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Figure from:Application of Artificial Neural Networks in Civil Engineering

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Fig. 3.1 Structure of Biological Neuron the abilities to remember, think, and apply previous experiences to our every action. These

Figure 3 1 Structure of Biological Neuron the abilities to remember, think, and apply previous experiences to our every action. These

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neurons. The figure below shows the basic structure of an artificial neuron. inputs are multiplied by its weight, these weights are represented by w,. In the simplest case
neurons. The figure below shows the basic structure of an artificial neuron. inputs are multiplied by its weight, these weights are represented by w,. In the simplest case
Fig.3.3 An artificial neural network
Fig.3.3 An artificial neural network
Fig. 4.1 Steps for ANN System Design The fig. 4.1 describes its basic steps.
Fig. 4.1 Steps for ANN System Design The fig. 4.1 describes its basic steps.
‘idal level record is an important factor in determining constructions or activity in maritime reas. Tsai and Lee applied the back propagation neural network to forecast the tidal leve sing the historical observations of water levels. However, their model is used only for the astant forecasting of tidal levels, not a long-term prediction. To demonstrate the ANN n0del, D.S. Jeng, D. H. Cha and M. Blumenstein (2003) used different data based in th raining procedure to predict the one-year tidal level in Taichung Harbour. Based on the 15 ay collected data (1-15 Jan 2000), the one-year prediction of tidal level (Jan 2000- Dec 000) against the observation is illustrated in Fig. 7.1. In the figure, solid lines denote th bservation data, and dashed lines are the predicted values. The prediction of the presen 10del overall agree with the observation. The correlation coefficient over one year is 0.9182 vhich is reasonable good. ‘ig. 7.1 Comparison of observed tide levels with those predicted over one year for Taichur Harbor (4/1996, 10/1996, 2/1997) (Jeng, Cha & Blumenstein, 2003) observation data, and dashed lines are the predicted values. The prediction of the present
‘idal level record is an important factor in determining constructions or activity in maritime reas. Tsai and Lee applied the back propagation neural network to forecast the tidal leve sing the historical observations of water levels. However, their model is used only for the astant forecasting of tidal levels, not a long-term prediction. To demonstrate the ANN n0del, D.S. Jeng, D. H. Cha and M. Blumenstein (2003) used different data based in th raining procedure to predict the one-year tidal level in Taichung Harbour. Based on the 15 ay collected data (1-15 Jan 2000), the one-year prediction of tidal level (Jan 2000- Dec 000) against the observation is illustrated in Fig. 7.1. In the figure, solid lines denote th bservation data, and dashed lines are the predicted values. The prediction of the presen 10del overall agree with the observation. The correlation coefficient over one year is 0.9182 vhich is reasonable good. ‘ig. 7.1 Comparison of observed tide levels with those predicted over one year for Taichur Harbor (4/1996, 10/1996, 2/1997) (Jeng, Cha & Blumenstein, 2003) observation data, and dashed lines are the predicted values. The prediction of the present
Table 7.1 Comparison of neural network predictions and field measurements (Goh, 1995)
Table 7.1 Comparison of neural network predictions and field measurements (Goh, 1995)
Fig. 7.3 Architecture of the artificial neural network model (Park & Cho, 2010)
Fig. 7.3 Architecture of the artificial neural network model (Park & Cho, 2010)
Fig. 7.4 Comparison of predicted and measured pile resistance (Park and Cho, 2010)
Fig. 7.4 Comparison of predicted and measured pile resistance (Park and Cho, 2010)