TY  - JOUR
TI  - Calibration of Foundation Settlement Surrogate Models: A Digital-Twin Protocol for Comparative Learning, Feature Optimization, and Directed Dependence Analysis
AU  - Smart World Journal Editorial Desk (Rohan Kapoor, editorial pseudonym)
JO  - Smart World Journal
SN  - 3070-6270
VL  - 1
IS  - 2
PY  - 2026
DA  - 2026-11-03
UR  - https://smartworldjournal.com/issues/vol1/issue2/articles/article23.html
AB  - Foundation settlement prediction often requires repeated numerical simulation, sparse field observations, and frequent recalibration as soil and loading conditions evolve. This protocol proposes a transferable calibration framework for surrogate models of foundation settlement that combines comparative surrogate assessment, metaheuristic feature and hyperparameter optimization, and digital-twin data fusion for iterative updating. The research question is whether a calibrated surrogate can approximate settlement responses with sufficient fidelity for design-space exploration while retaining explicit uncertainty and failure checks under limited, heterogeneous, and time-dependent measurements. The evidence scope is a targeted narrative synthesis of methodological findings from surrogate-based building optimization, ML task–algorithm mapping, digital-twin architectures for real-time simulation, geospatial calibration for flood dynamics, directed spatiotemporal dependence modeling, and ML calibration studies for sensor systems. These sources motivate the selection of BPNN and SVR as comparator surrogates, the use of wrapper-based feature selection and metaheuristic tuning, and the structuring of a foundation settlement digital twin around synchronized geometry, soil parameters, load history, and observation streams. The proposed protocol defines variables, operational rules, a calibration objective, model-selection criteria, and a validation sequence using blocked temporal and spatial splits. A direct operational rule is introduced for calibration: a surrogate is accepted only if it reduces both holdout error and systematic residual structure relative to a physics baseline, with residual autocorrelation explicitly tested rather than assumed absent. The manuscript establishes a reproducible methodological foundation and delineates what remains untested: empirical performance on a specific foundation class, site, or sensor regime.
N1  - Editorial pseudonym; responsible editor and original credit: Roman Vlasov. Not a separately verified researcher.
ER  -