التفاصيل البيبلوغرافية
| العنوان: |
Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence. |
| المؤلفون: |
Shi-fan, Qiao, Jun-kun, Tan, Yong-gang, Zhang, Li-jun, Wan, Ming-fei, Zhang, Jun, Tang, Qing, He |
| المصدر: |
Advances in Civil Engineering; 1/27/2021, p1-11, 11p |
| مصطلحات موضوعية: |
SETTLEMENT of structures, EXCAVATION, MACHINE learning, TIME series analysis, PROCESS optimization |
| مصطلحات جغرافية: |
KUNMING (China) |
| مستخلص: |
This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability. [ABSTRACT FROM AUTHOR] |
|
Copyright of Advances in Civil Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder