Deterioration assessment of buildings using an improved hybrid model updating approach and long-term health monitoring data

Abstract

In recent years, it has become increasingly important to develop methodologies for reliable deterioration assessment of civil structures over their life cycle to facilitate maintenance and/or rehabilitation planning processes. Several approaches have been established to address this issue mainly using Bayesian probabilistic model updating techniques with some capability to incorporate uncertainties in the updating process. However, Bayesian model updating techniques are often found to be complex and computationally inefficient as opposed to their deterministic counterparts such as conventional or hybrid techniques of sensitivity-based model updating. Nevertheless, the deterministic model updating techniques have not been well developed for sophisticated assessment applications such as deterioration evaluation. To address these issues, this article presents a novel methodology for deterioration assessment of building structures under serviceability loading conditions, based upon an improved hybrid model updating approach incorporating the use of long-term monitoring data. This is first realized by a simple but effective scheme to simulate the deterioration mechanism in serviceability loading conditions before enhanced with innovative solutions to classify structural elements as well as to handle measurement and updating uncertainties in a meaningful way. The effectiveness of the established methodology is illustrated through a benchmark 10-story reinforced concrete building which is equipped with a long-term structural health monitoring system.

Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework

“…1 ( ) = ℎ 1 ( ) = ( ) − ∑ 1 ( ) =1 (8) where ℎ 1 ( ) is the first IMF extracted from original signal. The remaining signal can be expressed as follows:…”

“…Accordingly, when the damage-sensitive features are affected by the aforementioned factors, it becomes extremely complex for damage identification in the structure. 7,8 Guided wave (GW)-based techniques have been widely used for pile-type structures, 9 which may also be a feasible solution to internal damage detection at unreachable locations of round poles based on GW propagation. In this technique, the structure is struck manually by a modal hammer which induces broadband and low frequency (long wavelength) input.…”

Fibre Bragg Grating Based Acoustic Emission Measurement System for Structural Health Monitoring Applications

“…It is known that civil, aerospace, marine, pipeline, and mechanical infrastructures deteriorate after being built due to ageing induced cracks, man-made hazards such as vehicle collisions and blasts, and natural disasters such as earthquakes or hurricanes [ 1 , 2 , 3 ]. These can have deleterious effects on the integrity of the engineering structures during their service life, leading to catastrophic operational failures.…”

Automated peak picking using region‐based convolutional neural network for operational modal analysis

“…A variety of modal identification techniques either in time or frequency domain have been proposed, including stochastic subspace identification (SSI), eigensystem realization algorithm (ERA), and frequency domain decomposition (FDD) . The obtained modal properties are further utilized in various structural health monitoring applications, such as finite element model updating, damage detection, and cable tension estimation . As such, modal analysis is a key component in many civil engineering applications that require the dynamic characteristics of a structure.…”