Sensitivity analysis of parameters affecting scour depth around bridge piers based on the non-tuned, rapid extreme learning machine method

2018 ◽  
Vol 31 (12) ◽  
pp. 9145-9156
Author(s):  
Isa Ebtehaj ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji ◽  
Hassan Sharafi
Keyword(s):  
Sensitivity Analysis ◽  
Extreme Learning Machine ◽  
Bridge Piers ◽  
Scour Depth ◽  
Machine Method ◽  
Learning Machine

scholarly journals Prediction of scour depth around bridge piers using self-adaptive extreme learning machine

2016 ◽  
Vol 19 (2) ◽  
pp. 207-224 ◽  
Author(s):  
Isa Ebtehaj ◽  
Ahmed M. A. Sattar ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Differential Evolution Algorithm ◽  
Bridge Piers ◽  
Support Vector ◽  
Scour Depth ◽  
Pier Scour ◽  
New Model ◽  
Learning Machine ◽  
Self Adaptive

Accurate prediction of pier scour can lead to economic design of bridge piers and prevent catastrophic incidents. This paper presents the application of self-adaptive evolutionary extreme learning machine (SAELM) to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers: sharp, round, cylindrical, and square. The model network parameters are optimized using the differential evolution algorithm. The best SAELM model calculates the scour depth as a function of pier dimensions and the sediment mean diameter. The developed SAELM model had the lowest error indicators when compared to regression-based prediction models for root mean square error (RMSE) (0.15, 0.65, respectively) and mean absolute relative error (MARE) (0.50, 2.0, respectively). The SAELM model was found to perform better than artificial neural networks or support vector machines on the same dataset. Parametric analysis showed that the new model predictions are influenced by pier dimensions and bed-sediment size and produce similar trends of variations of scour-hole depth as reported in literature and previous experimental measurements. The prediction uncertainty of the developed SAELM model is quantified and compared with existing regression-based models and found to be the least, ±0.03 compared with ±0.10 for other models.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

2018 ◽  
Vol 281 ◽  
pp. 209-221 ◽  
Author(s):  
Irfan Bahiuddin ◽  
Saiful A. Mazlan ◽  
Mohd. I. Shapiai ◽  
Seung-Bok Choi ◽  
Fitrian Imaduddin ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Extreme Learning Machine ◽  
Machine Method ◽  
Learning Machine ◽  
Magneto Rheological

scholarly journals An Improved QZSS Satellite Clock Offsets Prediction Based on the Extreme Learning Machine Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 156557-156568
Author(s):  
Lina He ◽  
Hairui Zhou ◽  
Shaolin Zhu ◽  
Ping Zeng
Keyword(s):  
Extreme Learning Machine ◽  
Satellite Clock ◽  
Machine Method ◽  
Learning Machine

Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

2020 ◽  
Vol 62 (1) ◽  
pp. 15-21
Author(s):  
Changdong Wu
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Classification Accuracy ◽  
Activation Function ◽  
Machine Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

Estimating building energy consumption using extreme learning machine method

Energy ◽  
2016 ◽  
Vol 97 ◽  
pp. 506-516 ◽  
Author(s):  
Sareh Naji ◽  
Afram Keivani ◽  
Shahaboddin Shamshirband ◽  
U. Johnson Alengaram ◽  
Mohd Zamin Jumaat ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Extreme Learning Machine ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Machine Method ◽  
Learning Machine
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