Regression models for structural health monitoring of welded bridge joints based on temperature, traffic and strain measurements

Abstract

A modelling platform based on regression analysis is developed as a novel approach to structural health monitoring of welded joints of orthotropic bridge steel decks. Monitoring outcomes from the Great Belt Bridge (Denmark) are used to develop regression models following a weighted least squares approach to characterize the normal correlation pattern between environmental conditions (daily-averaged pavement temperatures), operational loads (daily-aggregated heavy traffic counts) and a strain-based performance indicator. The developed models can be used within a structural health monitoring–based asset management framework for performance assessment (i.e. diagnosis of structural performance changes) and performance prediction (i.e. prognosis of structural performance leading to service life estimates). The main novelty of the work presented consists of the development of an algorithm based on statistical control charts related to the prediction bands of the regression models. The algorithm enables the interpretation of new monitoring data and the identification of potentially abnormal behaviours via outlier detection, as part of an envisaged ‘real-time’ performance assessment application. The proposed approach to outlier detection through structural health monitoring is finally illustrated considering actual monitoring outcomes from the bridge. This highlights the applicability of the developed modelling platform and contributes to bridging the gap between monitoring data and monitoring-based information that can lead to more effective asset management decisions

Interpretation of sensor data from in situ tests on a transversely bonded fibre-reinforced polymer road bridge

“…Strain sensors are of fundamental importance to the monitoring process. De Freitas et al [17] showed how short and long-term monitoring of an in-service orthotropic steel deck bridge was enhanced by use of such sensors, while Farreras-Alcover et al [18] illustrated how potentially abnormal behaviour of a bridge may be detected by applying statistics to new strain monitoring data from the structure.…”

Bearing assessment tool for longitudinal bridge performance

“…The assessment methodology for outlier detection developed in this work belongs to the unsupervised learning domain. This is the most common situation in SHM strategies to inservice structures since data from possible damage states are rarely available [11,56,76]. The main goal of this approach is to detect deviations from what is considered to be the reference state, which is statistically defined as the baseline model.…”

“…In [55] and [56], authors focus on temperature-displacement correlation analysis and regression models to remove environmental effects and normalize displacements. The recent work [57] investigates the longitudinal behavior of a jointless railway bridge and defines regression models to remove the temperature-induced displacements and implement a robust early warning system.…”

Numerical Analysis of Diaphragm Fatigue of Reinforced Concrete Simply Supported T-beams

“…On the basis of Miner theory and Corten-Dolan damage theory, Zhu [10] established a bridge bearing capacity-rigidity degradation correlation model, but the load spectrum application area was limited. Farreras-Alcover [11][12] constructed a fatigue reliability model of orthotropic bridge decks by conducting ambient temperature, traffic, and strain monitoring and derived S-N curves applicable to strain monitoring. Yan [13] comprehensively analyzed the fatigue performance decay characteristics of different reinforced concrete bridge decks through numerical simulation and attributed concrete fatigue facture to residual strain accumulation; however, the impacts of durability factors on fatigue analysis were not considered.…”