A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

2020 ◽  
Author(s):  
Mahdi Shariati ◽  
Mohammad Saeed Mafipour ◽  
Behzad Ghahremani ◽  
Fazel Azarhomayun ◽  
Masoud Ahmadi ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Extreme Learning Machine ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Compressive Strength Of Concrete ◽  
Learning Machine

Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm

2021 ◽  
Vol 146 (1-2) ◽  
pp. 833-849
Author(s):  
Ali Kozekalani Sales ◽  
Enes Gul ◽  
Mir Jafar Sadegh Safari ◽  
Hadi Ghodrat Gharehbagh ◽  
Babak Vaheddoost
Keyword(s):  
Extreme Learning Machine ◽  
Water Depth ◽  
Lake Water ◽  
Hybrid Algorithm ◽  
Urmia Lake ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Learning Machine ◽  
Improved Grey Wolf Optimizer

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

scholarly journals Assessment of Longstanding Effects of Fly Ash and Silica Fume on the Compressive Strength of Concrete Using Extreme Learning Machine and Artificial Neural Network

2021 ◽  
Vol 5 (1) ◽  
pp. 50
Author(s):  
Mahdi Shariati ◽  
Danial Jahed Armaghani ◽  
Manoj Khandelwal ◽  
Jian Zhou ◽  
Arameh Eyvaziyan ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Extreme Learning Machine ◽  
Silica Fume ◽  
Cementitious Material ◽  
Short Term ◽  
Replacement Ratio ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Learning Machine

Compressive Strength (CS) is an important mechanical feature of concrete taken as an essential factor in construction. The current study has investigated the effect of fly ash and silica fume replacement content on the strength of concrete through Artificial Neural Networks (ANNs) and Extreme Learning Machine (ELM). In this study, different ratios of fly ash with (out) extra quantity of silica fume have been tested. Water cement (w/c) ratio varies during the test. Eight input parameters including Total Cementitious Material (TCM), Silica Fume (SF) replacement ratio, coarse aggregate (ca), fly ash (FA) replacement ratio, Sewage Sludge Ash (SSA) as a combination of cement and fine aggregate replacement, water-cement ratio, High Ratio Water Reducing Agent (HRWRA) and Age of Samples (AS) and one output parameter as the CS of concrete have been investigated through ANN and ELM. Up to now, numerous experimental studies have been used to analyze the compressive strength of concrete while retrofitted with fly ash or silica fume, however, the novelty of this study is in its use of AI models (ELM, ANN). The models have been developed and their outcomes were compared through six statistical indicators (MAE, RMSE, RRMSE, WI, RMAE and R2). Subsequently, both methods were shown as reliable tools for assessing the influence of cementitious material on compressive strength of concrete, however, ANN remarkably was better than ELM. As a result, FA showed less contribution to the strength of concrete at short times, but much at later ages. As a result, the enhanced influence of low amount of SF on CS was not significant. Adding fly ash has reduced the compressive strength in short term, but increased the compressive strength in long term. Adding silica fume raises the strength in short term, but decreases the strength in longterm. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

2014 ◽  
Vol 548-549 ◽  
pp. 1735-1738 ◽  
Author(s):  
Jian Tang ◽  
Dong Yan ◽  
Li Jie Zhao
Keyword(s):  
Compressive Strength ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Extreme Learning Machines ◽  
Concrete Compressive Strength ◽  
Learning Machines ◽  
Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

scholarly journals Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1375 ◽  
Author(s):  
Hui Li ◽  
Bangji Fan ◽  
Rong Jia ◽  
Fang Zhai ◽  
Liang Bai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Grey Wolf Optimizer ◽  
Mechanical Equipment ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Fault Features ◽  
Frequency Domains ◽  
Learning Machine

Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

scholarly journals Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Hong Yang ◽  
Lipeng Gao ◽  
Guohui Li
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Acoustic Signal ◽  
Grey Wolf ◽  
Underwater Acoustic ◽  
Signal Prediction ◽  
Mode Decomposition ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Underwater Acoustic Signal

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

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