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Received: 2015-02-10

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Hossein Rezaei


Ehsan Momeni


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Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.4 P.273-285


Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Author(s):  Hossein Rezaei, Ramli Nazir, Ehsan Momeni

Affiliation(s):  Faculty of Engineering, Lorestan University, Khorram Abad 68151-44316, Iran; more

Corresponding email(s):   mehsan23@live.utm.my

Key Words:  Thin-walled foundation, Sand, Bearing capacity, Artificial neural network (ANN), Particle swarm optimization (PSO)

Hossein Rezaei, Ramli Nazir, Ehsan Momeni. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study[J]. Journal of Zhejiang University Science A, 2016, 17(4): 273-285.

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%T Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study
%A Hossein Rezaei
%A Ramli Nazir
%A Ehsan Momeni
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500033

T1 - Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study
A1 - Hossein Rezaei
A1 - Ramli Nazir
A1 - Ehsan Momeni
J0 - Journal of Zhejiang University Science A
VL - 17
IS - 4
SP - 273
EP - 285
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Y1 - 2016
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1500033

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Qu), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (Lw/B). Apart from Qu, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when Lw/B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.

The authors proposed an improved ANN-based predictive model of bearing capacity for thin-wall shallow foundations based on comprehensive testing data. This paper is well written and the model footing tests were carefully done


目的:薄壁扩展式地基已被广泛应用于土壤强度相对较低的沿海工程。目前,已有很多学者对其进行了人工神经网络的适用性研究,希望用此对地基的承重能力进行预测。但是这些研究多数是基于传统的人工神经网络,学习速度慢且受困于局部极小值。本文拟提出一种改进的基于人工神经网络的预测薄壁浅地基承重能力的 模型。
方法:1. 整合145组关于地基承重测试的文献数据和实验数据(包括承重能力、摩擦角、沙的单位重量、基脚宽度和长宽比等);除了承重能力,其他参数都是模型输入;2. 研究各参数对地基承重能力的影响,确定最优的人工神经网络模型参数,并对不同的人工神经网络模型进行 比较。
结论:1. 当基脚长宽比从0.5变为1.12时,地基的承重能力增加了大约一半;2. 基于粒子群优化算法的人工神经网络模型表现最好;在测试数据中,承重能力的预测值和测量值之间高达0.98的相关系数也表明,在无粘性土中,基于人工神经网络的预测模型适用于薄壁浅地基的承重能力预测。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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