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Dynamic strain-based methods are promising for bridge status assessment; however, the influences of vehicle parameters variations on their capabilities for damage identification are usually indeterminate. In this paper, parametric study for highway bridge rapid assessment method is conducted under moving vehicle loads, which is based on the distributed macro-strain envelope line. First, the relationship between the average bending stiffness and macro-strain history is studied according to the theory of the strain influence line. To determine the influence of different vehicle parameters (velocity, weight and axle numbers) on the method, a 1:10 scale model of a bridge-vehicle system is established for the parametric experimental study. A type of long-gauge Fiber Bragg Grating (FBG) sensor is used in the experiment. The empirical mode decomposition method (EMD) is applied to extract the static macro-strain component, and the maximum values of the static macro-strain histories are accumulated as the long-gauge strain envelope line. The experimental results show that the proposed method not only effectively identifies the location of damage but also damage extent and is not affected by the parameters of the vehicle. This method has engineering significance in bearing capacity evaluation and post-bridge structure reinforcement design.

Structural health monitoring is an important means of obtaining the state information of bridges, and the extracted quasi-static strain signal can reflect the stress state of bridges directly. However, the strain signals acquired during the operation stage of bridges are dynamic, and the strain gauges used in the health monitoring system are short (no more than 10 cm), which means they are easily affected by small damage at the installation parts of bridges and thereby the monitoring signal abnormalities occur. A type of externally affixed long-gauge fiber strain gauge is used to monitor the health of bridges, and the dynamic and quasi-static signal separation method for long-gauge strain sensors is studied under different vehicle loads; at the same time, the dynamic monitoring performance of the long-gauge sensor is investigated in this paper. The quasi-static strain signal extracted from the dynamic macro-strain signal can be used to directly monitor the stress status of the bridge. The results show that the method proposed in this paper is feasible for extracting the quasi-static macro-strain from a dynamic long-gauge strain signal.

A novel damage identification method for concrete continuous girder bridges based on spatially-distributed long-gauge strain sensing is presented in this paper. First, the variation regularity of the long-gauge strain influence line of continuous girder bridges which changes with the location of vehicles on the bridge is studied. According to this variation regularity, a calculation method for the distribution regularity of the area of long-gauge strain history is investigated. Second, a numerical simulation of damage identification based on the distribution regularity of the area of long-gauge strain history is conducted, and the results indicate that this method is effective for identifying damage and is not affected by the speed, axle number and weight of vehicles. Finally, a real bridge test on a highway is conducted, and the experimental results also show that this method is very effective for identifying damage in continuous girder bridges, and the local element stiffness distribution regularity can be revealed at the same time. This identified information is useful for maintaining of continuous girder bridges on highways.

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A finite model updating method that combines dynamic-static long-gauge strain responses is proposed for highway bridge static loading tests. For this method, the objective function consisting of static long-gauge stains and the first order modal macro-strain parameter (frequency) is established, wherein the local bending stiffness, density and boundary conditions of the structures are selected as the design variables. The relationship between the macro-strain and local element stiffness was studied first. It is revealed that the macro-strain is inversely proportional to the local stiffness covered by the long-gauge strain sensor. This corresponding relation is important for the modification of the local stiffness based on the macro-strain. The local and global parameters can be simultaneously updated. Then, a series of numerical simulation and experiments were conducted to verify the effectiveness of the proposed method. The results show that the static deformation, macro-strain and macro-strain modal can be predicted well by using the proposed updating model.

Currently, there have been plenty of researches conducted for two main problems in structural health monitoring, damage and vehicle parameter identification on bridges. However, only a handful of methods could achieve these two functions synchronously, which would cause the redundancy of sensors also the rise of cost in monitoring system. In this paper, a method to identify damage and vehicle parameters, such as axle load, wheelbase and velocity on a bridge simultaneously was proposed, based on the influence line of long-gauge strain. The influence line of long-gauge strain was derived primarily according to conventional strain influence line theory, at the basis of which the relationships among the local element bending stiffness of bridge, vehicle parameters and long-gauge strain were figured out then. The two order difference of long-gauge strain was chosen as the key index to found this identification method. Finally, to verify the reliability of this method, a set of numerical simulations was conducted, whose results showed that this method exhibited good performance.

Ambient vibration tests and analyses of a long-span rigid-frame bridge were conducted using distributed long-gauge fiber-optic sensors. The concept of a long-gauge fiber-optic sensor and it...

Fiber optic sensing technology has been widely used in civil infrastructure health monitoring due to its various advantages, e.g., anti-electromagnetic interference, corrosion resistance, etc. This paper investigates a new method for stiffness monitoring and damage identification of bridges under moving vehicle loads using spatially-distributed optical fiber sensors. The relationship between the element stiffness of the bridge and the long-gauge strain history is firstly studied, and a formula which is expressed by the long-gauge strain history is derived for the calculation of the bridge stiffness. Meanwhile, the stiffness coefficient from the formula can be used to identify the damage extent of the bridge. In order to verify the proposed method, a model test of a 1:10 scale bridge-vehicle system is conducted and the long-gauge strain history is obtained through fiber Bragg grating sensors. The test results indicate that the proposed method is suitable for stiffness monitoring and damage assessment of bridges under moving vehicular loads.