Comparison Between PLAXIS Output and Neural Network in the Guard Walls

Transcription

1 Coparison Between PLAXIS Output and Neural Network in the Guard Walls Ali Mahbod 1, Abdolghafar Ghorbani Pour 2, Abdollah Tabaroei 3, Sina Mokhtar 2 1- Departent of Civil Engineering, Shahid Bahonar University, Keran, Iran ) Corresponding Author) 2- Departent of Civil Engineering, Arak Branch,Islaic Azad University, Arak,Iran 3- Departent of Civil, Science and Research Branch, Islaic Azad University, Tehran, Iran )Presenter: Ali Mahbod ) Abstract The purpose of this study to estiate the paraeters of the soil and anchor by eans of artificial neural network to deterine its success in oving the walls of the guard. For this purpose, the artificial neural network application software Matlab was used. Six paraeters have been chosen to anchor the soil and then exained the function of neural network for oveent of the guard wall. Our finding suggest that With increasing soil echanical paraeters such as odulus of elasticity of soil and soil internal friction angle of the wall decreases the axiu aount of transforation and With increasing aounts of inhibitory Non tangly and transforation up to the wall decreases. using the artificial neural network in this article is cited can be ade saving tie and cust. Key words: Artificial Neural Network, Excavation, PLAXIS 2D, Horizontal Displaceent 1. Introduction In any deanding engineering applications coputationally inexpensive predictions, based on Met odels can be used, rather than solving a set of atheatical equations analytically, or even nuerically. Most ethods are inspired by natural paradigs and therefore differ significantly fro conventional atheatical approaches. Artificial neural networks (ANNs), fuzzy systes as well as evolutionary ethods are the ost popular. In general, artificial intelligence (AI) ethods are used either in order to reduce the coputational cost, or when the coplexity and/or the size of the proble prohibits the use of conventional techniques (Lagaros,2006). Especially, ANNs have been widely-used in any 1

2 fields of science and technology, as well as into an increasing nuber of various engineering applications (Wu,2006). 2. Methodology A learning algorith in order to achieve the correct response for each input vector that is given to the neural network. Previous estiates of the training set and a full investigation to obtain ore inforation and a better approxiation of the tie the forecasts were reduced. 3. Ground Anchors Paraeters Pressure-injected ground anchors were used to support the wall. The ground anchor angle was selected to be 30 fro the horizontal so the ground anchors would apply a significant downward load on the soldier beas. The anchors were installed by driving a closed-end, 8.89 c casing into the ground. After the casing reached the desired depth, then the ground anchor tendon was inserted in the casing and the closure point driven off. Ceent grout was puped down the casing as the casing was extracted. The top row of anchors had a 5.48 unbounded length and the botto row of anchors had a 4.57 unbounded length. A plastic tube was used as a bond breaker over the unbounded length. In 2d odeling the grout body(second part of anchor) odeled by Geogrid eleent, the unbonded lengh (first part of anchor) odeled by node to node anchor. Staged constructions was in 8 phases. The nuerical odeling in this article has been done by PLAXIS 2D software and the selecting conditions is plane strain. 4. Artificial Neural Networks Artificial neural networks (ANNs) are perhaps the ost popular intelligent coputational paradigs. An ANN consists of a nuber of units linked together and attepts to create a desired apping between the input and the output data of a specific set. In order to achieve this goal a training set (D) is coposed by input-target pairs D = [x,t], where is the nuber of the pairs, x the input data and t the target pairs. A neural network architecture A consists of a specific nuber of layers, a nuber of neurons in each layer, and a suitable activation function. The input layer projects the data to the interediate layer(s). Each interediate or hidden layer passes the data to the next interediate layer, while the final hidden layer projects the inforation to the output neurons. If a set of values w, corresponding to the weight factors, is assigned to the network then a apping y(x;w; A) is defined between the inputs x and the outputs y. The quality of this apping, with respect to the training set, is easured by an error function ED, defined as follows (Eq.1): 1 2 ED D w, A y( x ; w, A) t 2 (1) A learning algorith tries to deterine the optiu values of w that iniize the value of ED in order to achieve the correct response for each input vector that is given to the neural 2

