Improved Ultrasonic Computerized Tomography Method for STS (Steel Tube Slab) Structure Based on Compressive Sampling Algorithm

Jiang, Biwang; Zhao, Wen; Wang, Wentao

doi:10.3390/app7050432

Cited by 14 publications

(11 citation statements)

References 31 publications

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“…In this paper, the sparsest solution is solved by the ℓ1-minimization optimization algorithm as a convex optimization toolbox embedded in MATLAB (available at: http://cvxr.com/cvx/ ). It should be noted that the given CS algorithm would be more effective if a Lagrange multiplier was used in the imaging stage [ 31 , 32 ]; thereby, Eq ( 10 ) will change to Eq ( 11 ): where is the reconstructed slowness and λ is the Lagrange multiplier, which balances the ratio between the data misfit (‖ΘΔs − y‖ 2 ) and the model constraint (‖Δs‖ 1 ) in the optimization equation.…”

Section: Compressive Sampling Algorithmmentioning

confidence: 99%

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS)

et al. 2018

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This paper explores a new method for rapid structural damage inspection of steel tube slab (STS) structures along randomly measured paths based on a combination of compressive sampling (CS) and ultrasonic computerized tomography (UCT). In the measurement stage, using fewer randomly selected paths rather than the whole measurement net is proposed to detect the underlying damage of a concrete-filled steel tube. In the imaging stage, the ℓ1-minimization algorithm is employed to recover the information of the microstructures based on the measurement data related to the internal situation of the STS structure. A numerical concrete tube model, with the various level of damage, was studied to demonstrate the performance of the rapid UCT technique. Real-world concrete-filled steel tubes in the Shenyang Metro stations were detected using the proposed UCT technique in a CS framework. Both the numerical and experimental results show the rapid UCT technique has the capability of damage detection in an STS structure with a high level of accuracy and with fewer required measurements, which is more convenient and efficient than the traditional UCT technique.

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Section: Compressive Sampling Algorithmmentioning

confidence: 99%

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS)

et al. 2018

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“…The higher the weights, the higher the impact of the input node. It is used for modeling on prediction or estimation of strength of capacity of structures [23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Artificial Neural Network In Structural Engineering and Matementioning

confidence: 99%

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

2018

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An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch-catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson's Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content.

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“…There are many tomographic inversion algorithms based on the channel wave's travel time, including Back Projection Technique (BPT), Algebraic Reconstruction Technique (ART), Simultaneous Iterative Reconstructive Technique (SIRT), and Least Square QR-factorization (LSQR) (where Q is an orthogonal matrix and R is a triangular one) [15][16][17]. Among them, the SIRT algorithm is the most widely used in seismic tomography inversion, because it is more suitable for sparse, irregular, and low signal-to-noise ratio (SNR) measured data, and thus it is easy to add prior information [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm

et al. 2019

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Precise prediction of coal thickness is of the utmost importance in realizing intelligent and unmanned mining. As the channel wave is characterized by an easily recognizable waveform, a long propagation distance, and strong energy, it is widely used for coal thickness inversion. However, most traditional inversion methods are local in nature, and the inversion result is probably not optimal in the global scope. This paper introduces the GA-SIRT hybrid approach, which combines Genetic Algorithms (GA) and Simultaneous Iterative Reconstructive Techniques (SIRT) in order to deal with the above problem and to improve the accuracy of coal thickness inversion. The proposed model takes full advantage of the strong global search capability of GA and of the fast local convergence rate of the SIRT. Moreover, it inhibits the poor local search ability and the local optimal value effect of the GA and the SIRT respectively. The application of the GA-SIRT in the Guoerzhuang coal mine has significantly enhanced its accuracy, stability, and overall computational efficiency. Hence, the introduced novel hybrid model can precisely resolve and identify the coal thickness according to the channel wave. It can also be extended to other geophysical tomographic inversion problems towards the reduction of potential local optimal solutions.

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Improved Ultrasonic Computerized Tomography Method for STS (Steel Tube Slab) Structure Based on Compressive Sampling Algorithm

Cited by 14 publications

References 31 publications

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS)

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS)

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm

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