An efficient model for predicting the train-induced ground-borne vibration and uncertainty quantification based on Bayesian neural network

An efficient approach for prediction of subway train-induced ground vibrations considering random track unevenness

(2019) Zhihui Zhu et al.   JOURNAL OF SOUND AND VIBRATION

Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation

(2019) Yongchan Kwon et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

The influence of uncertain local subsoil conditions on the response of buildings to ground vibration

(2018) Manthos Papadopoulos et al.   JOURNAL OF SOUND AND VIBRATION

Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323

(2018) Mampi Karmakar et al.   MEASUREMENT

Development of a simple 2D model for railway track-bed mechanical behaviour based on field data

(2017) Tong-Wei Zhang et al.   SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

A combined numerical/experimental prediction method for urban railway vibration

(2017) Georges Kouroussis et al.   SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

Quantification of parametric uncertainty of ANN models with GLUE method for different streamflow dynamics

(2017) Hakan Tongal et al.   STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

Hybrid predictions of railway induced ground vibration using a combination of experimental measurements and numerical modelling

(2016) K.A. Kuo et al.   JOURNAL OF SOUND AND VIBRATION

Artificial neural network model for ground vibration amplitudes prediction due to light railway traffic in urban areas

(2016) G. Paneiro et al.   NEURAL COMPUTING & APPLICATIONS

Study of the train-induced vibration impact on a historic Bell Tower above two spatially overlapping metro lines

(2016) Meng Ma et al.   SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

Prediction of ground vibration amplitudes due to urban railway traffic using quantitative and qualitative field data

(2015) G. Paneiro et al.   TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT

Nonlinear time series forecasting with Bayesian neural networks

(2014) Ozan Kocadağlı et al.   EXPERT SYSTEMS WITH APPLICATIONS

Quantification of uncertainty in the prediction of railway induced ground vibration due to the use of statistical track unevenness data

(2014) G. Lombaert et al.   JOURNAL OF SOUND AND VIBRATION

Assessment of railway vibrations using an efficient scoping model

(2014) D.P. Connolly et al.   SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

Predicting surface vibration from underground railways through inhomogeneous soil

(2012) Simon Jones et al.   JOURNAL OF SOUND AND VIBRATION

Prediction uncertainties and inaccuracies resulting from common assumptions in modelling vibration from underground railways

(2012) Simon Jones et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT

Aleatory or epistemic? Does it matter?

(2008) Armen Der Kiureghian et al.   STRUCTURAL SAFETY

Prediction of vibrations induced by underground railway traffic in Beijing

(2007) S. Gupta et al.   JOURNAL OF SOUND AND VIBRATION