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<p>Accurate prediction of long-term settlement under complex traffic loads remains a pivotal challenge for the safety and durability of transportation infrastructure. While explicit models for settlement calculation have been advanced to handle general three-dimensional stress states, a major practical hurdle lies in determining reliable model parameters. Parameter inversion offers a viable path to high-fidelity estimates, yet conventional inversion techniques often fall short in accuracy. Ensemble learning methods can improve data precision by synthesizing predictions from multiple intelligent models; however, commonly used soft voting strategies tend to overlook both systemic bias across base models and the distinct contribution of each predictor. To address this, this study proposes a Particle Swarm Optimization-Back Propagation Neural Network-Random Forest (PSO-BPNN-RF) inversion model that incorporates a refined soft voting method. Coupling this inversion model with a three-dimensional explicit settlement calculation framework for complex traffic loading enables high-precision parameter identification. The proposed approach is subsequently applied to parameter inversion for an explicit model of the Xiaoshan Airport taxiway, demonstrating strong generalization capability and superior accuracy.</p>

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Evaluating long-term settlements under complex traffic loads via explicit cyclic model augmented by intelligent optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Shaofei Guo,
  • Jiafeng Zhang,
  • Zhenhao Shi,
  • Yang Li,
  • Jianjun Li,
  • Jiangu Qian

摘要

Accurate prediction of long-term settlement under complex traffic loads remains a pivotal challenge for the safety and durability of transportation infrastructure. While explicit models for settlement calculation have been advanced to handle general three-dimensional stress states, a major practical hurdle lies in determining reliable model parameters. Parameter inversion offers a viable path to high-fidelity estimates, yet conventional inversion techniques often fall short in accuracy. Ensemble learning methods can improve data precision by synthesizing predictions from multiple intelligent models; however, commonly used soft voting strategies tend to overlook both systemic bias across base models and the distinct contribution of each predictor. To address this, this study proposes a Particle Swarm Optimization-Back Propagation Neural Network-Random Forest (PSO-BPNN-RF) inversion model that incorporates a refined soft voting method. Coupling this inversion model with a three-dimensional explicit settlement calculation framework for complex traffic loading enables high-precision parameter identification. The proposed approach is subsequently applied to parameter inversion for an explicit model of the Xiaoshan Airport taxiway, demonstrating strong generalization capability and superior accuracy.