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Weighted-Combined Model for Reclaimed Foundation Settlement Prediction in Coastal Sludge-Bearing Composite Stratum

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Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2023
Page(s): 1-12
DOI: 10.1155/2023/9561818
Abstract:

Foundation settlement prediction is significant to the reuse and management of the reclaimed land from the sea by dykes where composite stratum of many strata with different physical and mechanical properties is encountered. Toward the foundation settlement analysis of reclaimed land in the Yellow Sea composite stratum in eastern China, a weighted-combination model is proposed in this paper combining the hyperbolic model, exponential curve model, and Asaoka model. First, the weight coefficient of the weighted-combination model is calculated by the reciprocal square method of average absolute error (MAE). Second, the settlement prediction results of different models are evaluated by the absolute error, MAE, root-mean-square error, and mean absolute percentage error. Finally, the settlement mechanism of reclaimed foundations in a composite stratum is analyzed from the point of view of the multistratum coupling, and the adaptability of different models to the settlement prediction of reclaimed foundations in a composite stratum is discussed. The results show that the predicted settlement duration curves of the weighted-combined model are in good agreement with the measured settlement duration curves, and the prediction performance is better than that of the hyperbolic model, exponential curve model, and Asaoka model. The MAE of the weighted-combination model is 75.7% lower than that of the exponential curve model, 90.2% lower than that of the hyperbolic model, and 70% lower than that of the Asaoka model. This model provides a new way to predict the settlement of reclaimed foundations in a similar composite stratum.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1155/2023/9561818.
  • About this
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  • Reference-ID
    10752150
  • Published on:
    14/01/2024
  • Last updated on:
    14/01/2024
 
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