scholarly journals Effective Stiffness Identification for Structural Health Monitoring of Reinforced Concrete Building using Hysteresis Loop Analysis

2017 ◽  
Vol 199 ◽  
pp. 1074-1079 ◽  
Author(s):  
Cong Zhou ◽  
J. Geoffrey Chase ◽  
Geoffrey W. Rodgers ◽  
Baofeng Huang ◽  
Chao Xu
Keyword(s):  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Hysteresis Loop ◽  
Health Monitoring ◽  
Effective Stiffness ◽  
Loop Analysis ◽  
Reinforced Concrete Building ◽  
Structural Health ◽  
Stiffness Identification ◽  
Concrete Building

A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring

2020 ◽  
pp. 147592172092064 ◽  
Author(s):  
Cong Zhou ◽  
J Geoffrey Chase
Keyword(s):  
Decision Making ◽  
Structural Health Monitoring ◽  
Hysteresis Loop ◽  
Health Monitoring ◽  
Average Error ◽  
Support Vector ◽  
Model Parameters ◽  
Loop Analysis ◽  
Structural Health ◽  
Vector Machines

Optimizing risk treatment of structures in post-event decision-making is extremely difficult due to the lack of information on building damage/status after an event, particularly for nonlinear structures. This work develops an automated, no human intervention, modeling approach using structural health monitoring results to create accurate digital building clones of nonlinear structures for collapse prediction assessment and optimized decision-making. Model-free hysteresis loop analysis structural health monitoring method provides accurate structural health monitoring results from which model parameters of a nonlinear computational foundation model are identified. A new identifiable nonlinear smooth hysteretic model capturing essential structural dynamics and deterioration is developed to ensure robust parameter identification using support vector machines. Method performance is validated against both numerical and experimental data of a scaled 12-story reinforced concrete nonlinear structure. Results of numerical validation show an average error of 1.5% across 18 structural parameters from hysteresis loop analysis and an average error of 2.0% over 30 identified model parameters from support vector machines in the presence of 10% added root-mean-square noise. Validation using experimental data of the scale test reinforced concrete structure also shows a good match of identified hysteresis loop analysis and predicted nonlinear stiffness changes using the digital clones created with an average difference of 1.4%. More importantly, the predicted response using the digital clones for the highly nonlinear pinched hysteretic behavior matches the measured response well, with the average correlation coefficient Rcoeff = 0.92 and average root-mean-square error of 4.6% across all cases. The overall approach takes structural health monitoring from a tool providing retrospective damage data into automated prospective prediction analysis by “cloning” the structure using computational modeling, which in turn allows optimized decision-making using existing risk analyses and tools.

Reliability of reinforced concrete beams in serviceability limit state via microprestress-solidification theory, a structural health monitoring strategy

2022 ◽  
pp. 146442072110690
Author(s):  
Mohsen Ghabdian ◽  
Seyed BB Aval ◽  
Mohammad Noori ◽  
Wael A Altabey
Keyword(s):  
Reinforced Concrete ◽  
High Temperature ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Concrete Beams ◽  
Limit State ◽  
Reinforced Concrete Beams ◽  
Nonlinear Creep ◽  
Structural Health ◽  
Serviceability Limit State

An important and critical area within the broad domain of structural health monitoring, as related to reinforced civil and mechanical structures, is the assessment of creep, shrinkage, and high-temperature effects on reliability and serviceability. Unfortunately, the monitoring and impact of these inherent mechanical characteristics and behaviors, and subsequent impact on serviceability, have rarely been considered in the literature in structural health monitoring. In this paper, the microprestress-solidification creep theory for beams is generalized for the simultaneous effect of linear/nonlinear creep, shrinkage, and high temperature in a reliability framework. This study conducts a systematic time-dependent procedure for the reliability analysis of structures using a powerful nanoscale method. It must be noted that this paper aims to extend the previously developed microprestress-solidification method in a health monitoring reliability-based framework with a close look at a nonlinear creep, parameters affecting creep, and long-time high temperature. A finite element approach is proposed where creep, shrinkage, temperature, and cracking are considered using strain splitting theory. First, the model performance was evaluated by comparing the results with the experimental test available in the literature in the case of creep and shrinkage. Then, the simultaneous effect of creep, shrinkage, and temperature was compared with experimental results obtained by the authors. Reliability analysis was applied to reinforced concrete beams subjected to sustained gravity loading and uniform temperature history in order to calculate exceedance probability in the serviceability limit state. It was found that the exceedance probability of reinforced concrete beams was dependent on the shear span-to-depth ratio. In the serviceability limit state, exceedance probabilities of 0.012 and 0.157 were calculated for the span-to-depth ratios of 1 and 5, respectively. In addition, it was shown that temperature plays an important role in the reliability of reinforced concrete beams. A 4.27-fold increase was observed in the case of moderate to high temperature. Finally, for three different load levels of 40%, 70%, and 80%, the exceedance probabilities were 0.156, 0.328, and 0.527, respectively, suggesting that load level is another key parameter affecting the reliability of reinforced concrete beams. It is thus concluded these fundamental phenomenological studies should be further considered as part of the broad field of structural health monitoring.

Smart self-sensory carbon-based textile reinforced concrete structures for structural health monitoring

2020 ◽  
pp. 147592172095112
Author(s):  
Lidor Yosef ◽  
Yiska Goldfeld
Keyword(s):  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Electrical Resistance ◽  
Textile Reinforced Concrete ◽  
Resistance Change ◽  
Gage Factor ◽  
Structural Health ◽  
Monitoring Procedure ◽  
Loading Procedure

The goal of this study is to develop a structural health monitoring methodology for smart self-sensory carbon-based textile reinforced concrete elements. The self-sensory concept is based on measuring the electrical resistance change in the carbon roving reinforcement and by means of an engineering gage factor, correlating the relative electrical resistance change to an integral value of strain along the location of the roving. The concept of the nonlinear engineering gage factor that captures the unique micro-structural mechanism of the roving within the concrete matrix is demonstrated and validated. The estimated value of strain is compared to a theoretical value calculated by assuming a healthy state. The amount of discrepancy between the two strain values makes it possible to indicate and distinguish between the structural states. The study experimentally demonstrates the engineering gage factor concept and the structural health monitoring procedure by mechanically loading two textile reinforced concrete beams, one by a monotonic loading procedure and the other by a cyclic loading procedure. It is presented that the proposed structural health monitoring procedure succeeded in estimating the strain and in clearly distinguishing between the structural states.

scholarly journals Condition assessment of reinforced concrete beams – Comparing digital image analysis with optic fibre Bragg gratings

2018 ◽  
Vol 199 ◽  
pp. 06011
Author(s):  
Elsabe Kearsley ◽  
SW Jacobsz
Keyword(s):  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Construction Material ◽  
Bragg Gratings ◽  
Condition Assessment ◽  
Reinforced Concrete Beams ◽  
Large Strains ◽  
Structural Health ◽  
Concrete Elements

Reinforced concrete is the most widely used construction material and thus effective condition assessment of reinforced concrete elements forms a significant part of structural health monitoring. An effective structural health monitoring system should be able to give the owner prior warning that structural elements are reaching conditions approaching either serviceability or ultimate limit states. The aim of this investigation is to compare strain data recorded during load testing of a reinforced concrete beam using Fibre optic Bragg Gratings (FBG) and a photographic technique to determine circumstances most suitable for the use of each of the techniques. The test results indicate that FBG sensors can be used to detect small strains as well as large strains in uncracked concrete elements, while optical images can be used to accurately map crack development over the surface area of the structure.

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