A Three-Point Hyperbolic Combination Model for the Settlement Prediction of Subgrade Filled with Construction and Demolition Waste

Wang, Haiying; Shen, Hui; Xu, Jian; Liang, Linhao

doi:10.3390/ma13081959

Cited by 9 publications

(9 citation statements)

References 45 publications

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“…The prediction results of the PSO-BP and GA-BP neural networks employed in this paper on the test set samples are compared with those obtained from standard BP neural network and curve fitting methods commonly used in engineering such as hyperbola method [48], Hoshino method [49][50], and Asaoka method [51], as well as field-measured data, which is as shown in figure 8. And the predicted accumulated settlement for each method at t = 1,361 d can be found in table 3 and the on-site measured accumulated settlement is 193 mm.…”

Section: Analysis Of Prediction Resultsmentioning

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: Analysis Of Prediction Resultsmentioning

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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“…Subsequently, the fitness values are calculated. The fitness value of each f(xi) firework according to the fitness function f(x) is computed, and the number of firework explosions generated by Si and the explosion radius are calculated according to Equations ( 1)- (2).…”

Section: The Firework Algorithmmentioning

confidence: 99%

“…In engineering, calculation methods for the settlement of soft soil foundations can be divided into three categories: theoretical formula methods based on settlement calculation, numerical analysis methods based on consolidation theory, and settlement prediction methods based on measured data. Commonly used prediction methods include the hyperbolic [2] , three-point, Asaoka, and Poisson methods. With the advancement of artificial intelligence technology, some settlement prediction methods have been developed that use artificial intelligence algorithms, such as neural networks, support vector machines, gray model predictions, and time series methods.…”

Section: Introductionmentioning

confidence: 99%

“…(13) Optimization of firework population. For each firework individual x, its fitness value f(xi) is calculated using Equation (13), and the number of exploding fireworks Si and Ai are calculated using Equations ( 1) and (2). Based on Equations ( 5) to (7), the explosion, displacement, and mutation operations are performed, and the selection strategy given in Equations ( 9) and ( 10) is used to select the optimal firework individuals to form a firework population for the next generation.…”

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confidence: 99%

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FAGSO-BP neural network model for settlement prediction of soft soil foundation

Ma,

Zhao,

Deng

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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To address the problem of difficulty in theoretical formulas and numerical analysis methods to consider the prediction of foundation settlement in the case of multiparameter coupling, the fireworks algorithm (FWA) with a gravity search operator (FAGSO) is introduced into the back propagation (BP) neural network model, and the weight and threshold processes in the neural network are improved by the FAGSO algorithm. A soft soil foundation settlement prediction model based on a FWA optimized BP neural network (FAGSO-BP) with a gravity search operator is proposed. Based on the second phase of the soft foundation treatment project of the Shatian Port Area, Humen City, and Dongguan City, the parameters of pile load height, pore water pressure, and groundwater level were collected and tested. The results indicate that the average relative error of the predicted cumulative settlement value of the soft soil foundation was 0.06%, root mean square error was 7.68, mean absolute error was 4.30, mean absolute percentage error was 1.40%, and mean square error was 59.98. Compared with the BP neural network, GA-BP neural network, and FWA-BP neural network, the error was smaller and the model stability was higher.

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“…However, there are still some large postconstruction settlements in the treatment section of pile-soil composite subgrade, such as car jump at the bridge head. Many scholars and engineers have carried out much useful work in the prediction of postconstruction settlements by means of theoretical analysis [3], numerical simulation [4], and on-site observation data fitting [5,6]. Previous related works on postconstruction settlement prediction are discussed and tabulated in Table 1.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for Postconstruction Settlement of Pile‐Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation

Shan

Jiang

Chang

et al. 2021

Advances in Civil Engineering

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This paper presents a postconstruction settlement prediction method for pile-soil composite subgrade based on the multilevel fuzzy comprehensive evaluation principle. In this method, the variation range of postconstruction settlement can be obtained from a simple calculation based on the basic data of actual engineering. Firstly, according to the characteristics of influencing factors in the construction of soft soil subgrade, the evaluation index set and two-level factor index sets were selected. The grading standards of the evaluation index and factor index were determined according to the allowable value of the standard and the numerical simulation results. Secondly, each factor index was standardized, and the normal distribution function in the form of exponential was used to construct the standard membership function for the first and second factor indexes. Finally, the comprehensive evaluation matrix of postconstruction settlement of composite subgrade was constructed based on the entropy weight method. The variation range of postconstruction settlement was predicted by the principle of maximum membership. The example analysis shows that the predicted results of the prediction method and the field measurement method are in good agreement, indicating that the proposed method can realize the postconstruction settlement prediction of composite subgrade, and the results are more accurate and more instructive.

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A Three-Point Hyperbolic Combination Model for the Settlement Prediction of Subgrade Filled with Construction and Demolition Waste

Cited by 9 publications

References 45 publications

Prediction of dredged soil settlement based on improved BP neural network

Prediction of dredged soil settlement based on improved BP neural network

FAGSO-BP neural network model for settlement prediction of soft soil foundation

A Prediction Method for Postconstruction Settlement of Pile‐Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation

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