System identification for structural engineering has received significant attention in the last thirty years. With the ever increasing capacity of computing technology, system identification has been applied to important structures such as bridges and aircraft. In the case of bridges, the output can easily be measured by accelerometers. Considerable research in system identification on bridges has been done using output-only models. Of course, it is difficult to measure the inputs on an in-service bridge. In this paper, we see how the inputs can be estimated from the output measurements. We then use an input-output model to develop an improved system identification technique for identifying bridges. We show that the proposed method using the estimated inputs yields superior identification in a simulated case (i.e., where everything is controlled). We then use the method on the in-service Walnut Creek Bridge located on the north-bound lanes of U.S. Interstate I-35 between Dallas, TX and Oklahoma City, OK.
The 5 DOF coordinate measuring arm is an equipment used to determine the coordinate of points in 3D space at the tangent point of the probe and object. The calculating of the coordinate at the tip of the probe is based on forward kinematic equation of a 5 DOF robot arm, by defining the transpose of the 5 knuckles. Accordingly, we can calculate and measure some geometric elements, such as distance, angle...rebuild surface of object and store data in files. So that, the equipment can be applied in many fieldS: measuring, examining, molding, reverse engineering…
This paper describes algorithms that fit geometric shapes to data sets according to maximum- inscribed (MI) and minimum- circumscribed (MC) fit. We use these fits to build the CMM’s (Coordinate Measuring Machine) software in cases of circle, sphere and cylinder. For each case, we obtain the fit by two methods: first, by (relative easy) least squares fit method and then refine by MI and MC fit method. Although, the later method is substantially more complicated than the former one, Its results are used to make comparision with the the results of least squares method in order to give more options in the CMM software. In the near future we will continue to develop MI and MC fit with an effective algorithm- Simulated Annealing algorithm.
Two most important requirements of Coordinate Measuring Machines (CMM) are the accuracy and the traceability. However, after a long period of use, errors caused by dynamic forces, thermal expansion, loads, etc can decrease the accuracy as well as the traceability. Therefore, CMM is calibrated to minimize these errors as small as possible. First at all, a geometric error model of CMM is proved mathematically. A method of determining 21 parametric errors by using a Hole Plate then is presented. In addition, a back-propagation algorithm is introduced to approximate parametric errors of all points in the CMM working volume. Finally, the proposed calibration method is demonstrated experimentally.
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