Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations

Post-earthquake structural safety evaluations are currently performed manually by a team of certified inspectors and/or structural engineers. This process is time-consuming and costly, keeping owners and occupants from returning to their businesses and homes. Automating these evaluations would enable faster, and potentially more consistent, relief and response processes. In order to do this, the detection of exposed reinforcing steel is of utmost significance. This paper presents a novel method of detecting exposed reinforcement in concrete columns for the purpose of advancing practices of structural and safety evaluation of buildings after earthquakes. Under this method, the binary image of the reinforcing area is first isolated using a state-of-the-art adaptive thresholding technique. Next, the ribbed regions of the reinforcement are detected by way of binary template matching. Finally, vertical and horizontal profiling are applied to the processed image in order to filter out any superfluous pixels and take into consideration the size of reinforcement bars in relation to that of the structural element within which they reside. The final result is the combined binary image disclosing only the regions containing rebar overlaid on top of the original image. The method is tested on a set of images from the January 2010 earthquake in Haiti. Preliminary test results convey that most exposed reinforcement could be properly detected in images of moderately-to-severely damaged concrete columns.

The vibro-acoustic analysis of built-up systems using a hybrid method with parametric and non-parametric uncertainties

An existing hybrid finite element (FE)/statistical energy analysis (SEA) approach to the analysis of the mid- and high frequency vibrations of a complex built-up system is extended here to a wider class of uncertainty modeling. In the original approach, the constituent parts of the system are considered to be either deterministic, and modeled using FE, or highly random, and modeled using SEA. A non-parametric model of randomness is employed in the SEA components, based on diffuse wave theory and the Gaussian Orthogonal Ensemble (GOE), and this enables the mean and variance of second order quantities such as vibrational energy and response cross-spectra to be predicted. In the present work the assumption that the FE components are deterministic is relaxed by the introduction of a parametric model of uncertainty in these components. The parametric uncertainty may be modeled either probabilistically, or by using a non-probabilistic approach such as interval analysis, and it is shown how these descriptions can be combined with the non-parametric uncertainty in the SEA subsystems to yield an overall assessment of the performance of the system. The method is illustrated by application to an example built-up plate system which has random properties, and benchmark comparisons are made with full Monte Carlo simulations. © 2012 Elsevier Ltd. All rights reserved.