Comparing two different vibration methods used on bridges.
Bridge Assessment Using Force-Vibration Testing and
Intelligent Bridge Seismic Monitoring System Based on
Neuro Genetic Hybrid
Cristian Alexander Diaz Salazar
ENG-202: Technical Writing
Prof: Pamela Stemberg
November 29 2022
Title
Lab Report 1. Bridge Assessment Using Force-Vibration Testing.
Lab Report 2. Intelligent Bridge Seismic Monitoring System Based on.
Abstract
In the Bridge Assessment Using Force-Vibration Testing report the full-scale force-vibration test were conducted before and after structural repairs on reinforced-concrete highway bridge. A purpose-built hydraulic vibrator was used to artificially excite the bridge, and they place accelerometers on the bridge deck to measure the dynamic response of the bridge. To extract natural frequencies a single-degree-of-freedom model was fitted. There was a slight reduction in the natural frequencies that was found due to the repair works but the was no definite trend in the changes to the modal damping ratios.
In the Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid report the natural disaster and design mistake can damage the bridge structure, the damage caused a severe safety problem to human. The study aims to develop the intelligent system for bridge health monitoring due to earthquake load. The Genetic Algorithm method in Neuro-Genetic hybrid has applied to optimize the acceptable Neural Network weight. The acceleration, displacement and time history of the bridge structural responses are used as the input, while the output is the damage level of the bridge. The system displays the alert warning of decks based on result prediction of Neural Network analysis. The result shows the damage level prediction is agreeable to the damage actual values. Therefore, this method in the bridge monitoring system can help the bridge authorities to predict the health condition of the bridge rapidly at any given time.
Introduction
Bridge Assessment Using Force-Vibration Testing
To bridges to continue providing a safe service is necessary to made periodic structural-condition monitoring of their structure. Current bridges-assessment procedures usually have some limitations like; they are part of the structure that are inaccessible or impossible to inspect; the inspection depends on the personnel’s experience and knowledge; the result from a particular area don’t represent conditions at another area; and it would be necessary to make measurement at many points to have a good representation of the global structural condition. The basic principle of using vibration monitoring to assess structural integrity relies on the fact that dynamic response is a sensitive indication of the physical integrity of any structure.
Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid
Bridges are necessary structures to connect two places throughout the transportation
system. The bridge should have enough strength capacity to withstand the self-weight and
moving loads on the deck. Construction of the bridge shall be supervised by the bridge
authorities to obtain long service life, ensure public safety, and reduce maintenance costs. One
of the essential efforts to know the life cycle performances and management procedures of
bridges are through Structural Health Monitoring
Material and Methods
Bridge Assessment Using Force-Vibration Testing
The instrumentation that was used for the Bridge Assessment Using Force-Vibration Testing to get the artificial excitation of the bridge was achieve with an excitation system based on hydraulic actuator and mounted within a purpose built frame. The system could induce vertical excitation of highways bridges, long-span floor slabs and similar structures. The actuator is powered by two pumps and has a maximum force amplitude and stroke of 5kN and 300mm. The system had an electronic control unit through which an input signal is fed to the actuator. Four Schaevitz closed-loop, force balance linear servo accelerometers were used to measure the bridge’s response to the induce excitation. The accelerometers can measure the acceleration as low as 10-6 . For better stability each accelerometer was screwed onto a heavy metal block that was place on the bridge deck. Screened cables were used to connect the accelerometers to the signal conditioning unit. A Racal Store 7 FM tape recorder was used to record the conditioned signals for detail analysis off-site. An HP3582A dual-channel spectrum analyzer was used to compute frequency response functions (FRF). The cost of the equipment used in the tests is approximately €60,000.00.
Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid
For Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid the material is the program, and the method of Neuro-Genetic Hybrid consists of acceleration (A), displacement (D), and time (T) as input data, while output data are the damage levels (Step 1). The input data was conducted through the Nonlinear Finite Element Analysis under earthquake load using SAP 2000 Software. Meanwhile, the damage levels consist of minor-damage as Immediate Occupancy (IO), moderate-damage stated Life Safety (LS) and severe damage as Collapse Prevention (CP) level based on FEMA 356. In Step 2, input and output data are loaded for training BPNN. Every chromosome has several genes in an initial population of GA is defined as A chromosomes times B genes. B genes refer to the total of weight that involved in BPNN based on Neural Networks architecture. The configuration of a BPNN consists of x, y, and z where, x is the number of input neuron, y is a number of the hidden neuron, and z is some output neuron respectively.
Results
Bridge Assessment Using Force-Vibration Testing
The frequency response function for each test series measured at the reference point and from points on the eastern and western sides of the bridge were added, and each resulting function was normalized with respect to the largest value. Some changes were detectable in the cumulative response functions, giving an indication that there has been a change in the bridge’s condition. The plots also show detectable differences between the two states of the bridge (Figure 7).
Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid
An acceptable Mean Square Error in this study has performance goal 0.005. The maximum number of epochs is 50000, and learning rate is 0.15. The training process used the Intel Core i5-2410M computer specification. The power of the processor is 2.30 GHz with turbo boost up to 2.90 GHz and memory 4 GB. Input data based on the time history of bridge response which, consists of the displacement on the top of the piers. The Neuro-Genetic Hybrid used 70% data for training, 15% data for testing and 15% data for the validation process. The best performances of Neuro-Genetic Hybrid depend on the selection of suitable initial weight, network architecture model, and activation functions.
Discussion
Bridge Assessment Using Force-Vibration Testing
The mode shapes before and after the repairs are compared in Fig. 10. The full mode shape for mode 2 was no identified because the vibrator was located close to a node of this mode. Mode 2 had been omitted from Fig. 10 and will not be referred to in subsequent discussions. Fig. 10 shows a large difference in the mode shapes at the repaired span DE especially for modes 7. That was why elements of the mode-shape matrix corresponding to those points are relatively large for some modes.
Conclusion
Bridge Assessment Using Force-Vibration Testing
The structural repairs of the bridge did not change significantly because of the natural frequencies. The localized nature of the repairs was the reason the modest changes. There was no definite trend in the changes in damping values due to the repairs. Two of the three affected points were detected, and two spurious locations were also identified. A procedure to assess the condition of bridge structure was suggested.
Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid
levels. According to the results, the Neuro-Genetic Hybrids method based on the sensor recording data in the system can produce the best performance for prediction of damage level of bridge structure due to earthquake loads. The prediction rate value is 97% closer to the actual damage values. Therefore, this quick method can be applied to the monitoring system and predict bridge performances during and after the earthquakes
Acknowledgements
Bridge Assessment Using Force-Vibration Testing
The writers were grateful to the U.K. Department of Transport for permission to test the bridge, and to Devon County Council, U.K., and Mott MacDonald Special Services Division, U.K., for supplying structural details of the bridge. The actuator and pump units of the excitation system were purchased with funds awarded by the Science and Engineering Research Council, U.K.
Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid
It does not have any acknowledgement.
Appendix
Bridge Assessment Using Force-Vibration Testing
It has a lot of references and then it has the symbols that were used in the paper.
References
Salawu, O. S., & Williams, C. (1995). Bridge Assessment Using Forced-Vibration Testing.
Journal of Structural Engineering, 121(2), 161. https://doi-org.hostos.ezproxy.cuny.edu/10.1061/(ASCE)0733-9445(1995)121:2(161)
Suryanita, R., Mardiyono, & Adnan, A. (2017). Intelligent Bridge Seismic Monitoring System.
Based on Neuro Genetic Hybrid. Telkomnika, 15(4), 1830–1840. htps://doi-org.hostos.ezproxy.cuny.edu/10.12928/TELKOMNIKA.v15i4.6006
