Concrete exhibits long-term time-dependent behavior due to creep and shrinkage that impacts the safety and serviceability of high-rise buildings. These rheological effects are difficult to predict due to their random nature, dependence on environmental conditions, and loading history. Current methods include the use of creep and shrinkage tests of structure-specific concrete samples to update compliance and shrinkage, and sophisticated numerical models for prediction of the long-term structural behavior. However, creep and shrinkage tests are time-consuming, and simulation requires reliable numerical models and often proprietary solvers that are not available to the structural health monitoring (SHM) practitioner. Further, uncertainty propagation in complex numerical models is rarely seen in the relevant literature. In contrast, data-driven prediction methods using SHM data and simplified analytical models have shown to be successful for prestressed concrete bridges. In this work, we investigate calibration strategies of creep and shrinkage models using SHM data toward data-driven forecasting of long-term time-dependent behavior of high-rise buildings. A calibration strategy is identified that enables significant and consistent improvement of forecasting of long-term time-dependent behavior. It is also shown that continuous calibration can provide good predictions at least 30 days ahead. First-order analytical uncertainty propagation formulas are also provided. The calibration strategies are evaluated on data from two residential high-rise buildings in Singapore. Recommendations to the SHM practitioners are also given.
All Science Journal Classification (ASJC) codes
- Mechanical Engineering
- Structural health monitoring
- creep and shrinkage
- data-driven prediction
- high-rise buildings
- long-term structural behavior