Deformation prediction during the construction of segmental bridges based on GA-SMO-SVM algorithm: an example of CFST arch bridge

Abstract

Limited research has been undertaken on the extant optimization models pertaining to the prediction of arch deformation in the course of erecting long-span concrete-filled steel tube (CFST) arch bridges. Moreover, CFST arch bridges stand as prototypical instances within the realm of bridge engineering’s segmental structures. This study focuses on the CFST arch bridge as a case study for deformation prediction. In pursuit of precise CFST arch bridge deformation prediction during construction, our investigation has formulated an arch-truss deformation prediction model and computational approach. Moreover, high-precision measuring robots (total stations) are usually utilized to monitor the deformation of arch bridges to obtain accurate deformation data for subsequent prediction studies. This model relies on the employment of the genetic algorithm (GA), sequential minimal optimization (SMO) algorithm, and an optimized support vector machine (SVM). Five input parameters, as utilized in the cable-stayed fastening-hanging cantilever assembly method, have been employed in constructing the model. The optimal parameter configuration for the SMO-SVM model was ascertained by employing an adaptive genetic algorithm to ensure an efficient and precise optimization process. Subsequently, the SMO-SVM model underwent training with the identified optimal parameter set. The prediction outcomes were subsequently verified through testing, facilitating the prediction of arch truss deformations during the construction of CFST arch bridges. To demonstrate the applicability of the proposed model, it was applied to the Pingnan Third Bridge, which is recognized as the world's longest-span CFST arch bridge (at the time of completion) and has a main span of 575 m. We also conducted a comparative study of the GA-SMO-SVM model in three distinct dimensions: various kernel functions, differing optimization algorithms, and alternative regression models. Our findings indicate that the GA-SMO-SVM model, which harnesses an adaptive genetic algorithm for efficient model optimization, achieves the most precise deformation predictions with a maximum absolute error of 8.86 mm, outperforming other models and achieving millimeter-level accuracy. Furthermore, the GA-SMO-SVM model operates with high efficiency, requiring approximately 1/64th of the time consumed by the SMO-SVM model optimized via a grid search (GS). This study validates the mechanism of the CFST arch bridge construction deformation prediction model founded on the GA-SMO-SVM algorithm through thorough model interpretation and analysis, presenting a pioneering approach to predicting arch deformations in the construction of extensive-span CFST arch bridges. Additionally, it offers a foundation for predicting deformations in other segmental structures.