Due to the fluctuation of the bearing stratum and the distinct properties of the soil layer, the buried depth of the pile foundation will differ from each other as well. In practical construction, since the designed pile length is not definitely consistent with the actual pile length, masses of piles will be required to be cut off or supplemented, resulting in huge cost waste and potential safety hazards. Accordingly, the prediction of pile foundation buried depth is of great significance in construction engineering. In this paper, a nonlinear model based on coordinates and buried depth of piles was established by the BP neural network to predict the samples to be evaluated, the consequence of which indicated that the BP neural network was easily trapped in local extreme value, and the error reached 31%. Afterwards, the QPSO algorithm was proposed to optimize the weights and thresholds of the BP network, which showed that the minimum error of QPSO-BP was merely 9.4% in predicting the depth of bearing stratum and 2.9% in predicting the buried depth of pile foundation. Besides, this paper compared QPSO-BP with three other robust models referred to as FWA-BP, PSO-BP, and BP by three statistical tests (RMSE, MAE, and MAPE). The accuracy of the QPSO-BP algorithm was the highest, which demonstrated the superiority of QPSO-BP in practical engineering.

中文翻译：

---

基于量子粒子群优化BP神经网络的桩基埋深预测

由于承载地层的波动和土层的不同性质，桩基的埋深也会有所不同。在实际施工中，由于设计桩长与实际桩长不一致，需要截断或补充大量桩，造成巨大的成本浪费和安全隐患。因此，预测桩基埋深在建筑工程中具有重要意义。本文利用BP神经网络建立了基于桩身坐标和埋深的非线性模型对待评价样本进行预测，结果表明BP神经网络容易陷入局部极值，错误率达到 31%。然后，提出QPSO算法对BP网络的权值和阈值进行优化，结果表明QPSO-BP在预测承载层深度时的最小误差仅为9.4%，在预测桩基埋深时的误差仅为2.9%。此外，本文通过三个统计检验（RMSE、MAE 和 MAPE）将 QPSO-BP 与其他三个稳健模型 FWA-BP、PSO-BP 和 BP 进行了比较。QPSO-BP算法的精度最高，证明了QPSO-BP在实际工程中的优越性。PSO-BP 和 BP 通过三个统计检验（RMSE、MAE 和 MAPE）。QPSO-BP算法的精度最高，证明了QPSO-BP在实际工程中的优越性。PSO-BP 和 BP 通过三个统计检验（RMSE、MAE 和 MAPE）。QPSO-BP算法的精度最高，证明了QPSO-BP在实际工程中的优越性。