###### Abstract
However, when the curing age of SCC was raised to 180 days, this value increased to 71 MPa. SP's high cement and binder contents make it impossible to make SCC without it. The presence of SP improves workability. Although SP is added to SCC to enhance its rheology, different dosages can also affect compressive strength. The CS of SCC increased by 30% after 28 days of curing as the SP dosage increased from 5.5 kg/m\({}^{3}\) to 8.25 kg/m\({}^{3}\)[24]. Aggregates comprise 60 and 70 percent of SCC's volume, significantly impacting the material's rheological and hardened performance. Compared to conventional concrete, SCC typically contains less coarse material [4]. According to earlier studies, the CS of SCC was less affected by the fine aggregate content than the coarse aggregate content. Additionally, compared to coarse aggregates with a maximum size of 10 mm, those with a maximum size of 20 mm had a higher CS of SCC [25]. Artificial neural networks have been widely used in many studies, such as predicting the piezoelectric effect of the plate of engineering structures in vibration and noise reduction. This study employed an artificial neural network (ANN) model to explore the piezoelectric patch size and thickness's effect on a plate's first-order natural frequency and displacement amplitude. With the finite element method (FEM), a rectangular plate actuated by a piezoelectric patch was analyzed with various patch sizes. The FEM data were later used to build an ANN model. The dynamic response of the plate was predicted by the ANN model and validated with FEM in terms of first-order natural frequency and displacement amplitude. Case studies showed that the ANN model could accurately predict both natural frequency and displacement amplitude with patch length, width, and thickness input. When the information of the ANN model was simplified to patch size and thickness or the patch's volume, the accuracy worsened. The patch size and thickness's influences on the first-order natural frequency were coupled, and the maximal and minimal values were predicted based on the ANN model. The purpose of this research is to develop and apply the artificial neural network (ANN) with an extreme learning machine (ELM) to forecast the gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on the value of agriculture, manufacturing, industry, and services added to GDP. The results were compared with the ANN with the backpropagation (BP) learning approach since BP could be considered a conventional learning methodology. The reliability of the computational models was assessed based on simulation results and several statistical indicators. The results showed that the ANN with ELM learning methodology could be applied effectively in applications of GDP forecasting [23; 24; 25; 26; 27]. Since compressive strength depends on various characteristics and mixture proportions, more sophisticated methodologies should be used to lessen the need for laboratory experiments and provide engineers with more convenient tools and numerical equations for predicting experimental results. An excellent strategy is to use soft computing techniques. The main benefit of using these techniques is the development of workarounds and solutions for linear or nonlinear problems where mathematical models cannot readily show the relationship between the pertinent factors in the problem [27; 28; 29; 30]. A methodical multiscale model was developed to calculate the CS of concrete made with significant amounts of fly ash [28]. Four hundred fifty different experimental datasets were used to create a model. MSP trees, linear, nonlinear, and multilogistic regression were developed as five distinct qualification modeling methods. It was concluded that the ANN, MSP tree, and multilogistic regression models could predict the CS of large-volume fly ash concrete wells based on higher R\({}^{2}\) and lower RMSE and MAE values [29].