Machine-Learning-Assisted DFOS Framework for Piled-Raft Foundation Monitoring

Reliable interpretation of distributed fiber optic sensing (DFOS) data from large building foundations requires accounting for thermal effects, temporal response, and structural context while preserving distributed spatial resolution. This study develops a machine-learning-assisted framework for pointwise screening and engineering interpretation of DFOS measurements from a reinforced-concrete piled-raft foundation. The workflow integrates temperature compensation, reference-profile comparison, deterministic engineering screening, structural-zone mapping, and Isolation Forest analysis using analysis strain, reference-relative strain change, and temperature change as ML inputs. The framework was demonstrated using 11 sequential profiles comprising 77,990 pointwise observations from the Block R Y-direction sensing segment. Two observations, at chainages 148.82 m and 367.94 m, shared the maximum Isolation Forest anomaly score. Independent post-processing showed that only the 148.82 m observation fell within the configured chainage-based screening neighborhood of R-Y-Z01. This result illustrates the value of combining response-based anomaly screening with independently retained structural context for engineering prioritization. The framework is intended to support engineering review rather than validated damage diagnosis; its current limitations include the short monitoring interval, route-based structural mapping, an adopted temperature-compensation coefficient, and the absence of independently labeled damage states.