The Inversion Analysis and Material Parameter Optimization of a High Earth-Rockfill Dam during Construction Periods

Abstract

Inversion analysis is usually an efficient solution to process the monitoring data of earth-rockfill dams. However, it is still difficult to obtain calculation results that are consistent with monitoring data due to different construction statuses. To deal with this situation and to introduce a new solution to improve calculation accuracy, the general method of inversion analysis based on back-propagation neural networks and the original step-by-step inversion method assuming that the parameters of the constitutive model vary with construction periods are introduced and verified in this work. Then, both methods are applied in the inversion analysis of a high gravelly soil core rock-fill dam during construction periods. Moreover, the relationship between the inversed material parameters and the stress values of the core wall is discussed. The material parameters are further optimized to obtain more accurate displacement values. The results show that the step-by-step inversion method has a higher accuracy in vertical compression values compared with the conventional inversion method, the trend of material parameter K is more significant than other parameters, and the proposed variable parameter constitutive model has an accuracy between the step-by-step and conventional inversion methods. Conclusions can be drawn that the original step-by-step inversion method has more advantages than the conventional method and the variable parameter constitutive model proposed in this paper might be more suitable for the analysis of a high earth-rockfill dam during construction periods.

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

“…Given the scarcity of research specifically focusing on the back analysis of soil parameters surrounding caisson engineering, the comparison primarily considers intelligent back analysis studies on soil-rock dams, pile foundations, and tunnel engineering. Pan et al [9] produced a stepwise back analysis method based on the BP ANN for the variation of constitutive model parameters along the construction period. The effectiveness of the parameters was evaluated by comparing the data obtained from the inverted parameters inputted into the forward model with the monitoring data.…”

“…For example, the vertical soil pressure at the four corners of the cutting edges. As a result, the 97 sensor readings are condensed into 20 representative monitoring results, including: the average vertical earth pressure at the corners of the cutting edges × 1, the average vertical earth pressure at the long side of the caisson × 1, the The smoothed data in Figure 14 was obtained using Equation (9), where s i represents the ith data after smoothed, o j refers to the jth raw data point, and n w indicates the window size determined by the desired level of smoothing. In Figure 14, the smoothed curve was generated with n w set to 60.…”

“…In the context of large-volume concrete dams and earth-rockfill dams, mature ANN algorithms have been employed to train a large amount of experimental or numerical simulation data. The resulting models are commonly used for back analysis of dam material seepage characteristics [6], geotechnical parameters of the dam body and surrounding rock mass, as well as parameter variation during construction [9], and prediction of optimal dam arch shape [4]. During this period, the intelligent models used for prediction have also been continuously improving.…”

Back Analysis of Geotechnical Engineering Based on Data-Driven Model and Grey Wolf Optimization

“…However, the numerical method is time-consuming and limits the engineering application of back analysis. In order to overcome the limitations of the numerical method, various machine learning-based surrogate models were a focus of attention to approximate the response of the geotechnical structure in the past decades [8][9][10][11]. The neural network method was utilized to construct the intelligent displacement back analysis model for identifying the mechanical property of the surrounding rock mass [12][13][14][15].…”