Prediction of soft soil foundation settlement in Guangxi granite area based on fuzzy neural network model

Luo, Junhui; Chao, Wu; Liu, Xianlin; Mi, Decai; Zeng, Fuquan; Zeng, Yongjun

doi:10.1088/1755-1315/108/3/032034

Cited by 8 publications

(7 citation statements)

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“…In recent years, the creep settlement prediction method has become a predominant prediction method because it has good applicability for newly filled soil. The GM method and BP neural network prediction method based on neural networks 25 , 26 are self-adaptive and suitable for situations with less measured settlement data.…”

Section: Settlement Prediction Analysismentioning

confidence: 99%

Long-term monitoring and analysis of the longitudinal differential settlement of an expressway bridge–subgrade transition section in a loess area

Chao

et al. 2022

Sci Rep

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To solve the problem of "bridgehead bumping" in the transition section between the road and bridge of an expressway in a collapsible loess area, a lime–soil compaction pile composite foundation is used for the first time in the transition section between the road and bridge of an expressway in China; the loess subgrade is improved by adding lime, and the subgrade is arranged in a multilayer geogrid for the joint treatment of various engineering measures. At the same time, a new type of precision differential pressure settlement meter is used to monitor the long-term settlement of a bridge–subgrade transition section with a small settlement magnitude after the joint treatment, and the distribution characteristics and variation laws of the settlement along the longitudinal direction of the line are obtained. The results show that the effect is better and the differential settlement is smaller when using a lime–soil compaction pile composite foundation; lime improves the loess subgrade backfill, and the multilayer geogrid addresses the bridge–subgrade transition in the collapsible loess area. The differential settlement and settlement rate of the subgrade and abutment increase with increased monitoring time, and the differential settlement increases gradually, while the growth rate decreases gradually and finally tends to be stable. The differential settlement of the transition section is predicted and analysed by using a hyperbolic curve, exponential curve and their combination in a prediction model, and the prediction analysis shows that the combined prediction model has the best prediction effect. These research results can provide guidance and reference for the design and construction of subgrade structures similar to the wet transition section between roads and bridges.

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Section: Settlement Prediction Analysismentioning

confidence: 99%

Long-term monitoring and analysis of the longitudinal differential settlement of an expressway bridge–subgrade transition section in a loess area

Chao

et al. 2022

Sci Rep

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“…Within the settlement determination, artificial neural network (ANN) models have been introduced to this field since about three decades ago [28][29][30][31][32]. is was followed by several implementations over the past two decades using diverse AI models including gene expression programming [33,34], support vector machine [35], least square support vector machine [36], and neuro-fuzzy network [37,38]. Among several AI models reported in the literature, adaptive neural fuzzy inference system (ANFIS) model exhibited a distinguished modeling technique in the field of geotechnical engineering [39].…”

Section: Literature Review and Research Motivationmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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“…e most important thing to build structures in these soft soil areas is to solve the problems of foundation settlement and stability of structures. With the development and utilization of soft soil areas by government departments, the problem of foundation settlement of buildings in soft soil areas not only affects the project cost and project cycle, but also affects the quality of the entire project [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Road Soft Soil Foundation Treatment and Settlement Prediction Method

Zhai¹

2022

Scientific Programming

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In order to effectively predict the settlement of soft soil foundation, improve the accuracy of road soft soil foundation settlement prediction, and improve the safety of the project, this paper proposes an optimized SVM-AR model and discusses the application scope of the SVM model and the time series AR model, respectively. The SVM-AR model is proposed by combining the respective advantages of the two types of models. Firstly, the prediction method of foundation settlement is analyzed and studied, and then the improved ABC algorithm is used to optimize the SVM model. Secondly, the optimized SVM model is combined with the AR model, the ABC-SVM model is used to predict the trend settlement, and the AR model is used to predict the random settlement and then combined to obtain the predicted settlement. The example verification shows that SVM-AR is more accurate than the SVM model prediction results and better reflects the settlement process of highway soft soil foundation.

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Prediction of soft soil foundation settlement in Guangxi granite area based on fuzzy neural network model

Cited by 8 publications

References 0 publications

Long-term monitoring and analysis of the longitudinal differential settlement of an expressway bridge–subgrade transition section in a loess area

Long-term monitoring and analysis of the longitudinal differential settlement of an expressway bridge–subgrade transition section in a loess area

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Machine-Learning-Based Road Soft Soil Foundation Treatment and Settlement Prediction Method

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