It has been shown that the variations of structural properties due to changing environmental conditions such as temperature can be as significant as those caused by structural damage and even liveload. Therefore, tracking changes that are correlated with environmental variations is a necessary step in order to detect and assess structural damage in addition to the normal structural response to traffic. In this paper, daily measurement data that is collected from the concrete towers of the Ironton-Russell Bridge will be presented and correlation of the collected measurement data and temperature will be overviewed. Variation of the daily thermal response of tower concrete walls will be compared with the daily thermal responses of the steel box within the tower and finally, thermal coefficient for compensating the thermal induced responses will be estimated.
Proc. SPIE. 9805, Health Monitoring of Structural and Biological Systems 2016
KEYWORDS: Data modeling, Bridges, Structural health monitoring, Nondestructive evaluation, Performance modeling, Time series analysis, Data processing, Detection and tracking algorithms, Sensors, Data acquisition, Temperature metrology, Safety, Astatine, Databases, Multiplexers
Monitoring a complex structure has gained popularity worldwide to ensure safety and longevity of the structure. Structural Health Monitoring (SHM) systems have been employed for highway bridges to increase the effectiveness of their in-service inspection, to help measure its degradation or damage, and hence, to ensure it’s safe and reliable operation. SHM may also be employed during the construction of a structure in order to ensure the safety and performance of the construction process. Monitoring during construction can also help designers compare the actual behavior of a structure with design models especially because of increasing development of accelerated or otherwise novel construction techniques. Analyzing the behavior of a structure at different stages of construction may also help later define some of the abnormal responses during the lifespan of a bridge. This paper overviews the SHM system of the Ironton-Russell Bridge, Ohio at the construction stage of its substructure. The stages involved in monitoring such as instrumentation of sensors, acquiring data from the sensors, data processing that includes a warning system, static analysis of the data collected and website are detailed in this paper. In addition to this, the effect of construction events as observed by the sensor data for the substructure is analyzed in detail thus validating the capability of the monitoring system.
Proc. SPIE. 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015
KEYWORDS: Autoregressive models, Data modeling, Bridges, Sensors, Structural health monitoring, Picture Archiving and Communication System, Data archive systems, Calibration, Statistical modeling, Machine learning
Data models have been increasingly used in recent years for documenting normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long term measurement data of structures and lack of access to the damaged states of structures. By monitoring the structure during construction and reviewing the effect of construction events on the measurement data, this study introduces a new approach to detect and eventually classify anomalies during construction and after construction. First, the implementation procedure of the sensory network that develops while the bridge is being built and its current status will be detailed. Second, the proposed anomaly detection algorithm will be applied on the collected data and finally, detected anomalies will be validated against the archived construction events.
Calibration of a finite element model based on measurement-data for complex structures is usually costly and sometimes not applicable. In this article, a methodology for detecting abnormal behavior including slow aging degradations of a structure solely based on historical patterns of the measurement data will be introduced. In the first step, principal components of the truck load test measurement data - that is centered and scaled - are calculated. In the second step, unsupervised classification is applied to the score data that is regenerated based on the major principal components. The same algorithm is applied to the measurement data of the bridge response to the sharp temperature change as well. Finally, the specified algorithm is applied separately to the collected static data from the Jeremiah Morrow Bridge (more than four years) using the calculated truck load test principal components. The optimized clustering model detected the outliers that are caused by heavy truck loads; clustering result is detailed. In summary, a simple data model that is able to find any known data signature such as truck load test in the daily measurement data is proposed. The proposed method is part of an ongoing effort in University of Cincinnati Infrastructure Institute to use the correlation between collected readings from different members of a bridge in order to interpret abnormal trend changes in the measurement data.
In this paper, a methodology for integrating a monitor and its warning system with results from both truckload tests
and structural analysis is presented for the Jeremiah Morrow Bridge. Wireless data collection system, data cleansing
and archiving procedures, Eigen vector prediction model, and capacity rating based upon truckload test results for
the instrumented members are detailed. The truckload test and monitored responses document the “normal” or expected behavior of the structure to traffic and environment, respectively. A warning system is then designed upon a threshold technique which minimizes the probabilities of false alarms and the missed detection of critical events based upon the capacity rating. Past results of the implemented system for the Jeremiah Morrow and other bridges
are discussed. Alarm scenarios are reviewed based upon the collected historical data from the monitors and generated warnings.