INTELLIGENT SEISMIC STRUCTURAL HEALTH MONITORING SYSTEM FOR THE SECOND PENANG BRIDGE OF MALAYSIA

Transcription

1 INTELLIGENT SEISMIC STRUCTURAL HEALTH MONITORING SYSTEM FOR THE SECOND PENANG BRIDGE OF MALAYSIA Reni Suryanita Faculty of Engineering Civil Engineering Department University of Riau, Pekanbaru Azlan Adnan Engineering Seismology & Earthquake Engineering Research (E-Seer), Universiti Teknologi Malaysia Johor Bahru Malaysia Abstract The study aims to analyze and monitor the behavior of the Second Penang Bridge during the earthquake. The monitoring system is developed using the Neural Network (NN) method. The NN inputs are the acceleration, displacement and time history of the bridge structural response while the output is the damage level of the bridge. Damage levels are conducted through nonlinear time history analyses of the nine earthquakes from the PEER ground motion database. The damage level criterion is based on FEMA 356. The Neural Network methods consist of the Feed Forward and the Back-Propagation algorithms. The best prediction was achieved when used three hidden layers with time domain input at R=0.903 in the training and R=0.905 in the testing process.. The implementation of the Neural Network method for the bridge seismic monitoring system can help the bridge authorities to predict the health condition of the bridge at any given time. Key Words: intelligent system, earthquake, Neural Network, acceleration, displacement, damage level. I. INTRODUCTION The Second Penang Bridge is a 24km length and 16.9km above seawater, connecting Peninsular Malaysia and Penang Island. The bridge was completed in 2013, become the longest bridge in Southeast Asia. The second Penang Bridge has more superiority rather than the first Penang Bridge which 8.4km length above seawater as shown in Figure. 1.1 The 2 lanes dual carriageway with 3m motorcycle lane has been designed on the Second Penang Bridge. The seismic designs for the bridge are the 475-year time period with PGA g and the 2500-year time period with PGA g (Taib, 2011). The six levels of ground motion hazard which are 75, 109, 475, 950, 1642 and 2500-year return periods of earthquakes were evaluated for the Second Penang bridge site plant by Adnan, et al (2009). The accelerations from real time histories data were adjusted within the range of 0.5 to 2.0 Hz to produce the targeted spectral acceleration. Other researchers, Meng, et al (2011) has conducted design earthquake with a return period of 475 years and maximum credible earthquake with a return period of 2500 years. Performance of the Second Penang Bridge can be affected by the proximity of the bridge to the fault and site conditions nearest earthquakes from Sumatera-Indonesia. Both factors affect the intensity of ground shaking and ground deformations, as well as variation effects along the length of the bridge. 1

2 Case Study Figure 1.1. The Second Penang Bridge map (Taib, 2011) The study aims to produce an intelligent system for monitoring and detecting the condition of the Second Penang Bridge due to earthquakes load. The neural network inputs in the system consist of bridge structure responses included time responses. II. SEISMIC MONITORING SYSTEM FOR THE SECOND PENANG BRIDGE Bridge seismic design practices have changed over the years, largely reflecting lessons from performance in past earthquakes. The construction era of a bridge is a good indicator of likely performance, with higher damage levels expected in older construction than in newer construction (Chen dan Duan, 2003). In general, seismic monitoring is separate with the seismic analysis system. Especially in Malaysia, there is no seismic monitoring and analysis for bridges, which were made integrality in one the monitoring system. Sometimes the analysis is performed after the evaluation results obtained. So, the analysis is based on the expertise of engineers in the process of evaluation results. In this study, analysis system is integrated with the intelligent system so it can be used to predict damage index of bridges in the seismic zone include high and low earthquake region. The sensors of acceleration are installed at the top of piers and deck along the length of the spans as shown in Figure 2.1. Data acquisition converts the accelerations data and transfers the information into the local server. The Neural Network method is applied to the server and used to interpret and predict input data into a graph and alert warning system. 2

3 Sensors location Data Acquisition Local and remote server Operator Earthquake load Mobile devices Internet connection Bridge authorities/users Figure 2.1. Intelligent monitoring proposed for the Second Penang Bridge (Suryanita, 2014) The intelligent seismic monitoring has been proposed for the Second Penang Bridge that includes two versions namely local and distance monitoring. The remote client system can monitor the acceleration data using far away observation. The function is similar to the local server, including intelligent engine, alert system and monitoring. The Hyper Text Markup Language (HTML) format is necessary for sending the information through remote connection (Suryanita dan Adnan, 2014). The bridge model has been analyzed using Non Linear Time History (NLTH) of nine earthquakes data from Pacific Earthquake Engineering Research Center (PEER, 2012). Data of earthquakes load are shown in Table 2.1. Table 2.1 Data of the earthquakes load 3

