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

• Published in: Journal of Zhejiang University. A. Science Vol. 17; no. 4; pp. 273 - 285
• Main Authors: Rezaei, Hossein, Nazir, Ramli, Momeni, Ehsan
• Format: Journal Article
• Language: English
• Published: Hangzhou Zhejiang University Press 01.04.2016; Springer Nature B.V
• Subjects: Airports; Artificial intelligence; Artificial neural networks; Civil Engineering; Classical and Continuum Physics; Correlation coefficient; Correlation coefficients; Datasets; Engineering; Industrial Chemistry/Chemical Engineering; Learning theory; Load tests; Mechanical Engineering; Neural networks; Particle swarm optimization; Prediction models; Sand; Shallow foundations; Site planning; Soil bearing capacity; Soil strength; Spread foundations; Swarm intelligence; Sand; Bearing capacity; 粒子群优化; Thin-walled foundation; Particle swarm optimization (PSO); 承重能力; 人工神经网络; TU43; 沙; Artificial neural network (ANN); 薄壁地基
• ISSN: 1673-565X; 1862-1775
• 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 LJB 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.