What does this research mean for the field?

Integrating physics-based degradation modelling with continuous monitoring data through a feedback-driven framework enables more reliable tracking of Alkali-Aggregate Reaction (AAR) progression and structural health in ageing concrete gravity dams. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To establish a closed-loop, feedback-driven assessment framework that bridges the gap between predictive multi-physics numerical modeling and field monitoring for ageing concrete gravity dams undergoing alkali-aggregate reaction degradation.

September 14, 2026Frontiers in Built EnvironmentOpen Access

Predictive assessment of ageing concrete gravity dams using feedback-driven modelling

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Authors

MTMohammed ThowsifNational Institute of Technology Calicut PNPraveen NagarajanNational Institute of Technology STSantosh G. ThampiNational Institute of Technology Calicut

Discussion

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Implication

Methodological framework proposes feedback-driven coupling of monitoring and predictive simulation in ageing concrete gravity dams, indicating enhanced long-term degradation tracking.

Key Points

To establish a closed-loop, feedback-driven assessment framework that bridges the gap between predictive multi-physics numerical modeling and field monitoring for ageing concrete gravity dams undergoing alkali-aggregate reaction degradation.
Coupled a multi-physics simulation domain (encompassing chemo-mechanical damage, thermal variations, moisture transport, and reservoir-foundation interactions) with field instrumentation monitoring.
Formulated an inverse-problem residual vector by calculating the mismatch between simulated outputs and sensor measurements, such as crest displacement, joint opening, and modal strains.
Integrated parameter-updating modules (including deterministic optimization, Bayesian calibration, and Kalman filtering) to iteratively adjust material and boundary parameters until residual acceptance is met.
Identified that conventional disconnected approaches fail to track non-uniform, progressive degradation because numerical forecasts lack continuous field validation while discrete sensor networks lack forward predictive capacity.
Established a systematic workflow where residual minimization reliably updates evolving structural conditions, stress redistributions, and regional stiffness reductions across decadal timescales.
Characterized core framework uncertainties, including non-uniqueness in multi-parameter inverse solutions, sensor drift, and substantial computational burdens in executing recurring multi-physics recalibrations.

Cite This Study

Thowsif et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3440926e14a848b201e https://doi.org/10.3389/fbuil.2026.1901819

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Implication

Methodological framework proposes feedback-driven coupling of monitoring and predictive simulation in ageing concrete gravity dams, indicating enhanced long-term degradation tracking.

Predictive assessment of ageing concrete gravity dams using feedback-driven modelling

Discussion

Member takes

Implication

Key Points

Cite This Study

Predictive assessment of ageing concrete gravity dams using feedback-driven modelling

Discussion

Member takes

Implication

Key Points

Cite This Study

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