Performance assessment of Jiangyin Bridge using long-term structural health monitoring data

2010 ◽  
pp. 587-587
Author(s):  
H Zhou ◽  
Y Ni ◽  
Y Guo ◽  
J Ko
Keyword(s):  
Structural Health Monitoring ◽  
Performance Assessment ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Structural Health

Quantitatively linking long-term monitoring data to condition ratings through a reliability-based framework

2020 ◽  
pp. 147592172094996
Author(s):  
Katherine A Flanigan ◽  
Jerome P Lynch ◽  
Mohammed Ettouney
Keyword(s):  
Structural Health Monitoring ◽  
Lower Limit ◽  
Health Monitoring ◽  
Reliability Index ◽  
Monitoring Data ◽  
Limit States ◽  
Structural Health ◽  
Long Term Monitoring ◽  
Term Monitoring

The holy grail of structural health monitoring is the quantitative linkage between data and decisions. While structural health monitoring has shown continued growth over the past several decades, there is a persistent chasm between structural health monitoring and the ability of structure owners to make asset management decisions based on structural health monitoring data. This is in part due to the historical structural health monitoring paradigm cast as a problem of estimating structural state and detecting damage by monitoring changes in structural properties (namely, reduced stiffness). For most operational structures, deterioration does not necessarily correspond to changes in structural properties with structures operating in their elastic regimes even when deteriorated. For structures like bridges, upkeep decisions are based on federally mandated condition ratings assigned during visual inspection. Since condition ratings are widely accepted in practice, the authors propose that condition ratings serve as lower limit states (i.e. limit states below yielding) with long-term monitoring data used to quantify these lower limit states in terms of the reliability index. This article presents a method to quantify the reliability index values corresponding to the lower limit states described by existing condition ratings. Once the reliability index thresholds are established, the data-driven reliability index of the in-service asset can be monitored continuously and explicitly mapped to a condition rating at any time. As an illustrative example, the proposed framework for tracking structural performance is implemented with long-term monitoring data collected on a pin-and-hanger assembly on the Telegraph Road Bridge, which is a highway bridge located in Monroe, MI. The successful implementation of the proposed method on the Telegraph Road Bridge results in a human-independent and truly data-driven decision-making strategy that is synergistic with the state of practice, eliminates risks associated with infrequent visual inspections, and expands condition ratings to encompass the entire measurable domain of damage that may exist in an asset.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

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