Abstract
Using construction and demolition waste (CDW) as road subgrade filling materials is an excellent way to solve the disparity between increased demand and road construction aggregate shortages. However, a key quality control problem is predicting the subgrade settlement, primarily because the CDW subgrade settlement prediction methods are not yet mature. To go some way in overcoming this problem, in this paper we developed a three-point hyperbolic combination model to predict CDW subgrade settlement, in which three appropriate points for the measured settlement curve were selected in the prediction samples to improve the hyperbolic model. Then, common prediction models—namely, the hyperbolic model, the three-point model, and the Hushino model—were compared with the proposed combination model to assess its viability. Finally, the three-point hyperbolic combination prediction accuracy was analyzed for different start points t0 and time intervals Δt. The analyses found that the proposed model was in good agreement with the measured data, had a high correlation coefficient, and had only small errors. However, the time interval needed to be greater than 80 days and the start point t0 needed to be selected at the beginning of the stable post-filling period, that is, t0 = 90–100 days. The application parameters were also determined to provide a reference for the large-scale application and settlement predictions of CDW subgrade.
Highlights
As a result of continued industrial and urban growth, there has been a commensurate increase in construction and demolition waste (CDW), which accounts for 30–40% of city waste in China and more than 40% of all municipal waste in Europe [1,2,3]
Based on the foundation characteristics of CDW subgrade, for the first time, this paper combined the characteristics of the three-point method and the hyperbolic model to propose a three-point hyperbolic combination model for CDW subgrade settlement predictions, for which relevant equations were designed and the prediction effects analyzed using case studies
To obtain optimum CDW subgrade prediction values and to avoid the errors caused by poor point selection, in the following, the three-point hyperbolic combination model prediction accuracy was examined using different time start points t0 and time intervals ∆t
Summary
As a result of continued industrial and urban growth, there has been a commensurate increase in construction and demolition waste (CDW), which accounts for 30–40% of city waste in China and more than 40% of all municipal waste in Europe [1,2,3]. Curve fitting methods—i.e., the Poisson model [24,25], the hyperbolic model [26,27], the three-point model [28,29], the Asaoka model [30,31], the Hushino model [32,33], the Gompertz model [34], the grey prediction model [35,36,37], and the neural network model [38,39]—are simple and easy to calculate, and have more satisfactory predictions as they fully consider the measured settlement data As these models have some application limitations, accurate settlement prediction results for varied scenarios often require model combinations. Based on the foundation characteristics of CDW subgrade, for the first time, this paper combined the characteristics of the three-point method and the hyperbolic model to propose a three-point hyperbolic combination model for CDW subgrade settlement predictions, for which relevant equations were designed and the prediction effects analyzed using case studies
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