针对某施工期间产生不可逆预应力缺陷的新建连续箱梁结构，为提高其刚度和抗力储备，该文采用建筑信息模型（building information modeling，BIM）和梯度提升决策树（gradient boosting decision tree，GBDT）算法，对其进行内支撑桁架-体外束组合加固方案设计、工序模拟，以及智能静态和动态工期预测。首先，采用桥梁结构专业有限元分析软件，分析了桁架加固对梁体刚度和受力情况的改善效果，验证了所提方案的有效性；其次，建立了加固项目的BIM参数化模型，并完成了碰撞检查和工序模拟等工作；最后，结合BIM特征指标，采用GBDT算法训练了加固项目的静态和动态工期预测模型，并采用数值模拟试验方法分析了动态预测模型的性能。结果表明：内支撑桁架可有效提高箱梁刚度，改善其弯矩，对于起控制作用的中支点负弯矩区段，恒载和活载（车道荷载）产生的弯矩平均分别减小12%和14%，但对支点附近的剪力几乎无影响；BIM定位并统计出1 300余处钢混构件穿插碰撞，指导了加固构造的优化设计，消除了设计阶段的构件碰撞；GBDT算法在各加固分项工程的相关训练集上，表现出较高的拟合及泛化能力，决定系数${R^2}$为0.89~0.93；相较于线性动态工期预测，GBDT动态工期预测的精度和可靠性更高，最低${R^2}$由0.66增至0.95，特别是在碰撞率未知的情况下，后者对于剩余工期的预测误差更小，最大误差由5.1%降至3.6%。该文研究结果可为同类工程箱梁结构的加固方案设计及工程应用提供参考。

Objective: 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. Methods: 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. Results: 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%. Conclusions: 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.