Institute of Fundamental Technological Research

Damage identification forms a key objective in structural health monitoring. Several state-of-the-art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable-stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory-scale implementations, full-scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next-generation structural health monitoring systems.

arch bridge, beam bridge, cable‐stayed bridge, damage identification, suspension bridge, truss bridge

In vibration-based damage identification, a common problem is that modal information is not enough and insensitive to local damage. To solve this problem, an effective method is to increase the amount of modal information and enhance the sensitivity of the experimental data to the local damage. In this paper, a damage identification method based on additional virtual masses and Bayesian theory is proposed. First, the virtual structure with optimal additional mass and high sensitivity to local damage is determined through sensitivity analysis, and then a large number of virtual structures can be obtained by adding virtual masses; thus, a lot of modal and statistical information of virtual structures can be obtained. Second, the Bayesian theory is used to obtain the posterior probability distribution of the damage factor when structural a priori information is considered. Third, by finding the extreme value of the probability density function, the damage factor is derived based on the a priori information and the statistical information of virtual structures. Finally, the effectiveness of the proposed method is verified by numerical simulations and experiments of a 3-story frame structure. Experimental and numerical results show that the proposed method can be used to identify the damage severity of each substructure and thus damaged substructures can be localized and quantified; the error in damage factor is basically within 5%, which shows the accuracy of the proposed method. The proposed method can not only provide the structural damage localization and quantification result (i.e., the damage factor), but also the probability distribution of the damage factor; moreover, it has high sensitivity to damage and high accuracy and efficiency.

Structural health monitoring, Damage identification, Bayesian theory, Virtual distortion method (VDM), Virtual mass

This work proposes an efficient and reliable method for damage localization in truss structures. The damage is localized on the basis of measured acceleration signals of the structure followed by simple statistical signal processing. It has three main advantages over many existing methods. Firstly, it can be directly applied to real engineering structures without the need of identifying modal parameters or solving any global optimization problem. Secondly, the proposed method has higher sensitivity to damage than some other frequently used methods and allows to localize damage as small as a few percents. Thirdly, it is a model-free method, which does not require precise finite element model development or updating. Validation of the method has been conducted on numerical examples and laboratory-scale trusses. Two types of frequently used trusses have been selected for this study, namely Howe and Bailey trusses. The presented experimental validation of the method shows its efficiency and robustness for damage localization in truss structures.

structural health monitoring, truss structures, damage detection

The purpose of this work is the development of an efficient and high-sensitive damage localization technique for truss structures, based on the rank-revealing QR decomposition (RRQR) of the difference-of-flexibility matrix. The method is an enhancement of the existing techniques of damage detection, which rely on the set of so-called damage locating vector (DLV). The advantages of the RRQR decomposition-based DLV (RRQR-DLV) method are its less computational effort and high sensitivity to damage. Compared with the frequently used stochastic DLV (SDLV) method, RRQR-DLV offers higher sensitivity to damage, which has been validated based on the presented numerical simulation. The effectiveness of the proposed RRQR-DLV method is also illustrated with the experimental validation based on a laboratory-scale Bailey truss bridge model. The proposed method works under ambient excitation such as traffic excitation and wind excitation; therefore, it is promising for real-time damage monitoring of truss structures.

damage localization, rank-revealing QR decomposition, damage sensitivity, truss structure, structural health monitoring

We present an efficient and robust damage localization method. Its applications therefore include defect location in shear buildings and beam structures. The proposed method is based on the knowledge of the difference of curvatures, computed for a structure before and after damage occurs. However, instead of using modal shapes for this purpose, as is frequently performed, the present method computes the curvature directly from acceleration signals, without identifying modal shapes of the structure. Additionally, the accelerations are subjected to averaging, which reduces measurement noise, and logarithm extraction, which renders the method independent of the amplitude of the loading impulse used for damage location. Another important feature of the method is that it does not require any calibration of numerical models, because it is solely based on measurement data. The presented method of damage location is illustrated with two examples, which involve experimental tests on laboratory-scale structures. The first example concerns defect location in a shear-building structure, and the second one in a spatially excited simply supported steel beam. Both cases confirm the effectiveness of the method, and its robustness to measurement noise.

degree of dispersion, transient response-based damage detection, robust damage localization, shear buildings, beam structures

The purpose of this study is to introduce a new method of damage quantification for truss structures. Its advantage is that it can be directly applied to engineering structures without identifying modal parameters or solving a global optimization problem. The damage is localized and quantified based only on measured acceleration signals, distributed across the structure. Moreover, the method is implemented in a decentralized way rather than a centralized one; that is, for quantification of damage in a given substructure, only a small subset of sensors is considered. The method possesses higher sensitivity to damage than other frequently used methods such as Damage Locating Vectors. Validation of the method has been conducted on a numerical example and a laboratory-scale model of a truss bridge, showing its efficiency and robustness.

structural health monitoring, damage detection, truss structures

Real-time structural health monitoring is very important for truss structures especially those having large-spans. In recent years, many methods have been proposed for damage monitoring of truss structures. However, damage sensitivity of these methods is still required to be improved. In this work an efficient damage localization technique for truss structures is proposed, which is based on the LDLT decomposition of the flexibility difference matrix and the Damage Locating Vectors (DLV) method. Compared with the present Stochastic DLV (SDLV) method, the proposed method is modified in two ways. First of all, the way of calculating the damage locating vectors is modified by using LDLT decomposition instead of Singular Value Decomposition. Secondly, in order to compute the flexibility, the mass matrix which is obtained from the finite element model is used to mass-normalize mode shapes identified from ambient excitations. As a result, the proposed LDLT-DLV method has a higher sensitivity to damage for different types of truss members. The effectiveness of the proposed LDLTDLV method is validated with the numerical example of a laboratory-scale Bailey truss bridge.

Damage localization, damage localization, LDLT-DLV method, truss structure, structural health monitoring