No abstract

This paper presents a study on detecting structural deterioration in existing buildings using ambient vibration measurements. Deterioration is a slow and progressive process which reduces the structural performance, including load-bearing capacity. Each building has unique vibration characteristics which change in time due to deterioration and damage. However, the changes due to deterioration are generally subtler than changes due to damage. Examples of deterioration include subtle loss of steel-concrete bond strength, slight corrosion of reinforcement and onset of internal cracks in structural members. Whereas damage can be defined as major sudden structural changes, such as major external cracks of concrete covers. Herein, a deterioration detection method which uses structural health monitoring (SHM) data is proposed to address the deterioration assessment problem. The proposed novel vibration-based deterioration identification method is a parametric-based approach, incorporated with a nonparametric statistical test, to capture changes in the dynamic characteristics of structures. First, autoregressive (AR) time-series models are fitted to the vibration response time histories at different sensor locations. A sensitive deterioration feature is proposed for detecting deterioration by applying statistical hypotheses of two-sample f-test on the model residuals, based on which a function of the resulting P-values is calculated. A novel AR model order estimation procedure is proposed to enhance the sensitivity of the method. The performance of the proposed method is demonstrated through comprehensive simulations of deterioration at single and multiple locations in finite element models (FEM) of 3 and 20-storey reinforced concrete (RC) frames. The method shows a promising sensitivity to detect small levels of structural deterioration prior to damage, even in the presence of noise.

The main objective of this study is strengthening of flat slabs in punching shear with a new model. For this purpose 15 numerical samples include a control and 14 strengthened defined and nonlinearly analyzed up to failure. The strengthening method is new method of grooving in two orthogonal direction (x and y axes of slab plan) and then mounting the external bars (sticking) in one direction and FRP in EBROG (externally bonded reinforcement on groove) method in another direction. The results showed the great efficiency of the method so that the punching shear capacity of strengthened samples increased between 28 and 62% compare to control one. Also the mode of failure changed in strengthened specimens from shear to flexure-shear and the punching critical area was broader and developed from the loading point. In the grooving method the sticking bars yielded in all specimens and the FRP tensile strain was closer to rupture strain compared to EBR.

Irrespective to how well structures were built, they all deteriorate. Herein, deterioration is defined as a slow and continuous reduction of structural performance, which if prolonged can lead to damage. Deterioration occurs due to different factors such as ageing, environmental and operational (E&O) variations including those due to service loads. Structural performance can be defined as load-carrying capacity, deformation capacity, service life and so on. This paper aims to develop an effective method to detect and locate deterioration in the presence of E&O variations and high measurement noise content. For this reason, a novel vibration-based deterioration assessment method is developed. Since deterioration alters the unique vibration characteristics of a structure, it can be identified by tracking the changes in the vibration characteristics. This study uses enhanced autoregressive (AR) time-series models to fit the vibration response data of a structure. Then, the statistical hypotheses of chi-square variance test and two-sample [Formula: see text]-test are applied to the model residuals. To precisely evaluate changes in the vibration characteristics, an integrated deterioration identification (DI) is defined using the calculated statistical hypotheses and a Hampel filter is used to detect and remove false positive and negative results. Model residual is the difference between the predicted signal from the time series model and the actual measured response data at each time interval. The response data of two numerically simulated case studies of 3-storey and 20-storey reinforced concrete (RC) shear frames contaminated with different noise contents demonstrate the efficacy of the proposed method. Multiple deterioration and damage locations, as well as preventive maintenance actions, are also considered in these case studies. Furthermore, the method was successfully verified utilizing measured data from an experiment carried out on a box-girder bridge (BGB) structure.

No abstract