damage state of concrete without a baseline measurement.

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Section: Linear Impact-echo Resultssupporting

confidence: 91%

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Malone,

Sun,

Zhu

2023

J Nondestruct Eval

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“…As ASR progresses, the amplitude and velocity of ultrasonic waves propagating through the concrete decrease while the wave attenuation increases. Multiple studies have implemented ultrasonic NDE for long-term monitoring of the ASR damage development in concrete and have proven the success of ultrasonic testing techniques for assessing a concrete structure that has developed ASR damage [2], [7], [8], [9], [10].…”

Section: List Of Figuresmentioning

confidence: 99%

“…Data were not collected during the conditioning chamber shutdowns. More details about the ultrasonic monitoring system can be found in other works by Sun [7], [39]. Overall, 644 ultrasonic signals were collected from the ASR specimen, and 620 signals were collected from the ASR-2D specimen.…”

Section: Concrete Specimens and Ultrasonic Data Collectionmentioning

confidence: 99%

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Sun,

Sabatino

2024

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Alkali-silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material's structural integrity and the overall structure's lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer's subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI-assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated.The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.Additionally, this work studied the effect of temperature on model performance, revealing significant prediction errors when the temperature of testing data differed from that of training data. Furthermore, this study examined training and testing datasets from two distinct specimen batches, revealing the SVR model's superior generalization ability compared with DNN when considering data from different batches. Lastly, DNN models were trained and tested using both time-domain signals and frequency spectra. The results showed potential for achieving high regression performance (e.g., the ML model was able to predict ASR expansion with relatively high accuracy), although further efforts in model tuning and data preprocessing are still requir...

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Continuous Monitoring of the Early-Age Strength Development of Cement Paste Using Embeddable Piezoelectric-Based Ultrasonic Transducers

Narayanan,

Duddi,

Kocherla

et al. 2024

Lecture Notes in Civil Engineering

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Long-term ultrasonic monitoring of concrete affected by alkali-silica reaction

Cited by 4 publications

References 34 publications

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Continuous Monitoring of the Early-Age Strength Development of Cement Paste Using Embeddable Piezoelectric-Based Ultrasonic Transducers

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