The vehicle load on a bridge is a critical and dynamic variable. It adversely affects bridges, especially when overloading occurs. Bridges are prone to fatigue damage or collapse. Therefore, identifying the size and type of dynamic vehicle loads on bridges is critical for theoretical studies and practical applications, such as health monitoring, daily maintenance, safety assessment, and traffic planning. The paper proposes a method for identifying the dynamic load parameters based on a convolutional neural network (CNN) and dynamic strain data. The model is implemented in MATLAB. An initial finite-element model of a three-span precast beam bridge is established in the software ABAQUS and modified by combining the modal and experimental data derived from a segmental girder bridge. The dynamic strain response of the bridge under a moving vehicle load is simulated under different working conditions. The results are used as the training data of the CNN to identify the vehicle’s position, speed, and load on the bridge. The high prediction accuracy indicates the proposed model’s suitability for identifying the dynamic load parameters.

The prestress loss of prefabricated continuous beam bridges directly affects their stress state and operating conditions. Excessive prestress loss can lead to increased mid span deflection of the main beam, cracking of the web and bottom plates, reduced bearing capacity, and even affect the safety of the structure. This study focuses on urban highway overpasses and measures the deflection of the bridge deck using the drop hammer impact method. The impact test of prefabricated segmental beam bridges was simulated using a finite element model, and the influence of prestress loss at different positions on the deflection basin of the bridge deck during operation was studied. The research results indicate that the factor causing the greatest damage to bridge stiffness is the loss of prestress in the bottom plate. When the prestress loss reaches 30%, the maximum response value increases by 33.6%. In contrast, the prestress loss of the roof has a smaller impact on deflection, with a maximum response value increase of 18.7%. This study provides important reference for evaluating prestress loss through impact testing methods in the future.

The grouting quality of prestressed pipelines directly affects the safety and durability of prestressed reinforced concrete bridge structures, attracting wide attention from the engineering community. Based on the principles of the scattering wave method, this paper designs and manufactures a large-scale model to simulate the actual construction testing environment. Several sets of design defects with significantly distributed spans in the prestressing ducts were pre-set, and the Engineering Geophysical Instrument System–Pipeline Grouting Profile (EGS-PGP) equipment developed was used to detect the grouting quality of the prestressed pipelines under different conditions, obtaining the frequency attenuation values at defect locations. The study shows that stability of the peak frequency throughout the test can indicate compact grouting, while a large fluctuation in the peak frequency range with no regular changes can indicate non-compact grouting; a sudden increase or decrease in peak frequency can effectively indicate the location of grouting defects along the pipeline. The number of sudden changes in frequency can indicate the number of gaps inside the pipeline. The chirp signal, compared to the hammer impact signal, shows greater stability and engineering application value in detecting defects using the scatter wave method. This study provides technical support for the detection of grouting quality in prestressed pipelines based on chirp signals.

In order to study the stiffness degradation of pre-stressed concrete T-beams throughout their entire service life, full-scale destructive tests were conducted on newly constructed T-beams. The test process characterized the relationship between load and deflection at different load levels. Meanwhile, the equivalent stiffness and short-term stiffness of the T-beams at different load levels were calculated based on formulas from the code, and a segmental stiffness back-calculation method for pre-stressed concrete T-beam bridges was proposed based on system identification theory. The results show that, during the destructive test process, the T-beams experienced complete, partial linear, and non-linear stages. By using the equivalent stiffness and short-term stiffness to predict the stiffness changes during the overall failure process of the T-beams, it was found that, when cracks appeared, the stiffness of the T-beams decreased by 31% compared to the initial value, and the stiffness continued to decrease as the cracks extended further. The segmental stiffness system identification back-calculation method more accurately described the destructive test process of each section of the T-beams. This method can help to evaluate the extent of damage in each section of the beams during the overall destructive test process and further assess the overall structural integrity of the beams.

Traditional damage detection methods of prestressed concrete box girder bridges have low efficiency and cannot quantify the structure’s internal damage. We used an inversion method and a falling weight deflectometer to estimate the mechanical parameters of prestressed box girder bridges. A finite element model of the bridge dynamics under impact loading was established. A perturbation-based update was conducted, and a multi-parameter inversion algorithm was constructed. The measured data were used for the efficient identification of the bridge’s elasticity modulus and the prestressing tensile force. The theoretical validation indicated a high modeling accuracy and inversion efficiency, with a convergence accuracy within 1%. The initial value had a minimal influence on the inversion results. The engineering application showed that the maximum error of the elastic modulus between the inversion and the rebound methods was 1.55%. The loss rates of the deck slab’s elastic modulus and the prestressing force obtained from the inversion were 4.39% and 7.64%, respectively. The proposed method provides a new strategy for evaluating damage to prestressed box girder bridges.