Prediction of Three-Dimensional Consolidation Settlement: Observational Method and Its Applicability

Chung, S. G.; Kweon, H. J.

doi:10.1061/ijgnai.gmeng-7918

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Based on existing research, the current methods for predicting ground settlement mainly include purely theoretical calculation methods based on soil consolidation principles [17][18][19], various curvefitting methods based on measured settlement data commonly used in engineering [20][21], and emerging dynamic prediction methods represented by artificial neural network analysis [22][23][24]. In the process of settlement prediction, purely theoretical calculation methods based on ground consolidation settlement principles are overly complex and involve numerous parameters, making them less convenient to use.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dredged soil settlement based on improved BP neural network

Li,

Liu

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.

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Section: Introductionmentioning

confidence: 99%

Prediction of dredged soil settlement based on improved BP neural network

Li,

Liu

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Physical model case study: treatment effect of soft ground by vacuum preloading combined with liquid bag pressurization method

Feng,

Xing,

Lei

et al. 2024

Can. Geotech. J.

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Vacuum preloading and composite ground reinforcement are commonly used methods for reinforcing soft soil, but there is a lack of integrated design method for vacuum preloading combined with composite ground. This case study introduces an innovative approach that combines vacuum preloading with liquid bag pressurization to achieve the integrated design of consolidation drainage method and composite ground reinforcement, which is different from the reported air bag pressurization. To illustrate the effectiveness of this method. Model tests were carried out to analyze the variation of water discharge, pore water pressure, ground settlement, and average consolidation degree in the process of vacuum consolidation. The study investigated the water content, undrained shear strength and ground bearing capacity of composite ground after ground treatment. A correlation between average undrained shear strength and characteristic value of ground-bearing capacity was established to evaluate and predict the treatment effect of composite ground. Research shows that compared with traditional vacuum preloading, the undrained shear strength can be increased by 13.78%~65.08%, and the characteristic value of bearing capacity for the composite ground can be enlarged by 2.3-4 times. These results indicate that the vacuum preloading combined with liquid bag pressurization can significantly improve reinforcement effect on soft ground.

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Prediction of Three-Dimensional Consolidation Settlement: Observational Method and Its Applicability

Cited by 2 publications

References 26 publications

Prediction of dredged soil settlement based on improved BP neural network

Prediction of dredged soil settlement based on improved BP neural network

Physical model case study: treatment effect of soft ground by vacuum preloading combined with liquid bag pressurization method

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