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CIV5887 Infrastructure Health Monitoring

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CIV5887 Infrastructure Health Monitoring

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Course Code: CIV5887
University: Monash University is not sponsored or endorsed by this college or university

Country: Australia


Sensor optimization using a genetic algorithm for structural health monitoring in harsh environments.
Optimal sensor placement for long-span cable-stayed bridge using a novel particle swarm optimization algorithm.
Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm
Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network
Damage diagnostic technique combining POD with time-frequency analysis and dynamic quantum PSO.
Structural damage detection based on posteriori probability support vector machine and Dempster-Shafer evidence theory.
Vibration-based damage detection techniques used for health monitoring of structures:
Department of defense handbook. Nondestructive evaluation system reliability assessment
lower vehicle speed seems to overall provide measurements that enable better predictions by the trained network, in the sense that the prediction errors in both healthy and damaged structural condition are inferior than for higher speeds;
The two sensors placed in the middle of the bridge seem to be the most efficient in the discrimination between healthy and damaged data, apparently disregarding where in the bridge damage takes place. This may be explained by the fact that the response of the simply supported bridge is emphasized at half-span;


Bridges represent an essential structural link within a nation’s transportation system. They usually support a nation’s economic activities by offering convenient means of transporting people and goods over mountain valleys, water bodies and other topological obstructions (de Dios OrtÃozar and Willumsen, L. G. (2011). For many decades the damage detection in bridges has been an active research topic. Due to proliferated traffic demands, environmental factors and ageing, there is a need to monitor these bridges in order to ensure that safety and efficiency are achieved. At the same time, the high demand for maintenance of this structures most governments has to incur an extra cost of maintenance which was not in the initial budget. Structural Health Monitoring can be a great solution to the maintenance of the bridges (Leung et al. 2017). Structural Health Monitoring is an effective data processing and effective sensory technology which deal with the structural analysis and the modern information technology algorithms. The key objectives of Structural Health Monitoring are to carefully monitor structural behavior due to varying loading conditions, identify any damage, location and nature of the damage and to assist the inspection of the structure. Therefore, Structural Health Monitoring can perform a key role in maintaining and managing these infrastructure systems due to various security and economic safety reasons.
In many engineering applications such as model updating, structural control and structural health monitoring, the rate of vibrations is a very significant dynamic parameter for bridges. At the same time, the occurrence of vibration of moving vehicles is an essential dynamic aspect of the newly generated methods for determining the bridge frequencies in the situations where a vehicle in motion is used as a test vehicle, for the purpose of detecting damage of bridges or shaking control of bridges that are exposed to moving vehicles (Farrar and Worden 2012).
Structural Health Monitoring Approaches for bridges.
At present, there are two main methods for Structural Health Monitoring of bridges, i.e., direct approach and an indirect approach (Magalhães, Cunha and Caetano, 2012). Most of the present monitoring methods are direct approaches in which sensors are fixed at specific locations of the bridges and data is obtained from them. These on-site installed sensors happen to be expensive in terms of initial cost and maintenance, vulnerable, time-consuming in installation and continuously energy consuming for their operation. Moreover, this procedure requires immense data processing and management at the bridge.

