Multiple moving force identification for real multi-lane prestressed concrete bridges

Many medium- and long-span prestressed concrete bridges (PCBs) are approaching the end of their service lives. Rising heavy-freight demand is increasing the risk of overload on these assets, underscoring the need for accurate, computationally efficient moving-force identification to inform maintenance, monitoring, and life-extension decisions. This study proposes an enhanced, robust time-domain methodology for identifying multiple moving forces on a real, multi-lane PCB. The approach is demonstrated on a four-lane, 378 m, three-span continuous PCB with an 18.5-m-wide deck. Validation draws on bridge weigh-in-motion (B-WIM) records complemented by targeted numerical simulations. The core of the method is a strain-dependent shape function matrix that maps measured responses to moving forces with substantially reduced computational cost while retaining high fidelity under complex loading conditions. To address the ill-posed inverse problem, this study develops a column-normalized Tikhonov regularization scheme augmented by a tri-diagonal regularization matrix, yielding stable force recovery and improved subspace identification. Field deployment integrates sensor data—fibre-optic and electrical-resistance strain gauge together with accelerometer—to enhance observability and robustness. The methodology also simplifies the evaluation of dynamic amplification factors (DAFs) by applying a strain-based shape function similarity approach to field data. The resulting DAFs are then compared across different axle configurations, vehicle speeds, gross vehicle weights, and traffic lanes, while accounting for both global and localized dynamic effects. The findings support bridge design and performance assessment by accurately identifying moving forces under multi-lane traffic, unlike traditional B-WIM systems. This enables data-driven maintenance and safety decisions, helping to extend service life and address critical gaps in force identification.