Special issue on Structural Health Monitoring of Civil Structures

Ching-Tai Ng, Tommy H.T. Cha

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element model of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures

Damage detection in hyperbolic cooling towers using vibration based damage detection techniques

Hyperbolic cooling towers are large reinforced concrete shell structures used to cool the hot waste water from thermal power stations, nuclear power plants, coal fried plants, chemical plants and oil refineries. Hence, they are subjected to large temperature variations. This together with deterioration due to aging, environmental effects and random actions such as wind loading and earthquakes, can cause distress in these structures. Such a distress can go undetected as most part of the cooling tower especially its interior, is neither visible from the outside nor easily accessible. This distress can develop into a larger damage to trigger the failure or collapse of the cooling tower and disrupt the functioning of the entire facility or plant. All structures need to be monitored regularly to check whether they are safe to operate and capable of withstanding environmental impacts. In this context Structural Health Monitoring using vibration based damage detection techniques has emerged as a useful means to detect damage at the outset to enable appropriate retrofitting and minimize structural failure. The principle of these techniques is that the damage in a structure causes a change in its vibration properties and this change can be used to detect the damage in the structure. Vibration based damage detection techniques have been used to detect, locate and characterize the damage in many simple and some complex structures. This paper develops and applies vibration based damage indicators to detect and locate damage in hyperbolic cooling towers

Statistical analyses of weigh-in-motion data for bridge live load development

This paper discusses the statistical analyses used to derive bridge live loads models for Hong Kong from a 10-year weigh-in-motion (WIM) data. The statistical concepts required and the terminologies adopted in the development of bridge live load models are introduced. This paper includes studies for representative vehicles from the large amount of WIM data in Hong Kong. Different load affecting parameters such as gross vehicle weights, axle weights, axle spacings, average daily number of trucks etc are first analyzed by various stochastic processes in order to obtain the mathematical distributions of these parameters. As a prerequisite to determine accurate bridge design loadings in Hong Kong, this study not only takes advantages of code formulation methods used internationally but also presents a new method for modelling collected WIM data using a statistical approach

Bridge live load models from WIM data

This paper describes the development of a new methodology for deriving highway bridge live load models for short span bridges. The development is based on a 'repeatable' methodology to obtain extreme daily moments and shears using 10 years Hong Kong weigh-in-motion (WIM) data as compared to the traditional normal probability paper approach. The methodology can also be applied to the development of bridge live loading models in other parts of the world. Two types of loading are proposed. A methodology based on the equivalent base length concept is used to derive the lane loading model and the standard truck loading model is developed based on a statistical approach. The developed lane and truck loadings are compared with other loading models adopted locally and overseas. It is the first time in Hong Kong the bridge design loading is developed based on actual acquired load data using statistical and probabilistic approach

Damage detection in slab-on-girder bridges using vibration characteristics

This paper develops and applies a multi-criteria procedure, incorporating changes in natural frequencies, modal\ud flexibility and the modal strain energy, for damage detection in slab-on-girder bridges. The proposed procedure\ud is first validated through experimental testing of a model bridge. Numerically simulated modal data obtained\ud through finite element analyses are then used to evaluate the vibration parameters before and after damage and\ud used as the indices for assessment of the state of structural health. The procedure is illustrated by its application to full scale slab-on-girder bridges under different damage scenarios involving single and multiple damages on the deck and girders

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Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution

Application of Gaussian process metamodel in structural finite element model updating applying dynamic measured data

Civil infrastructure is vital linking component whose behavior is necessary to be monitored continuously since any fault in performance will cause significant risks. Recently, structural health monitoring (SHM) has obtained a significant contribution in preparing information related to structural behavior during functional life. Though, determining real infrastructure's behavior is intricate, since it relies on structural parameters that cannot be obtained directly from observed data and such identification is prone to uncertainties. Finite element model updating (FEMU) is an approach to address this issue. The current study employs a Modular Bayesian approach (MBA) to update a finite element model (FEM) of a lab-scaled box girder bridge applying natural frequencies. This approach is performed in two stages as undamaged and damaged. These stages can be denoted as the change in structural parameters due to incidences such as impact or fatigue effect. The performed MBA deals with uncertainties thoroughly in all steps. In this study, a discrepancy function is applied to detect the discrepancy in natural frequencies between the FEM and the experimental counterpart. A Gaussian process (GP) is used as a metamodel for the simulated model and the model discrepancy function. In this research, updating the initial FEM of the lab-scale Box Girder Bridge (BGB) by calibrating multi parameters is highlighted. Results specify a considerable drop in stiffness of concrete in damaged phase which is well matched with the cracks observed on the structure's body. Also, discrepancy records reach satisfying range in both stages which implies the structure's properties are predicted accurately.</p

Methodology for measuring the vertical displacements of bridges using fibre bragg grating sensors

In many bridges, vertical displacements are one of the most relevant parameters for structural health monitoring in both the short- and long-terms. Bridge managers around the globe are always looking for a simple way to measure vertical displacements of bridges. However, it is difficult to carry out such measurements. On the other hand, in recent years, with the advancement of fibre-optic technologies, fibre Bragg grating (FBG) sensors are more commonly used in structural health monitoring due to their outstanding advantages including multiplexing capability, immunity of electromagnetic interference as well as high resolution and accuracy. For these reasons, a methodology for measuring the vertical displacements of bridges using FBG sensors is proposed. The methodology includes two approaches. One of which is based on curvature measurements while the other utilises inclination measurements from successfully developed FBG tilt sensors. A series of simulation tests of a full-scale bridge was conducted. It shows that both approaches can be implemented to measure the vertical displacements for bridges with various support conditions, varying stiffness along the spans and without any prior known loading. A static loading beam test with increasing loads at the mid-span and a beam test with different loading locations were conducted to measure vertical displacements using FBG strain sensors and tilt sensors. The results show that the approaches can successfully measure vertical displacements