4.7 Article

Performance evaluation of a single footing over rigid inclusions-reinforced soft soil-use of a novel LWSO-RF model to predict load transfer efficiency

期刊

ENGINEERING STRUCTURES
卷 292, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2023.116556

关键词

Load transfer efficiency; White shark optimizer; Logistic chaotic mapping; Single footing; Rigid inclusion

向作者/读者索取更多资源

In this study, an intelligence approach combining the white shark optimizer and the logistic chaotic mapping was proposed to optimize the random forest model for predicting the load transfer efficiency of a single footing over rigid inclusion-reinforced soft soils. Through the investigation of seven variables, it was found that the thickness of the load transfer platform is the most important variable for predicting the load transfer efficiency.
The rigid inclusion (RI) reinforcement technique has been increasingly used to reduce settlements for engineering constructions over soft soils. However, the load transfer mechanisms within the granular mattress are not well understood, especially for the RI-reinforced systems with the presence of structural elements such as footings. To evaluate the performance of a single footing over RI-reinforced soft soils, a novel intelligence approach combining the white shark optimizer (WSO) and the Logistic chaotic mapping (LCM) was proposed to optimize the random forest (RF) model for predicting the load transfer efficiency (LTE). Seven variables were considered to systematically investigate the LTE, including the loading magnitude (L), the load eccentricity (LE), the geometrical and strength parameters of the load transfer platform thickness (LT), Young's modulus (E), cohesion (C) and friction angle (F)) and the compression ratio (CR). Based on the feature selection method, nine different combination models were developed to estimate the LTE. Their performances were compared using the determination coefficient (R2), the root mean squared error (RMSE), the variance accounted for (VAF) and the Willmott's index (WI). The evaluation results indicated that the LWSO-RF model containing seven variables (L, LT, LE, E, CF, FA, CR) is the best one as it gives the highest prediction accuracy in both the training and testing phases. The sensitivity results demonstrated that LT is the most important variable for the LTE prediction. This work utilizes an intelligence approach to extend the traditional numerical simulation analysis for predicting the LTE, which provides additional guidance to further assess the performance of foundations over RI-reinforced soft soil.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据