VIWNO: Vehicle–Bridge Interaction Wavelet Neural Operator for Controlled Bridge Simulation and Laboratory Damage Identification

Controlled bridge simulation and laboratory damage identification require models that can simulate structural responses and infer localized stiffness loss from limited measurements. Existing Fourier Neural Operator (FNO)-based vehicle–bridge interaction (VBI) models provide efficient surrogates for these mappings, but the global Fourier representation can smooth localized damage transitions and introduce boundary-related errors for finite-span bridge responses. This study adapts the Wavelet Neural Operator (WNO) to the VBI setting and develops the Vehicle–Bridge Interaction Wavelet Neural Operator (VIWNO), an application-oriented framework for wavelet-domain operator learning between structural response fields and damage fields. VIWNO is pre-trained on a numerical VBI finite-element dataset (VBI-FE) and fine-tuned using only healthy-state measurements from a scaled VBI experimental dataset (VBI-EXP), before being evaluated on unseen laboratory damage scenarios. Under the controlled VBI-FE setting, where bridge, vehicle, speed, and measured road-profile parameters are fixed and the main variation is the damage field, VIWNO reduces forward response errors by 20–30% and inverse damage-estimation errors by 26–32% relative to the FNO-based Vehicle–Bridge Interaction Neural Operator (VINO) baseline. Additional morphology and operating-condition stress tests show that the error increases under sharper damage fields and perturbed VBI conditions, but VIWNO remains more accurate than VINO and the added convolutional or frequency-domain baselines in the tested cases. On VBI-EXP, projection-only healthy-state fine-tuning reduces intact false-damage levels and yields sharper damage estimates than VINO under both displacement and acceleration inputs. Stability checks over five initializations and repeated vehicle passages show limited variation in the reported inverse metrics. These results support the feasibility of wavelet-domain neural operators for calibrated VBI simulation and scaled laboratory damage identification, while field-scale bridge health monitoring still requires validation under broader traffic, environmental, support, and damage-morphology variability.