An instrumented vehicle fitted with accelerometers and GPS allows recording frequencies with position. This information can be used to determine the main frequencies of a bridge as the vehicle traverses it. The accelerations, that contain the dynamic excitation result of the interaction between vehicle and bridge, can be processed using spectrum analysis. Numerical simulations are used to define the accuracy of the predicted frequency for a number of scenarios. Accuracy typically gets better as the bridge span increases, the vehicle speed decreases and the road gets smoother. Experimental data is also employed to test this measuring technique.
Cross-entropy optimization has recently been applied to the damage detection in structures subject to static loading. The optimization procedure minimizes the error between the measured deflection data and theoretical deflection data obtained from artificially generated finite element models based on assumed statistical distributions of stiffness for each discretized element. Following a number of iterations, the finite element model with stiffness properties producing deflections closer to reality is established as the mathematical model closest to the true structure. However, while previous testing of the algorithm has been relatively successful, it has been limited to theoretical simulations. Therefore, this paper conducts lab experiments on a beam loaded statically to test the accuracy of the algorithm. Deflections are measured for beam scenarios under different loading levels. The accuracy of the results is discussed and recommendations are made to improve the performance of the algorithm when implemented in practice.
The present paper develops a series of methods for the estimation of uncertainty when measuring certain measurands of interest in surveying practice, such as points elevation given a planimetric position within a triangle mesh, 2D and 3D lengths (including perimeters enclosures), 2D areas (horizontal surfaces) and 3D areas (natural surfaces). The basis for the proposed methodology is the law of propagation of variance–covariance, which, applied to the corresponding model for each measurand, allows calculating the resulting uncertainty from known measurement errors. The methods are tested first in a small example, with a limited number of measurement points, and then in two real-life measurements. In addition, the proposed methods have been incorporated to commercial software used in the field of surveying engineering and focused on the creation of digital terrain models. The aim of this evolution is, firstly, to comply with the guidelines of the BIPM (Bureau International des Poids et Mesures), as the international reference agency in the field of metrology, in relation to the determination and expression of uncertainty; and secondly, to improve the quality of the measurement by indicating the uncertainty associated with a given level of confidence. The conceptual and mathematical developments for the uncertainty estimation in the aforementioned cases were conducted by researchers from the AssIST group at the University of Oviedo, eventually resulting in several different mathematical algorithms implemented in the form of MATLAB code. Based on these prototypes, technicians incorporated the referred functionality to commercial software, developed in C++. As a result of this collaboration, in early 2016 a new version of this commercial software was made available, which will be the first, as far as the authors are aware, that incorporates the possibility of estimating the uncertainty for a given level of confidence when computing the aforementioned surveying measurands.
The estimation of flexural stiffness from static loading test data is the basis of many methods assessing the condition of structural elements. These methods are usually developed under the assumption of having sufficiently accurate data available. Hence, their performance deteriorates as the differences between the measured and true values of the response, often denoted as noise, increase. The proposed methodology is specifically designed to mitigate errors derived from noisy static data when estimating flexural stiffness. It relies on the linearization of the equations relating displacements to stiffness through the unit-force theorem, combined with regularization tools such as Lcurve and generalized cross-validation. The methodology is tested using theoretical simulations of the static response of a simply supported beam subjected to a 4-point flexural test for several levels of noise, two types of responses (deflections and rotations) and different levels of discretization. Recommendations for selecting the optimal regularization tool and parameter are provided. The use of rotations as inputs for predicting stiffness is shown to outperform deflections. Finally, the methodology is extended to a statically indeterminate beam.
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