4 III. APPLICATION OF NEURAL NETWORKS IN THE SYSTEM Neural Networks are numerical modeling techniques that are inspired by the functioning of the human brain and nerve system. An Artificial Neural Network imitates the basic concept of the brain. Some cells tell the brain that they are experiencing towards any number of sensations. These specialized communication cells are called neurons. The information obtained will be passed between the neurons, based on the structure and synapse weights. The neurons are connected to other neurons. They receive inputs from other neurons and send output to the other neurons and cells. The Neural Network is divided into single and several layers. A single layer is a connectionist model that consists of a single processing unit while multiple layers permit more complex, non-linear relationship of input data to the output result. The architecture of two hidden layers in the Neural Networks system is shown in Figure 3.1. Figure 3.1. Multilayer perceptron The cell of the input layer is represented by u i identifier. Identifying connections within the network are w(j,i), w(k,j) until w(l.k) are known as the weighted connection between the hidden cells and input cells. Through input cells (u 1, u 2, and u 3 ) provide an input value to the hidden and output cells represent a the Sigmoid function f(x), defined as Equation (3.1). (3.1) The equation above sums the product of the weights (w i ), and inputs (u i ) and a bias input (w 0 ). The output (γ) is an activation function. In this study, the Neural Network algorithms refer to Jones (2005) begins with the assignment of randomly generated weights in the feed forward, multilayer networks. The steps are repeated until the mean-squared error (MSE) of the output is sufficiently small. The parameter of learning rate ρ, is 0.5 that can be tuned to determine how quickly the back-propagation algorithm converges toward a solution. The error function of the output neurons is defined as Equation (3.2) 4

5 (3.2) where d k and O k are the desired and predicted value of the outputs, respectively. The error function should be minimized so that the Neural Network achieves the best performance. In training process, the network memorizes the relationships between input and output of the system through the connection weights. Before starting the training process, all the weights associated with the connections between the neurons must be initialized to small random numbers. In this study, normalization input data is the linear normalization as follows: (3.3) where is the normalized input or output values, z i the original data, z max and z min, respectively, the maximum and minimum values, and a and b are the positive constants allowing to fix the limits of the interval for the scaled values. IV. RESULTS AND DISCUSSION The Second Penang Bridge model in Figure 4.1 has analyzed using SAP2000 Non Linear Time History analysis subject to the earthquakes load as shown in Table 2.1. The damage of the piers due to San Francisco earthquake is shown in Figure 4.2. Criterion of bridge damage is based on standard of Federal Emergency Management Agency (FEMA356, 2000). Initial of B is described as operation level, IO is Immediate Occupancy, LS is life safety, and CP is collapse prevention. The level before damage is described with S (safe level). Figure 4.1. The Second Penang bridge model with layout of sensors. Figure 4.2. Damage level of the piers due to San Francisco earthquake In this study, the architecture of Neural Network method as shown in Figure 3.1. The inputs are accelerations, displacements and time domain compared with the input without the time domain in training and testing neural network. The output layer is a damage level of the bridge. The hidden layer used the multiple layers from one to five hidden layers. The inputs are the response of the bridge model through the finite-element analysis at the 5

6 critical point of the damage. The input and output of 1809 data for training neural network as shown in Table 4.1. Table 4.1. Input and output data for neural network method The indicators of the acceptable result in the Neural Network are the Mean Square Error (MSE) approached 0 (zero), and the regression value (R) approached 1 (one). The results show the regression value, R of training and testing process with three hidden layers is more accurate rather than else as shown in Figure 4.3 and Figure 4.4. Figure 4.3. Regression of training process. Figure 4.4. Regression of testing process. Figure 4.5. MSE value of model. Figure 4.6. The Snapshot of the software. 6

7 V. CONCLUSION 2 nd ACE National Conference 2015 The Neural Network methods on the Feed Forward and the Back-Propagation algorithms will be used in the computer program, namely Seer-Monalisa software. This software displays the alert warning system of the Second Penang Bridge based on result prediction of Neural Network analysis. Based on the results, the Neural Network method is recommended use three hidden layers and included the time domain as the input because the MSE value is smaller than others layers and can predict the damage more accurately. Therefore, the implementation of the intelligent Neural Network method for the bridge seismic monitoring system can help the bridge authorities to predict the stability and health condition of the bridge damage at any given time. REFERENCES Adnan, A., Hendriyawan & Suhatril, M. (2009). Seismic Hazard Assessment for Second Penang Bridge. Johor Bahru: SEER & GTIM Universiti Teknologi Malaysia. Chen, W.-F. & Duan, L. (eds.) (2003). Bridge Engineering Seismic Design, Florida: CRC Press. FEMA356 (2000). Prestandard and Commentary for The Seismic Rehabilitation of Buildings. Federal Emergency Management Agency. Jones, M. T. (2005). AI Application Programming. In: PALLAI, D. (ed.). Boston, Massachusetts: Charles River Media. Meng, F. C., Sham, R. & Zhenru, F. (Year). The Design of Second Penang Bridge. In: The Frist International Seminar on the Design and Construction of Second Penang Bridge 2011 Kuala Lumpur. PEER (2012). Pacific Earthquake Engineering Research Ground Motion Database. Suryanita, R. (2014). Integrated Bridge Health Monitoring, Evaluation and Alert System using Neuro-Genetic Hybrids Doctor of Philosophy Program, Universiti Teknologi Malaysia. Suryanita, R. & Adnan, A. (2014). Early-Warning System in Bridge Monitoring Based on Acceleration and Displacement Data Domain. In: Yang, G.-C., AO, S.-I., Huang, X. & Castillo, O. (eds.) Transactions on Engineering Technologies. Springer Netherlands. Taib, I. b. M. (2011). The Second Penang Bridge: Sustainable Design and Construction. Building and Infrastructure Technology Conference. Universiti Sain Malaysia, Kuala Lumpur: Universiti Sain Malaysia. 7