Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Abstract

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

“…The 15 × 180 × 1060 mm sized columns were made of Q690 high-strength steel plates, and the 2.0 m × 2.0 m sized floors were of Q345 steel plates. The structure can be simplified into a 5-story shear frame as shown in Figure 9, and the structural dynamic equation can be described as Equation (2). In this case, the structural dynamics behavior can be determined by the parameter set θ = (m1, m2, …, m5, k1, k2, …, k5, ζ1, ζ2) with the mass matrices M = (1032 kg, 1032 kg, 1032 kg, 103 2kg, 829.9 kg) already known.…”

“…Therefore, it provides a reliable model to describe the current state of the structure [1]. Structural parameter identification is based on least squares or maximum likelihood criterion generally [2,3], which requires solving a nonlinear and nonconvex optimization problem. The error surface in this estimation problem is a hypersurface in the multidimensional parameter space, thereby the hypersurface potential is highly multimodal possessing local optimum.…”

A Case Study on the Planning and Scheduling of the Package Project Piplmc-6A Using Microsoft Project 2016

“…[2] The structural health of these buildings should be monitored to assess their ability to resist future threats. [16] The structural health of these buildings depends upon the effect of load, environmental erosion, material aging, accidental bumping on the structures and many other factors, which will ultimately lead to structural damage and destruction [17] one of the important parameter which effect the structural health of masonry buildings is modulus of elasticity.…”