Xây dựng mô hình MLR, ANN và FL để dự đoán sức mạnh của đất sét vấn đề được ổn định bằng sự kết hợp giữa vôi nano và puzolan nano có nguồn gốc tự nhiên cho việc xây dựng đường

Springer Science and Business Media LLC - Tập 15 - Trang 1-34 - 2024
Aref M. Al-Swaidani1, Ayman Meziab2, Waed T. Khwies3, Mohamad Al-Bali3, Tarek Lala3
1Faculty of Architectural Engineering, Arab International University, Damascus, Syria
2Faculty of Civil Engineering, Arab International University, Damascus, Syria
3Faculty of Information Technology, Arab International University, Damascus, Syria

Tóm tắt

Nghiên cứu hiện tại nhằm dự đoán sức mạnh của các loại đất sét có vấn đề được xử lý bằng cách kết hợp puzolan có nguồn gốc tự nhiên và bột vôi khi được thêm vào như là phụ gia đất ở quy mô nano. Nghiên cứu phân tích đã sử dụng công cụ hồi quy tuyến tính đa biến (MLR), mạng nơ-ron nhân tạo (ANN) và logic mờ (FL). Các biến của nghiên cứu hiện tại bao gồm: hàm lượng puzolan nano có nguồn gốc tự nhiên (NNP), hàm lượng vôi nano (NL), kích thước hạt trung vị của NNP, hàm lượng silica hoạt động của NNP (SiO2active), giới hạn lỏng ban đầu (ILL) và giới hạn dẻo ban đầu (IPL) của các loại đất được nghiên cứu. NNP được thêm vào với năm tỷ lệ phần trăm, tức là 0%, 0.5%, 1%, 1.5% và 2%, trong khi NL được thêm vào với năm tỷ lệ phần trăm, tức là 0%, 0.3%, 0.6%, 0.9% và 1.2%. Ba kích thước hạt trung vị là 50, 100 và 500 nm đã được nghiên cứu. Dựa trên các loại đất đã được nghiên cứu và các sự kết hợp, 120 hỗn hợp đất đã được chuẩn bị và thử nghiệm. Tỷ lệ chịu tải California (CBR) và chỉ số dẻo (PI) đã được xem xét đặc biệt. Các thử nghiệm CBR được thực hiện trong điều kiện ngâm ướt trên các mẫu được nén đến độ chặt khô tối đa (MDD) tại độ ẩm tối ưu (OMC). Giá trị PI được thu được thông qua bài kiểm tra giới hạn Atterberg. Dựa trên kết quả của các tiêu chí hiệu suất của các mô hình dự đoán đã phát triển, có thể kết luận rằng CBR và PI của các loại đất sét giãn nở có thể được dự đoán hiệu quả bằng cách sử dụng các kỹ thuật ANN và FL. Các kết quả thu được từ MLR cách xa so với các kết quả thu được từ cả ANN và FL. Ngoài ra, công cụ ANN có độ chính xác cao hơn một chút so với FL khi nói đến việc dự đoán CBR và PI. Khả năng cao hơn của các mô hình ANN và FL trong việc dự đoán giá trị CBR và PI, mà thường được thu được thông qua các thử nghiệm tốn thời gian và tốn kém, có thể hữu ích cho các kỹ sư địa kỹ thuật trong việc đánh giá hoặc thiết kế một dự án đường mới. Hơn nữa, khuyến nghị tiến hành tái đánh giá nghiên cứu hiện tại trong tương lai, đặc biệt khi có thêm dữ liệu sẵn có trong tài liệu.

Từ khóa


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