In the linear control of cantilever construction for long-span continuous rigid-frame bridge, existing prediction methods exhibit systemic deficiencies in both model construction and learning mechanisms. The traditional methods suffer from weak nonlinear fitting capabilities, while machine learning models are prone to local optima or insufficient generalization performance. In addition, most methods adopt a static modeling paradigm characterized by offline training and fixed parameters, which makes it difficult to dynamically adapt to the time-varying characteristics of structural responses and the accumulation of errors during construction. To overcome this problem, this study proposes a COA-BP model combining crayfish optimization algorithm (COA) and BP neural network, and innovatively introduces an incremental learning mechanism. Firstly, based on FEA NX, a refined solid finite element model was established. Considering the variability of key parameters such as concrete unit weight, elastic modulus and prestressed tension control stress, the Latin hypercube sampling was used to generate the input parameter combination, and the theoretical formwork elevation of each beam section was inversely calculated. The measured elevation was obtained after the completion of the site construction, and the difference between the two was used as the output target of the model. Then, the COA algorithm was used to optimize the initial weights and thresholds of the BP network, effectively improving the model’s convergence speed and global search capability. On this basis, a phased learning strategy was designed: segments No. 3, 4, and 5 were used for the static learning phase, where the model is initialized using the differences between measured and theoretical elevations. From block 6 onwards, the incremental learning phase begins, during which the model guides the adjustment of formwork placement elevations based on prediction results. After each construction phase, the elevation differences corresponding to newly measured data were incorporated into the training set, enabling a dynamic closed loop of “construction, learning, and optimization in parallel.” The proposed method is validated using an actual continuous rigid-frame bridge project. The results show that after the correction at segment No.6, the maximum error is reduced to −1.8 mm, and the prediction errors for subsequent beam segments converge continuously, with a smoothly declining prediction curve. This performance is significantly superior to that of traditional methods, demonstrating the effectiveness of the proposed COA-BP model in improving the accuracy and adaptability of linear prediction.

In-service reinforced concrete bridges, following prolonged operational periods, frequently undergo alterations in structural integrity and significant deterioration of material properties. As a result, it becomes imperative to evaluate and analyze these bridge structures. This study focuses on a specific in-service concrete bridge and employs finite element simulation analysis to investigate the failure behavior and degradation of load-bearing capacity in concrete beam bridge structures. The research examines the impact of various crack distributions and types, specifically addressing the effects of bending and shear cracks on the failure behavior and degradation of load-bearing capacity in concrete bridge structures. Load simulations are conducted on the bridge, revealing that bending cracks exert a relatively minor influence on the failure mode of the structures. The load-deflection curves demonstrate minimal variation across different crack heights, indicating that the structures do not experience abrupt brittle failure, and their structural performance is largely optimized in this context. Conversely, shear cracks have a pronounced effect on the failure mode of the bridge structures. Notably, when the crack height reaches 0.6h, the load-deflection curve exhibits a significant alteration, leading to brittle failure attributed to shear cracks, thereby indicating that the structural performance is not fully realized. Given that the deformations in cracked bridge structures comprise two components, stiffness reduction formulas are introduced. Utilizing the stiffness reduction formula outlined in the standard (JCT3362—2018) for cracked components, a formula for calculating residual load-bearing capacity is derived. Calculations pertaining to the mid-span section load-bearing capacity of the selected bridge reveal deviations within 5%. This study offers a valuable methodology and reference for assessing the residual load-bearing capacity of reinforced concrete bridges exhibiting crack damage.