Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

Abstract

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics

“…ANN networks are able to model any kind of data, and modeling requires a specific network architecture for optimal accuracy of modeled results. 55 When the number of inputs and outputs is fixed, the undefined architecture parameters are, the number of the hidden layers, and the number of neurons on each hidden layer. See Table 5 with a selection of architectures used on this study.…”

“…The MAE represents the difference between the experimental input data and predicted output values of the model, determined by averaging the absolute difference, | Y i − Y ^ i | , over the chosen dataset 54,55 :…”

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

“…The ANN has been used to estimate the soil water retention curve based on field-based soil data [60]. Recently, Pham et al [61] used the ANN to predict the soil coefficient of consolidation as a mechanical parameter to define the compaction or consolidation status of the soil. Khan et al [62] establish an ANN-predictive model of soil temperature as geotechnical properties of clay-rich soil using a field dataset.…”

Nghiên cứu dự báo cường độ bê tông xốp và xác định tương quan với các tham số đầu vào bằng máy vec tơ hỗ trợ

“…Điều này khẳng định trong các nghiên cứu về ML thì việc đánh giá nhiều chỉ tiêu khác nhau là đặc biệt quan trọng. Công thức tính toán ba chỉ tiêu trên có thể được tham khảo trong các tài liệu [25][26][27].…”