MCP-DD prediction method of single bridge static cone penetration curve of rock and soil

Static cone penetration test is often used in engineering practice to obtain relevant geological information. Yet the relatively sparse arrangement of test holes results in a large number of unknown areas in the site, which could affect the accurate judgment of the actual geological information in engineering design and construction. Therefore, this study proposes MCP-DD (minimax concave penalty-distance determined) prediction method for single bridge static cone penetration curve of rock and soil. It contains three main parts: 1) screen neighboring training data through the neighborhood radius search algorithm; 2) compute trend estimation functions of selected data through the modified MCP algorithm based on the B-spline basis function; 3) weight the trend estimation function by the spatial correlation function to obtain an integrated prediction model. This method was used to predict the single bridge static cone penetration curve of a certain engineering site. Results show that compared with the traditional linear interpolation method, the proposed method exhibits a higher coefficient of determination R2. The mean absolute error MAE reduction reduced by 26.8% ~55.8% and the root mean squared error RMSE reduces by 25.2% ~54.9%. The optimal range for predicting the radius R0 is 25.0~37.8 m. Increasing average number of relevant data points leads to smaller average relative distance between the predicted points and the relevant data points, thus bettering the prediction performance of the model.