Non-destructive neural identification of the bond between concrete layers in existing elements

Sadowski, Łukasz; Hoła, Jerzy; Czarnecki, Sławomir

doi:10.1016/j.conbuildmat.2016.09.146

Cited by 26 publications

(10 citation statements)

References 35 publications

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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%

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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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Section: Artificial Neural Networkmentioning

confidence: 99%

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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“…The results indicated that the ICA-ANN model outperforms the other methods, such as the GA and PSO. The R 2 values are also higher than those obtained previously by the simple MLP-ANN [29]. The obtained values of R 2 can be considered as satisfactory, taking into account the fact that it is not necessary to use the 3D roughness parameters of an existing concrete substrate surface.…”

Section: Discussionmentioning

confidence: 48%

“…The fb values were then used as patterns for learning and testing the ANN. The data used for development of the models was obtained from past experiments [29]. In this article, the output value of the pull-off adhesion predicted by the ANN is denoted as fc,b.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites

Sadowski

Nikoo

2017

Coatings

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Abstract:The article presents the hybrid metaheuristic-neural assessment of the pull-off adhesion in existing multi-layer cement composites using artificial neural networks (ANNs) and the imperialist competitive algorithm (ICA). The ICA is a metaheuristic algorithm inspired by the human political-social evolution. This method is based solely on the use of ANNs and two non-destructive testing (NDT) methods: the impact-echo method (I-E) and the impulse response method (IR). In this research, the ICA has been used to optimize the weights of the ANN. The combined ICA-ANN model has been compared to the genetic algorithm (GA) and particle swarm optimization (PSO) to evaluate its accuracy. The results showed that the ICA-ANN model outperforms other techniques when testing datasets in terms of both effectiveness and efficiency. As presented in the validation stage, it is possible to reliably map the adhesion level on a tested surface without local damage to the latter.

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“…Rough "adhesion maps" were also made based on results obtained using impact-echo and ultrasonic echo methods [17]. In turn, Sadowski and Hoła [18][19][20][21][22][23][24] proved that predicting the values of f b is possible based on non-destructive testing (NDT) using artificial neural networks (ANN). An evaluation at a lower level of observation mainly involved the observation of the interphase zone and its destruction, which occurred after tests using the pull-off method [25][26][27][28][29][30][31][32][33][34][35] and also bending [36][37][38][39][40], shear [26,34,39,[41][42][43][44][45][46][47][48][49][50][51][52][53][54], flexural [55,56], direct tensile [57,58], splitting prism [59], splitting tensile …”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Scale Evaluation of the Interphase Zone between the Overlay and Concrete Substrate: Methods and Descriptors

Sadowski

2017

Applied Sciences

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This article presents the problem of examining the interphase zone between the overlay and concrete substrate at different levels of observation. The possibility of applying available modern research methods in order to examine the interphase zone with regard to the level of observation is presented. These levels were defined in the paper. Examples of tests that show a possible approach to the examination of the interphase zone are also presented.

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Non-destructive neural identification of the bond between concrete layers in existing elements

Cited by 26 publications

References 35 publications

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

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

Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites

Multi-Scale Evaluation of the Interphase Zone between the Overlay and Concrete Substrate: Methods and Descriptors

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