On the Use of Neuro-Swarm System to Forecast the Pile Settlement
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Tunneling and Rock Mechanics · Geotechnical Engineering and Analysis · Grouting, Rheology, and Soil Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece
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
Department of Civil Engineering, University of Tabriz, 29 Bahman Blvd, 51666 Tabriz, Iran
Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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