PSO-BP优化MGM(1,n)模型的风积沙路基沉降预测研究OA

Accurate settlement prediction is essential for ensuring subgrade construction quality and the long-term safe operation of highways.Aeolian sand has low shear strength and can undergo substantial settlement deformation when used as subgrade fill,thereby compromising pavement structural safety.Taking an aeolian-sand section of National Highway G219 as the study site,this study proposes a high-accuracy PSO-BP-MGM(1,n)model for subgrade settlement prediction.First,settlement data collected using embedded monitoring instruments are used as modeling samples.Next,a hybrid model is constructed by integrating the Multivariable Grey Model(MGM(1,n))with Particle Swarm Optimi-zation(PSO)and a Back Propagation(BP)neural network.The MGM(1,n)model captures settlement trends,while PSO-BP performs nonlinear correction of the MGM(1,n)prediction residuals,thereby compensating for the limited nonlinear fitting capability of MGM(1,n)and reducing the tendency of BP neural networks to overfit small samples.Finally,the proposed model is applied to settlement predic-tion for the G219 aeolian-sand subgrade.The results show that the settlement predictions obtained us-ing the PSO-BP-MGM(1,n)model agree most closely with the measured values.Compared with the MGM(1,n)model,the maximum reductions in Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE),and Sum of Squared Errors(SSE)are 1.341 percentage points,0.528,and 1.817,respectively;compared with the hyperbolic model,the corresponding maximum reduc-tions are 2.21 percentage points,0.965,and 5.523.All absolute grey relational grades for the PSO-BP-MGM(1,n)model exceed 0.995,outperforming those of the MGM(1,n)and hyperbolic models.The proposed model accounts for interactions among monitoring points and is therefore suitable for joint multi-point prediction.By further modeling and correcting the MGM(1,n)predic-tion residuals,PSO-BP effectively identifies systematic components hidden in the residual se-quences and improves the overall prediction accuracy.The proposed approach can support sub-grade settlement control in aeolian-sand regions and settlement prediction under similar geological conditions.