Inverse Method for Assembly Tolerance Itervals of Cable-Stayed Bridge Based on Proactive Fault Tolerance

To enhance construction efficiency, prefabricated and assembled industrialized construction methods are widely adopted in bridge engineering. However, on-site procedural adjustments can easily lead to assembly failures, resulting in schedule and cost overruns, which contradict the original intention of rapid construction. For cable-stayed bridge prefabricated components produced according to the original single-segment cyclic process, how to achieve assembly coordination under the adjusted dual-segment cyclic process is a key challenge when complying with overall schedule decisions. Integrating the unstressed state method and tolerance allocation approach, this paper first develops a machine learning-based inversion method for the assembly tolerance intervals of cable-stayed bridges. Then a non-negative dimensionless indicator is introduced as an independent optimization objective to characterize construction operability. Next, a GA-BPNN surrogate model combined with the NSGA-Ⅱ multi-objective optimization algorithm is adopted to conduct a trade-off optimization between structural safety and design optimality. By incorporating prior error reserves, the passive assembly tolerance ranges for various components of a cable-stayed bridge are inversely derived. Finally, integrating field-measured data from preceding construction segments, an assembly tolerance design framework for cable-stayed bridges oriented toward active fault tolerance is established. Results demonstrate that the proposed interval inversion method effectively enhances on-site operability while ensuring structural safety and design optimality. Compared with passive tolerance design, the active tolerance design approach increases the tolerance interval for the girder splicing angle of segment G2 by 1.4 times and that for the unstressed cable length of stay S16 by 2.1 times. Moreover, the active tolerance analysis framework enables adaptive adjustment in assembly failure scenarios by modifying tolerance ranges of subsequent components, thereby reducing the occurrence of work stoppages and reworks.