Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này: https://dspace.ctu.edu.vn/jspui/handle/123456789/105247
Nhan đề: A novel swarm intelligence optimized extreme learning machine for predicting soil shear strength: A case study at Hoa Vuong new urban project (Vietnam)
Tác giả: Ha, Nhu Viet
Pham, Thai Binh
Bui, Tien Dieu
Từ khoá: Extreme learning machine
Particle swarm optimization
Soil
Shear strength
Năm xuất bản: 2023
Tùng thư/Số báo cáo: Vietnam journal of Earth sciences;Vol.45, No.02 .- P.219-237
Tóm tắt: In geotechnical engineering, soil shear strength is one of the most important parameters used in the design and construction of construction projects. However, determining this parameter in the laboratory is costly and time-consuming. Therefore, the main objective of this work is to develop a new alternative machine learning approach based on extreme learning machine (ELM) and Particle Swarm Optimization (PSO), namely PSO-ELM, for the shear strength prediction of soil for the Hoa Vuong new urban project in Nam Dinh province, North Vietnam. For this purpose, twelve soil parameters were collected on data from a survey of 155 soil samples to construct and validate the proposed model. We assessed the model's performance using the root-mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2). We compared the model's capability with five benchmark models, support vector regression (SVR), Gaussian process (GP), multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN), and the fast-decision tree (Fast-DT). The results revealed that the proposed PSO-ELM model yielded the highest prediction performance and outperformed the five benchmark models. It suggests that PSO-ELM can be an alternative method in estimating the shear strength of soil that would help geotechnical engineers reduce the cost of construction.
Định danh: https://dspace.ctu.edu.vn/jspui/handle/123456789/105247
ISSN: 0866-7187
Bộ sưu tập: Vietnam journal of Earth sciences

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