On the Use of Neuro-Swarm System to Forecast the Pile Settlement

Applied Sciences

AhmadSafuan A. Rashid¹  PanagiotisG. Asteris²  Reza Tarinejad³  SeyedAlireza Fatemi⁴  Mahdi Hasanipanah⁵  VanVan Huynh⁶  DanialJahed Armaghani⁶ 

[1] Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia;Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece;Department of Civil Engineering, University of Tabriz, 29 Bahman Blvd, 51666 Tabriz, Iran;Department of Civil and Environmental Engineering, Amirkabir University of Technology, 15914 Tehran, Iran;Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam;

关键词: pile settlement;    neural network;    particle swarm optimization;    hybrid intelligence technique;   

DOI  :  10.3390/app10061904

来源: DOAJ

【 摘 要 】

In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R²) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

【 授权许可】

Unknown