3 network. The nuerical iniization algoriths used for the ANN training generate a sequence of weight paraeters w through an iterative procedure. To apply an algorithic operator the starting weight paraeters w are needed, which are subsequently updated as follows (Eq.2): W new old 1 T W S (a ) (2) 1 T Here is the learning rate, S the sensitivity of the network layer, (a ) is the transposed atrix of output layer before. S S M M M F (n )(t a) (3) 1 T 1 f (n )(W ) S M 1,...,2,1 (4) Where F M M 1 T (n ) derived fro the conversion function, (W ) transpose of weight atrix in the previous layer. (Eq.3) Learning algoriths can be classified into local and global algoriths (MacKay,1992). Global algoriths use knowledge of the current state of the entire network, such as the direction of the overall weight update vector. For instance, in the widely-used backpropagation learning algorith the gradient descent algorith is used. In contrast, local adaptation strategies are based on specific inforation of the weight values, such as the teporal behavior of the partial derivative of the weights. The local approach is better related to the natural neural networks concept of distributed processing, where the coputations are perfored independently. Moreover, it appears that for any applications local strategies achieve faster and ore reliable predictions than global techniques (Riediller,1994) ANN odel with the data odel 162 PLAXIS trained. Model input data for ANN (E 1, E 2, φ 1, φ 2, L 1, L 2 ), output guards are reovable wall. ANN to predict the oveent used is coposed of three layers: a: input layer with six node (E 1, E 2, φ 1, φ 2, L 1, L 2 ). b: the hidden layer, and c: the output layer with one node (displaceent) After an initial investigation with respect to the nuber of the hidden layer s nodes, the ANN configuration resulted in a [6-9-1] architecture (Figure 1). In addition, it was observed that increasing the nuber of hidden layers did not alter significantly the perforance of the ANN, thus the runs were perfored using one interediate layer. 3

4 Fig 1: Hidden Layers Previous estiates of the training set and a full investigation to obtain ore inforation and a better approxiation of the tie the forecasts were reduced. input data and architecture of the ANN used in the training set is used in Table 1 and 2 have shown and the results of the perforance of ANN for four odel inputs varied showed that Fro these results it can be seen that the accuracy of the ANNs is not deteriorated significantly by the reduction of the training data. Table1: Input Paraeters Model The input paraeters 1 E 1,E 2,φ 1,φ 2,L 1,L 2 2 E 1,E 2,φ 1,φ 2,L 1 3 E 1,E 2,φ 1,φ 2 4 E 1,E 2,φ 1 Table2: Architecture of ANN ANN Architecture 1 [6-9-1] 2 [5-9-1] 3 [4-9-1] 4 [3-9-1] Network training error with respect to changes in input data, the nuber of hidden layers and nuber of neurons in the hidden layer is calculated. The observations of how the error decreases and the network test Found wrong in the past for a odel that has the best answer, is as follows (Table 3): Table3: The Observations of how the error decreases and the network test Found wrong in the past for a odel that has the best answer Model 1 Paraeters 6 The nuber of input 1 9 inf The nuber of hidden layers The nuber of neurons in the hidden layer Tie 0.05 Learning rate 4

5 1000 Repeat Network is trained to evaluate the data series of 18 shrip were not used in the training phase. The following Table 4 Coparison between the output of network and application data is done. Table4: Coparison Between the Output of Network and Application Data Model E first layer (KN/ 2 ) E second layer (KN/ 2 ) φ first layer φ second layer L UNB of first anchor L UNB of second anchor Plaxis results Ann results Error (%) It has seen fro the table that has a high success rate in the network and 2.69% error rate predicted to ove the wall guards. In order to ore closely define the output PLAXIS and ANN, the above table can be found in the following chart (Fig 2): 5

6 5. Conclusion Fig 2: Coparison between PLAXIS output and Neural Network Purpose of The current study was to estiate the paraeters of the soil and anchor by eans of artificial neural network to deterine its success in oving the walls of the guard. Our finding suggest that With increasing soil echanical paraeters such as odulus of elasticity of soil and soil internal friction angle of the wall decreases the axiu aount of transforation and With increasing aounts of inhibitory non tangly and transforation up to the wall decreases. Reducing the inhibitory non tangly and transforation values of axiu wall increases. During the transforation non tangly on the axiu wall length is ore tangly. Stress reduction in force, with a axiu inhibitory aounts of deforation increases. using the artificial neural network in this article is cited can be ade saving tie and cust. Further work needs to be done to establish whether other software, such results are confired and also to apply these results in real cases fro the field copared with the reports entioned in this article. Refrences: 1. Lagaros ND, Papadrakakis M. (2004) "Iproving the condition of the Jacobin in neural network training", Adv Eng Softy, Lagaros ND, Tsopanakis Y, editors. (2006) Intelligent coputational paradigs in earthquake engineering. Idea Publishers, MacKay DJC.(1992) "A practical Bayesian fraework for back prop networks", Neural Copute,

7 4. Papadrakakis M, Lagaros ND, Tsopanakis Y. (1998) "Structural optiization using evolution strategies and neural networks", Copute Methods Apple Mach Eng, Riediller M. (1994) "Advanced supervised learning in ulti-layer perceptions fro back propagation to adaptive learning algoriths", International Journal of Coputer Standards and Interfaces Special Issue on Neural Networks, Riediller M, Braun H.( 1993) "A direct adaptive ethod for faster back-propagation learning: the RPROP algorith", In: Proceedings of the IEEE international conference on neural networks (ICNN). San Francisco, Wu CL, Chau KW. (2006) "A flood forecasting neural network odel with genetic algorith", Int J Environ Pollute,