Bridge Weigh-in-Motion (B-WIM) systems provide vital traffic data for bridge design, management, and maintenance, yet conventional approaches often rely on bridge influence lines that are notoriously challenging to be identified accurately. While some model-independent approaches have been proposed, they typically estimate only partial parameters like axle loads, lacking the capability to simultaneously determine axle spacings. To address these limitations, this study proposes a model-free B-WIM methodology for simultaneous identification of vehicle axle loads and spacings using an influence line-free transmissibility-like index. This index, defined as the ratio of frequency-domain responses at the same location for two distinct vehicles, is analytically proven to equal the ratio of their moving load functions in the frequency domain, thereby eliminating the need for influence line estimation. Given the response of a reference vehicle with known axle configuration, this property enables the simultaneous identification of both axle loads and spacings. A Bayesian inference scheme is further developed to integrate multiple measurements and accommodate uncertainties stemming from measurement noise and modeling errors. Moreover, analytical likelihood function, gradients, and posterior covariances are derived to support efficient optimization scheme. Ultimately, numerical simulations and experimental studies validate the method’s accuracy and robustness under varying scenarios, without requiring influence line estimation.
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