Prestressed concrete structures often require active strengthening measures to improve structural behavior and increase stiffness when there is insufficient prestress. The strengthening design needs to efficiently handle the complex connection relationships between new strengthening members and the original structure, aiming to minimize component clashes during the design stage. A scientific evaluation of the corresponding construction duration is also crucial for assessing the scheme's feasibility. Targeting a newly constructed continuous box-girder structure with irreversible prestress defects arising during construction, this paper designs a combined strengthening scheme using internal support trusses and external tendons, along with process simulation and intelligent static/dynamic duration prediction, using building information modeling (BIM) and gradient boosting decision tree (GBDT) methods to increase its stiffness and resistance reserve.

First, specialized bridge structural finite element analysis software was used to analyze the improvement efficiency of truss strengthening on the girder stiffness and internal forces, thereby verifying the scheme effectiveness. Second, a BIM parametric model of the strengthening project was established to complete tasks such as clash detection and construction process simulation. Furthermore, upon integrating BIM feature indicators, the GBDT algorithm was used to train static duration prediction models for the four subprojects involved in the strengthening project: support erection, installation of internal steel support trusses, tensioning and anchoring of external prestressing strands, and bridge deck pavement construction. Thereafter, the accuracy of the prediction results was analyzed. Finally, for the internal steel support truss installation subproject, a GBDT dynamic duration prediction model that accounts for random clash rates across construction segments was built. This model's performance was analyzed through numerical experiments, and its prediction accuracy was compared with that of a linear prediction model.

The results revealed the following: 1) The internal support trusses were effective in improving the box-girder stiffness and bending moment distribution. For the controlling mid-support negative moment region, the bending moments caused by the dead load and the live load (lane load) were reduced by an average of 12% and 14%, respectively, but there was no effect on the shear force near the supports. 2) BIM technology identified and counted more than 1 300 clashes between the steel and concrete components, guiding the optimization of the strengthening details and eliminating clashes at the design stage. 3) The GBDT algorithm demonstrated high fitting and generalization capabilities on the relevant training sets for each strengthening subproject, with the coefficients of determination ranging between 0.89 and 0.93. 4) The GBDT dynamic duration prediction model showed higher accuracy and reliability than the linear prediction. Furthermore, the minimum coefficient of determination increased from 0.66 to 0.95. In particular, under unknown clash rate conditions, the former also had smaller prediction errors for the remaining duration, with the maximum error decreasing from 5.1% to 3.6%.

The BIM–GBDT method efficiently completes the forward design of this strengthening project and accurately predicts the project duration, thereby demonstrating the feasibility of the active strengthening scheme for a prestressed concrete structure. The methodological workflow described in this paper can serve as a reference for similar projects.