Fig 1: Sensors are fixed at specific locations of the bridges
Conversely, an indirect approach for monitoring of bridges completely eliminates the use of any installed equipment on the bridges. As data are collected from sensors equipped moving vehicles passing over the bridges and based on dynamic responses of those moving vehicles, this approach is considered as direct one (Jang et al. 2010). The collected data from moving vehicles are structurally analyzed and processed based on multiresolution pattern techniques. This Vehicle-Bridge Interaction approach covers the monitoring of entire bridge due to moving vehicles, unlike the installed sensors which only cover the fraction of portion. Other advantages are simplicity, accuracy, and certainty of structural identification, economy and so on.
Generally, indirect approaches to bridge health monitoring can be classified into two parts: bridge identification and bridge damage detection methods. Bridge identification methods are based on identifying bridge modal parameters, and bridge damage detection is based on identifying bridge deterioration measured from responses of moving vehicles. Measured dynamic properties obtained from the first group can also be utilized for bridge damage detection (Okasha and Frangopol 2012)
Damage identification methods
The techniques that are usually applied in identifying damages in bridges include.
Damage index method
This technique was established in 1994 by Kim and Stubbs with one main purpose of locating destruction in structures by having their distinguishing mode shape both after and before the damage. For those structures that can be characterized as a beam having a damage index β is established based on the changes in the strain energy that is stored in the given structure the moment it bends in a specific mode shape.
Change in flexibility method
Biswas and Pandey (1994) show that for the damaged and undamaged structures, the F the (flexibility matric) can be estimated from the unit-mass-normalized modal data as shown below.
Whereby ωi is the ith modal frequency, φi the ith unit-mass-normalized mode, n is the number of measured modes and the asterisks signify property of the damaged structure. The equations presented above are estimations because fewer modes are typically identified as compared to the total number of measurements degrees or points of freedom.
Whereby  represents  the change  in the flexibility matrix. For a column with  matrix  is defined to be the absolute maximum value  of the elements in the given column (Guo, Xiao, and Yao, 2011). Thus
Whereby  are the elements of the matrix  and  is then taken as the measure of  flexibility change  at each given measurement location.
Dynamic response of a moving vehicle passing over a bridge
by considering a moving vehicle over a bridge as a sprung mass and bridge as a simply supported as shown in the figure below, the following equation was generated.
Whereby mv, and qv are mass, stiffness, and displacement of sprung mass respectively and qb |x=vt is beam deflection where sprung mass is located. By further expressing Eq. 2 by taking into account the contact force between this simply supported beam and sprung mass.
Whereby wvis the natural frequency  of a vehicle and it is usually given by wv =Ö(kv/mv), t is the time , v is the velocity of the sprung mass and L is the length of the beam.
Fig 2: Vehicle as a sprung mass and the bridge as a simply supported beam.
If the mass of the vehicle is considerably less than the mass of the bridge, then the acceleration of the vehicle can be computed by use of the following equation.
Whereby g is the ratio of the frequencies of the bridge  and vehicle, µ = wb/wv and Dst is deflection at mid-span of the beam. By simplifying equation 3, we can obtain equation 4 as shown below.
Whereby A1, A2, A3, and A4 are relative contributing components of the acceleration of the vehicle as illustrated by equation 4 above that there are three key components of the response of the moving vehicle i.e.
i ) driving frequency

