Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
- URL: http://arxiv.org/abs/2404.16549v2
- Date: Fri, 3 May 2024 06:32:29 GMT
- Title: Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
- Authors: Tahrima Hashem, Negin Yousefpour,
- Abstract summary: We exploit the power of deep learning algorithms to forecast scour depth variations around bridge piers based on historical sensor monitoring data.
We investigated the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for real-time scour forecasting.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around bridge piers based on historical sensor monitoring data, including riverbed elevation, flow elevation, and flow velocity. We investigated the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for real-time scour forecasting using data collected from bridges in Alaska and Oregon from 2006 to 2021. The LSTM models achieved mean absolute error (MAE) ranging from 0.1m to 0.5m for predicting bed level variations a week in advance, showing a reasonable performance. The Fully Convolutional Network (FCN) variant of CNN outperformed other CNN configurations, showing a comparable performance to LSTMs with significantly lower computational costs. We explored various innovative random-search heuristics for hyperparameter tuning and model optimisation which resulted in reduced computational cost compared to grid-search method. The impact of different combinations of sensor features on scour prediction showed the significance of the historical time series of scour for predicting upcoming events. Overall, this study provides a greater understanding of the potential of Deep Learning algorithms for real-time scour prediction and early warning for bridges with distinct geology, geomorphology and flow characteristics.
Related papers
- Physically Explainable Deep Learning for Convective Initiation
Nowcasting Using GOES-16 Satellite Observations [0.1874930567916036]
Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms.
In this study, object-based probabilistic deep learning models are developed to predict CI based on multichannel infrared GOES-R satellite observations.
arXiv Detail & Related papers (2023-10-24T17:18:44Z) - Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian
Optimization with Spatiotemporal feature fusion [7.790241122137617]
This study presented a CNN-RNN hybrid feature fusion modelling approach for urban flood prediction.
It integrated the strengths of CNNs in processing spatial features and RNNs in analyzing different dimensions of time sequences.
arXiv Detail & Related papers (2023-04-19T22:00:04Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - A predictive physics-aware hybrid reduced order model for reacting flows [65.73506571113623]
A new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems.
The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients.
Two different deep learning architectures have been tested to predict the temporal coefficients.
arXiv Detail & Related papers (2023-01-24T08:39:20Z) - An advanced spatio-temporal convolutional recurrent neural network for
storm surge predictions [73.4962254843935]
We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history.
This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
arXiv Detail & Related papers (2022-04-18T23:42:18Z) - Lost Vibration Test Data Recovery Using Convolutional Neural Network: A
Case Study [0.0]
This paper proposes a CNN algorithm for the Alamosa Canyon Bridge as a real structure.
Three different CNN models were considered to predict one and two malfunctioned sensors.
The accuracy of the model was increased by adding a convolutional layer.
arXiv Detail & Related papers (2022-04-11T23:24:03Z) - Probabilistic AutoRegressive Neural Networks for Accurate Long-range
Forecasting [6.295157260756792]
We introduce the Probabilistic AutoRegressive Neural Networks (PARNN)
PARNN is capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns.
We evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR.
arXiv Detail & Related papers (2022-04-01T17:57:36Z) - Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic
Turbulence via Deep Sequence Learning Models [24.025975236316842]
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques.
The accuracy of the model is assessed using statistical and physics-based metrics.
arXiv Detail & Related papers (2021-12-07T03:33:39Z) - RIFLE: Backpropagation in Depth for Deep Transfer Learning through
Re-Initializing the Fully-connected LayEr [60.07531696857743]
Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task.
We propose RIFLE - a strategy that deepens backpropagation in transfer learning settings.
RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning.
arXiv Detail & Related papers (2020-07-07T11:27:43Z) - Communication-Efficient Distributed Stochastic AUC Maximization with
Deep Neural Networks [50.42141893913188]
We study a distributed variable for large-scale AUC for a neural network as with a deep neural network.
Our model requires a much less number of communication rounds and still a number of communication rounds in theory.
Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
arXiv Detail & Related papers (2020-05-05T18:08:23Z) - Rectified Linear Postsynaptic Potential Function for Backpropagation in
Deep Spiking Neural Networks [55.0627904986664]
Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation.
This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
arXiv Detail & Related papers (2020-03-26T11:13:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.