Sensing-Based Simulation of Bending Moments in Reinforced Concrete Columns.

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Bibliographic Details
Title:	Sensing-Based Simulation of Bending Moments in Reinforced Concrete Columns.
Authors:	Iranmanesh, Amir¹, Panahi, Mahsa², Ansari, Farhad³
Source:	ACI Structural Journal. May2026, Vol. 123 Issue 3, p81-94. 14p.
Subjects:	Bending moment, Concrete columns, Deformations (Mechanics), Fiber Bragg gratings, Structural health monitoring, Ground motion, Damage models
Abstract:	Integrating real-time sensor data with physics-based models enhances the accuracy and efficiency of structural simulation and prognosis. In this study, a sensing-based simulation method is introduced to compute bending moments in reinforced concrete bridge columns subjected to seismic motions, based on the measured strains continuously fed into plasticity models. The experimental program included hybrid testing of scaled reinforced concrete bridges under consecutive seismic events. The experimental columns were instrumented with embedded as well as surface-adhered fiber-optic Bragg grating (FBG) sensors for real-time monitoring of strains, reflecting degradation of the columns during the formation of damage. The fundamental assumption of strain compatibility in reinforced concrete members was investigated for the successive progression of damage in the cross sections of the columns. The stress distributions within the concrete core and cover were computed through the confined and unconfined concrete stress-strain relations for loading, unloading, and reloading scenarios. The bending moments in the cross section were computed and compared with the corresponding experimental values calculated based on direct measurements of forces. The results from this study revealed that the cross-sectional strains exhibit three primary features during the seismic events that need to be considered for the accurate calculation of bending moments. Computation of the bending moments requires consideration of the shifts in cyclic reference, post-event residual strains, and real steel strains. Using these features, the computed bending moments during the column tests mimicked the experimental results based on the measured seismic forces on the columns. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Integrating real-time sensor data with physics-based models enhances the accuracy and efficiency of structural simulation and prognosis. In this study, a sensing-based simulation method is introduced to compute bending moments in reinforced concrete bridge columns subjected to seismic motions, based on the measured strains continuously fed into plasticity models. The experimental program included hybrid testing of scaled reinforced concrete bridges under consecutive seismic events. The experimental columns were instrumented with embedded as well as surface-adhered fiber-optic Bragg grating (FBG) sensors for real-time monitoring of strains, reflecting degradation of the columns during the formation of damage. The fundamental assumption of strain compatibility in reinforced concrete members was investigated for the successive progression of damage in the cross sections of the columns. The stress distributions within the concrete core and cover were computed through the confined and unconfined concrete stress-strain relations for loading, unloading, and reloading scenarios. The bending moments in the cross section were computed and compared with the corresponding experimental values calculated based on direct measurements of forces. The results from this study revealed that the cross-sectional strains exhibit three primary features during the seismic events that need to be considered for the accurate calculation of bending moments. Computation of the bending moments requires consideration of the shifts in cyclic reference, post-event residual strains, and real steel strains. Using these features, the computed bending moments during the column tests mimicked the experimental results based on the measured seismic forces on the columns. [ABSTRACT FROM AUTHOR]
ISSN:	08893241
DOI:	10.14359/51749316