Non-destructive identification of pull-off adhesion between concrete layers

Sadowski, Łukasz

doi:10.1016/j.autcon.2015.06.004

Cited by 29 publications

(9 citation statements)

References 47 publications

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“…Machine learning as a means of improving the IE method has been less explored, compared to signal processing. A few studies involve using different machine learning techniques, for example, artificial neural networks [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ] and support vector machine (SVM) [ 35 ]. In [ 36 ], a Bayesian classifier was used for diagnosing internal defects of metal element using the IE technique.…”

Section: Related Workmentioning

confidence: 99%

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

Zhang

Yan

Cui

2016

Sensors

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The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

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Section: Related Workmentioning

confidence: 99%

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

Zhang

Yan

Cui

2016

Sensors

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“…In ANNs, generally, experimental datasets are used to attain relationships between input and output parameters. In this method, the large number of databases is one of the requirements to get the results with less error [17][18][19][20][21][22]37].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Hence, the application of neural network modeling and evolutionary polynomial regressions (EPRs), which are believed to be common ways to accurately and timely predict engineering complicated functions, can be examined. Different attempts to apply neural networks and EPRs to model different civil and geotechnical problems are presented in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. They are well-applied in a wide range of problems from deep soil stabilizations, concrete, and their related structures, compressive strength of soils, rocks, and stabilized samples, bearing capacity of shallow and deep foundations, lateral spreading, rock mechanics, rock engineering, and soil mechanics [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using threedimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile's configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

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“…The early systems commonly tackle echo investigation problem with statistical pattern classification, in which the echo spectra has been used as feature vector and various conventional classifiers have been employed, such as Gaussian mixture models (GMM) [11], Artificial Neural Network (ANN) [12] and Support Vector Machines (SVM) [13], to characterize discriminant information of healthy/defective echoes. In recent years, significant progress has been made in noise robust echo feature representation learning.…”

Section: Impact-echo Methods and Air-coupled Hammering Inspectionmentioning

confidence: 99%

Computerized Data Interpretation for Concrete Assessment with Air-Coupled Impact-Echo: An Online Learning Approach

Ye¹,

Kobayashi²,

Iwata³

et al. 2018

Preprint

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Developing efficient Artificial Intelligence (AI)-enabled system to substitute human role in 1 non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel 2 impact-echo analysis system using online machine learning, which aims at achieving near-human 3 performance for assessment of concrete structures. Current computerized impact-echo systems 4 commonly employ lab-scale data to validate the models. In practice, however, the echo patterns can 5 be far more complicated due to varying geometric shapes and materials of structures. To deal with 6 a large variety of unseen data, we propose a sequential treatment for echo characterization. More 7 specifically, the proposed system can adaptively update itself to approaching human performance in

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Non-destructive identification of pull-off adhesion between concrete layers

Cited by 29 publications

References 47 publications

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Computerized Data Interpretation for Concrete Assessment with Air-Coupled Impact-Echo: An Online Learning Approach

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