Subgrade settlement is a complex process system. Commonly used single-point prediction models can’t consider the correlation between the discrete deformation monitoring points, which doesn’t adequately reflect the overall deformation law of subgrade. A multivariable grey model (MGM(1,n)), which is an expansion of the single-point GM(1,1) model for multiple variables, is introduced to resolve the above problem. Aiming at the drawback of background value in the traditional MGM(1,n) model, the functions with non-homogeneous exponential law are used to fit the accumulated sequences for every variable, reconstruct the calculating formula of background value, and gets a new MGM(1,n) model based on optimized background value (OMGM(1,n)). A case study shows that the forecast result of the proposed model is more precise and effective than these of the single-point GM(1,1) model and the traditional MGM(1,n) model for predicting subgrade settlement.
A mixture method based on exponential curve and ANN is presented according to settlement prediction of roadbed with measured data. Based on this method, the rule of roadbed settlement is classified into sure part and uncertain part. Exponential curve is used to model the sure part, and ANN to model the uncertain part, thus the mixture settlement model can be obtained. Prediction results show that the mixture model has advantages of high precision and small network scale; it provides a new method for settlement prediction of roadbed.
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