Artificial neural network-based fully data-driven models for prediction of newmark sliding displacement of slopes

Nayek, Partha Sarathi; Gade, Maheshreddy

doi:10.1007/s00521-022-06945-8

Cited by 18 publications

(14 citation statements)

References 31 publications

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“…Additionally, key components of the network include transfer and error functions (Wang and Wang, 2003). In this developed model, it was observed that a single layer of hidden neurons yields satisfactory performance, consistent with prior studies (Nayek and Gade, 2022). The transfer function between the input-hidden and hidden-output layers is hyperbolic tangent and linear, respectively, following the approach of Dhanya and Raghukanth (2018).…”

Section: Model Developmentsupporting

confidence: 78%

ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra

Sharma,

Arya,

Gade

et al. 2024

Preprint

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Vertical ground motion plays a crucial role in the design of critical structures, with recorded vertical ground motions often surpassing their horizontal counterparts in the near-field. This study proposes a new simplified Ground Motion Model (GMM) for vertical spectra by combining comprehensive datasets from the NESS and NGA-West2 databases. The proposed Artificial Neural Network (ANN) architecture-based model requires only 288 unknowns to predict spectral accelerations ($Sa$) at 33 distinct periods ranging from 0 to 4 seconds. Notably, this model inherently captures known physical phenomena with reduced variability using a minimum number of unknowns compared to the GMMs existing literature, thus offering a valuable addition to current hazard estimation frameworks. Furthermore, recognizing the necessity for physics-based simulations in vertical ground motion analysis, we introduce a novel physics-based broadband model for vertical spectra using ANN methodology. The proposed broadband model exhibits better robustness due to the comprehensiveness of the dataset utilized and the inclusion of source path and site characteristics at the input layer. Additionally, the model effectively captures the physical trends with minimal deviation. Further, a comparison of the model's output with physics-based simulations for the 2008 Mw 6.9 Iwate earthquake and recorded data showcase its ability to accurately capture the non-ergodic nature of ground motion. The proposed models serve as essential tools for physics-based broadband simulations and hazard assessments in active shallow crustal regions.

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Section: Model Developmentsupporting

confidence: 78%

ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra

Sharma,

Arya,

Gade

et al. 2024

Preprint

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show abstract

“…The initial step in generating a neural network was deciding the functional form such that it captures the buried features within the data with minimal variance. For this purpose, several trials were performed using different combinations of input variables and transfer functions (Nayek and Gade, 2022). The corresponding details are included in the sullplementary material.…”

Section: Spectramentioning

confidence: 99%

New generalized ANN-based hybrid broadband response spectra generator using physics-based simulations

et al. 2022

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To evaluate the seismic risk associated with infrastructures, site-specific seismic hazard studies are needed to be performed. Further, for nonlinear time history analysis, one requires broadband ground motion. In modern times using physics-based simulations (PBS) for deriving the ground motion for future earthquakes has got due consideration. The PBS helps in decreasing the uncertainties related to the hazard estimation compared to GMPEs. There is a certain threshold to the PBS methods with the limited computational facilities. Hence, certain hybrid methods are required to attain a Broadband spectra for the simulated ground motion. This study uses a new ANN-based model to generate broadband ground motion spectra using the low-frequency spectral acceleration from PBS, source, path, and site parameters as input variables.A detailed parametric study and performance evaluation was made to 1 Springer Nature 2021 L A T E X template 2 ANNHBB identify the optimal input parameters in conjunction with the best-suited ANN architecture. The performance of the ANN model is demonstrated for Iwate (Mw 6.9,2008) earthquake. We found that the predicted values from the developed ANN model are in good agreement with the recorded data. Furthermore, time histories are generated using the spectral ordinate matching technique from the estimated broadband spectra.

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“…Among them, dendrites are the input layer of cells in the brain, which senses the signals transmitted through a large number of terminal factors; cell bodies, as the processing central system, have the ability to process data; axons, as the output layer of brain cells, transmit the data processed by cell bodies to the next neuron. Neurons relay way into a state of excitement and inhibition, when entering the urge to make potentials in the membrane potential reaches a certain value, the neurons in the excited state, in accordance with the "dendritic cell body, axons", this value is about 40 mv, when did not reach the numerical input impulse, inhibitory neurons, Does not transmit these nerve impulses [17][18]. The neuron model is shown in Figure 1.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Landslide Risk Assessment Based on Artificial Neural Network Model

2021

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According to the public data, the overall trend of economic loss caused by landslide disaster is growing year by year, and improving the prediction level of landslide disaster can effectively reduce the loss caused by landslide disaster. Therefore, the study of landslide prediction and prediction has important practical significance, and landslide displacement prediction is an important content of landslide prediction and prediction. This paper mainly studies landslide risk assessment based on artificial neural network model. In this paper, based on the results of field investigation, literature summary and expert experience, collected samples of 100 landslides, analyzes the distribution and the main influence factors of landslide, and establishes the surface elevation, and vegetation index, cutting slope, annual average rainfall, surface density, overlying soil types for the impact factor of regional landslide hazard assessment system. According to the requirement of BP neural network (BPNN) for input data, the landslide sample data processing method which is suitable for Sichuan landslide risk assessment system is established.

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Artificial neural network-based fully data-driven models for prediction of newmark sliding displacement of slopes

Cited by 18 publications

References 31 publications

ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra

ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra

New generalized ANN-based hybrid broadband response spectra generator using physics-based simulations

Landslide Risk Assessment Based on Artificial Neural Network Model

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