Tên bài báo:

Application of Artificial Intelligence to Determined Unconfined Compressive Strength of Cement-Stabilized Soil in Vietnam
Tác giả:
Ngô Thị Thanh Hương
Tham gia cùng:
Phạm Tuấn Anh
Vũ Thị Hương Lan
Giáp Văn Lợi
Tạp chí:
Applied Sciences (Switzerland)
Năm xuất bản:
Từ trang 1 đến trang 19
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

Cement stabilized soil is one of the commonly used as ground reinforcement solutions in geotechnical engineering. In this study, the main object was to apply three machine learning (ML) methods namely gradient boosting (GB), artificial neural network (ANN) and support vector machine (SVM) to predict unconfined compressive strength (UCS) of cement stabilized soil. Soil samples were collected at Hai Duong city, Vietnam. A total of 216 soil–cement samples were mixed in the laboratory and compressed to determine the UCS. This data set is divided into two parts of the training data set (80%) and testing set (20%) to build and test the model, respectively. To verify the performance of ML model, various criteria named correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used. The results show that all three ML models were effective methods to predict the UCS of cement-stabilized soil. Amongst three model used in this study, optimized ANN model provided superior performance compare to two others models with performance indicator R = 0.925, RMSE = 419.82 and MAE = 292.2 for testing part. This study can provide an effective tool to quickly predict the UCS of cement stabilized soil with high accuracy.

Từ khóa:

gradient boosting; artificial neural network; support vector machine; feature important analysis
Thông tin tác giả
Ngô Thị Thanh Hương

Ngô Thị Thanh Hương

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