Bridge Performance Assessment Based on an Adaptive  Neuro-Fuzzy Inference System with Wavelet Filter  for the GPS Measurements

Kaloop, Mosbeh R.; Hu, Jong Wan; Sayed, Mohamed A.

doi:10.3390/ijgi4042339

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…Based on (4) and (11), to estimate the element ( = − 1), the DANFIS output is calculated as follows [14,22]:…”

Section: Input Layer Input Mf Layermentioning

confidence: 99%

“…Also, it is found that the nonlinear Hammerstein-Wiener model performance is better than the linear one [10]. On the other hand, the adaptive neurofuzzy inference system (ANFIS) is used widely for the designing of prediction's models; more details on the ANFIS model design and previous studies can be found in [11][12][13][14]. The performance of the ANFIS model is better with MISO variables [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Kaloop

Elbeltagi

2017

Shock and Vibration

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This study investigates predicting the pullout capacity of small ground anchors using nonlinear computing techniques. The input-output prediction model for the nonlinear Hammerstein-Wiener (NHW) and delay inputs for the adaptive neurofuzzy inference system (DANFIS) are developed and utilized to predict the pullout capacity. The results of the developed models are compared with previous studies that used artificial neural networks and least square support vector machine techniques for the same case study. The in situ data collection and statistical performances are used to evaluate the models performance. Results show that the developed models enhance the precision of predicting the pullout capacity when compared with previous studies. Also, the DANFIS model performance is proven to be better than other models used to detect the pullout capacity of ground anchors.

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“…Based on (4) and (11), to estimate the element ( = − 1), the DANFIS output is calculated as follows [14,22]:…”

Section: Input Layer Input Mf Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Kaloop

Elbeltagi

2017

Shock and Vibration

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“…Structural engineers are trying to seek some new test methods to overcome these challenges. Currently, the technique of Global Positioning System-Real Time Kinematic (GPS-RTK) is widely used in SHM (see, e.g., [3][4][5]). However, vibration signals collected by GPS-RTK are often embedded in various noises.…”

Section: Introductionmentioning

confidence: 99%

Denoising GPS‐Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

2017

Mathematical Problems in Engineering

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High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD) based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP) is proposed to denoise Global Positioning System-(GPS-) based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs) with different characteristics. Second, highfrequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.

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“…Therefore, the number and type of MF are an important parameter in identifying the model selected. The second model is called ANFIS model, which is known as the best models used in previous research in controlled structures [1,12,24,25]. The number and type of MF and input time-delayed are main parameters for the ANFIS model as presented in [1].…”

Section: Training Setting Evaluationmentioning

confidence: 99%

“…In addition, four common MF types are evaluated in Table 3, and it can be seen that the sigmoid function has a good performance. The sigmoid function is presented in Equation (12).…”

Section: Training Setting Evaluationmentioning

confidence: 99%

Identification of the Response of a Controlled Building Structure Subjected to Seismic Load by Using Nonlinear System Models

Kaloop

Bigdeli

2016

Applied Sciences

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Abstract:The present study investigates the prediction efficiency of nonlinear system-identification models, in assessing the behavior of a coupled structure-passive vibration controller. Two system-identification models, including Nonlinear AutoRegresive with eXogenous inputs (NARX) and adaptive neuro-fuzzy inference system (ANFIS), are used to model the behavior of an experimentally scaled three-story building incorporated with a tuned mass damper (TMD) subjected to seismic loads. The experimental study is performed to generate the input and output data sets for training and testing the designed models. The parameters of root-mean-squared error, mean absolute error and determination coefficient statistics are used to compare the performance of the aforementioned models. A TMD controller system works efficiently to mitigate the structural vibration. The results revealed that the NARX and ANFIS models could be used to identify the response of a controlled structure. The parameters of both two time-delays of the structure response and the seismic load were proven to be effective tools in identifying the performance of the models. A comparison based on the parametric evaluation of the two methods showed that the NARX model outperforms the ANFIS model in identifying structures response.

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Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Cited by 9 publications

References 29 publications

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques

Denoising GPS‐Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

Identification of the Response of a Controlled Building Structure Subjected to Seismic Load by Using Nonlinear System Models

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