ii) natural frequency of the bridge

iii) frequency of the vehicle (Park, Lee, and Myung, 2010)
In most situations, the vibration of bridges is not dominant, and they contribute little to vehicle response. This can be very problematic in the case of a speeding car. In such a scenario, the natural frequency of the bridge can be helpful to establish corresponding bridge mode shape s information. Furthermore, the natural frequency of a bridge component can be utilized for the detection of damage through the identification of a difference in the natural frequency and the damage localization in bridge mode shapes. Moreover, the moving component is very sensitive to any kind of deterioration in the bridge and can be utilized for that damage localization. Thus, when a moving vehicle passes over the deteriorated portion of the bridge, discontinuity can be detected in the driving component of the vehicle response. Nevertheless, driving frequency component of the vehicle is much lesser in total response, and thus its detection in total response is very challenging (Ou and Li, 2010).
Indirect bridge identification methods 
These type of identification methods are based on the dynamic properties of a bridge such as the natural frequency, model shapes and damping ratio parameters. Most of the vibration-based health monitoring methods are dependent on these modal parameters. As discussed below.
Bridge Natural Frequency
Bridge health monitoring based on bridge natural frequency by vehicle-bridge interaction was first proposed by Yang et al. (2004) and further developed by Yang and Lin (2005). Thereafter, the significant amount of investigations and researches have been carried out, and abundant works of literature have been published in order to determine natural bridge frequency from the indirect approach of bridge health monitoring through various numerical and experimental case studies (Cross et al. 2013).
Numerical Studies
From the parametric investigations that have been successfully carried out. It clearly shows that the value of shifted bridge frequency peaks relative to vehicle frequency peak is critical in the process of extracting more bridge frequencies. This indicates the significance of initial acceleration amplitude ratio of vehicle and bridge in the extraction process of bridge frequencies. Smaller the ratio, higher the chances of extraction of bridge frequencies. The vehicle pre-processing measurement technique empirical mode decomposition-EMD identifies higher modes of bridge natural frequencies. So, initial natural frequencies are extracted in numerical investigations, and higher modes are identified in detailed experimental investigations. In further investigations, bridge natural frequency and stiffness are measured more precisely with the newly proposed approach of vehicle-bridge interaction system. However, a more popular, Frequency Domain Decomposition-FDD modal analysis approach identifies both bridge and vehicle frequencies more effectively with consideration of close bridge and road profile surface.
Bridge Damping 
To determine bridge damping using vehicle bridge interaction, many efforts have been made. (Behnia, Chai and Shiotani,2014).  illustrates that value of power spectral density-PSD in vehicle acceleration spectrum and bridge damping are inversely proportional. (Behnia, Chai and Shiotani,2014).   verify this by conducting lab investigations by taking similar bridge crossings and three distinct values of vehicle speed. Furthermore, new six-step algorithm method is presented by (Behnia, Chai and Shiotani,2014).   to know bridge damping using a moving vehicle. Further study suggests that this algorithm-based approach eliminates the negative effects of road surface profile.
In conclusion, the key objectives of Structural Health Monitoring are to carefully monitor structural behavior due to varying loading conditions, identify any damage, location and nature of the damage and to assist the inspection of the structure. Therefore, Structural Health Monitoring can perform a key role in maintaining and managing these infrastructure systems due to various security and economic safety reasons.
At present, there are two main methods for Structural Health Monitoring of bridges, i.e., direct approach and indirect approach. Most of the present monitoring methods are direct approaches in which sensors are fixed at specific locations of the bridges and data is obtained from them. In this method of calculating natural bridge frequency and damping ratio of the bridge, it is observed that those values can be determined using bridge natural frequency component of vehicle response. However, the road surface profile still affects the accuracy of the results.
Majority of the techniques that are used in the Structural Health Monitoring of the speed of the vehicle is taken as constant. However, it is not possible to carry the monitoring procedure with the constant speed, and therefore results are affected. Hence, there is a future scope of speed variations in bridge health monitoring studies. Finally, the environment where the bridge is located plays a very crucial role on the bridge damage. This environmental effects can be incorporated by using a vehicle that passes through the same places regularly, and in such a way environmental impacts can be omitted (Huston,2010).
Behnia, A., Chai, H. K., & Shiotani, T. (2014). Advanced structural health monitoring of concrete structures with the aid of acoustic emission. Construction and Building Materials, 65, 282-302.
Cross, E. J., Koo, K. Y., Brownjohn, J. M. W., & Worden, K. (2013). Long-term monitoring and data analysis of the Tamar Bridge. Mechanical Systems and Signal Processing, 35(1-2), 16-34.
de Dios OrtÃozar, J., & Willumsen, L. G. (2011). Modelling transport. John Wiley & Sons.
Farrar, C. R., & Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley & Sons.
Guo, H., Xiao, G., Mrad, N., & Yao, J. (2011). Fiber optic sensors for structural health monitoring of air platforms. Sensors, 11(4), 3687-3705.
Huston, D. (2010). Structural sensing, health monitoring, and performance evaluation. CRC Press.
Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J. A., Sim, S. H., … & Agha, G. (2010). Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation.
Leung, A., Tanko, M., Burke, M., & Shui, C. S. (2017). Bridges, tunnels, and ferries: connectivity, transport, and the future of Hong Kong’s outlying islands. Island Studies Journal, 12(2), 61-82.
Magalhães, F., Cunha, A., & Caetano, E. (2012). Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mechanical Systems and Signal Processing, 28, 212-228.
Okasha, N. M., & Frangopol, D. M. (2012). Integration of structural health monitoring in system performance based life-cycle bridge management framework. Structure and Infrastructure Engineering, 8(11), 999-1016.
Ou, J., & Li, H. (2010). Structural health monitoring in mainland China: review and future trends. Structural Health Monitoring, 9(3), 219-231.
Park, J. W., Lee, J. J., Jung, H. J., & Myung, H. (2010). Vision-based displacement measurement method for high-rise building structures using a partitioning approach. Ndt & E International, 43(7), 642-647.

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