Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Due to the limitation in the prediction of the foundation pit settlement, this paper proposed a new methodology which takes advantage of the grey Verhulst model and a genetic algorithm. In the previous study, excavation times are often the only factor to predict the settlement, which is mainly because the correspondence between real-time excavation depth and the excavation time is hard to determine. To solve this issue, the supporting times are precisely recorded and the excavation depth rate can be obtained through the excavation time length and excavation depth between two adjacent supports. After the correspondence between real-time excavation depth and the excavation time is obtained, the internal friction angle, cohesion, bulk density, Poisson’s ratio, void ratio, water level changes, permeability coefficient, number of supports, and excavation depth, which can influence the settlement, are taken to be considered in this study. For the application of the methodology, the settlement monitoring point of D4, which is near the bridge pier of the highway, is studied in this paper. The predicted values of the BP neural network, GA-BP neural network, BP neural network optimized by the grey Verhulst model, and GA-BP neural network optimized by the grey Verhulst model are detailed compared with the measured values. And the evaluation indexes of RMSE, MAE, MSE, MAPE, and are calculated for these models. The results show that the grey Verhulst model can greatly improve the consistency between predicted values and measured values, while the accuracy and resolution is still low. The genetic algorithm (GA) can greatly improve the accuracy of the predicted values, while the GA-BP neural network shows low reflection to the fluctuation of measured values. The GA-BP neural network optimized by the grey Verhulst model, which has taken the advantages of GA and the grey Verhulst model, has extremely high accuracy and well consistency with the measured values.

[1]  M. He,et al.  A physical modeling-based study on the control mechanisms of Negative Poisson's ratio anchor cable on the stratified toppling deformation of anti-inclined slopes , 2021, International Journal of Rock Mechanics and Mining Sciences.

[2]  Y. Wang,et al.  Investigation on the effect of freeze-thaw on fracture mode classification in marble subjected to multi-level cyclic loads , 2021, Theoretical and Applied Fracture Mechanics.

[3]  Xiaodong Wang,et al.  Experimental study on vacuum preloading consolidation of landfill sludge conditioned by Fenton's reagent under varying filter pore size , 2021, Geotextiles and Geomembranes.

[4]  Yalu Han,et al.  Assessment of Soil Thermal Conductivity Based on BPNN Optimized by Genetic Algorithm , 2020 .

[5]  RETRACTED ARTICLE: Study on settlement prediction model of deep foundation pit in sand and pebble strata based on grey theory and BP neural network , 2020, Arabian Journal of Geosciences.

[6]  T. Rabczuk,et al.  Three-dimensional mesoscale computational modeling of soil-rock mixtures with concave particles , 2020 .

[7]  Bo Zeng,et al.  A new-structure grey Verhulst model for China's tight gas production forecasting , 2020, Appl. Soft Comput..

[8]  Yajun Wu,et al.  Coupling analysis of the heat-water dynamics and frozen depth in a seasonally frozen zone , 2020 .

[9]  Han Wu,et al.  Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network , 2020, Advances in Civil Engineering.

[10]  Ke Yang,et al.  A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network , 2020 .

[11]  M. He,et al.  Investigating Toppling Failure Mechanism of Anti-dip Layered Slope due to Excavation by Physical Modelling , 2020, Rock Mechanics and Rock Engineering.

[12]  Yong Tan,et al.  Forensic Geotechnical Analyses on the 2009 Building-Overturning Accident in Shanghai, China: Beyond Common Recognitions , 2020 .

[13]  Suzanne Lacasse,et al.  State-of-the-art review of soft computing applications in underground excavations , 2020, Geoscience Frontiers.

[14]  Jun Hu,et al.  Triaxial behavior of cement-stabilized organic matter–disseminated sand , 2020 .

[15]  Qing-hua Zhu,et al.  Multiscale Laboratory Study and Numerical Analysis of Water-Weakening Effect on Shale , 2020 .

[16]  L. Tang,et al.  Study of the grey Verhulst model based on the weighted least square method , 2020 .

[17]  W. Tian,et al.  Analysis of Deformation Characteristics of Foundation-Pit Excavation and Circular Wall , 2020, Sustainability.

[18]  Yang-ping Yao,et al.  Prediction method of creep settlement considering abrupt factors , 2020 .

[19]  Jianzhong Wang,et al.  Influence of sample length on gray fuzzy prediction performance , 2020, J. Intell. Fuzzy Syst..

[20]  Yun Lin,et al.  Softening Damage Analysis of Gypsum Rock With Water Immersion Time Based on Laboratory Experiment , 2019, IEEE Access.

[21]  W. Ye,et al.  Experimental and numerical study on heavy metal contaminant migration and retention behavior of engineered barrier in tailings pond. , 2019, Environmental pollution.

[22]  Lin Zhang,et al.  Study on a combined prediction method based on BP neural network and improved Verhulst model , 2019, Systems Science & Control Engineering.

[23]  Yong He,et al.  Application of optimized grey discrete Verhulst–BP neural network model in settlement prediction of foundation pit , 2019, Environmental Earth Sciences.

[24]  Yalu Han,et al.  Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve , 2019, Frattura ed Integrità Strutturale.

[25]  Wei Guo,et al.  Research on the Grey Verhulst Model Based on Particle Swarm Optimization and Markov Chain to Predict the Settlement of High Fill Subgrade in Xiangli Expressway , 2019, Mathematical Problems in Engineering.

[26]  Qiang Zhang,et al.  Generation of Numerical Models of Anisotropic Columnar Jointed Rock Mass Using Modified Centroidal Voronoi Diagrams , 2018, Symmetry.

[27]  Weiya Xu,et al.  A coupling method incorporating digital image processing and discrete element method for modeling of geomaterials , 2018 .

[28]  Günther Meschke,et al.  Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes , 2017, Computers & Structures.

[29]  F. M. Gaone,et al.  Insights from a shallow foundation load-settlement prediction exercise , 2018 .

[30]  Mark B. Jaksa,et al.  Load-settlement behavior modeling of single piles using artificial neural networks and CPT data , 2017 .

[31]  Ali Ghorbani,et al.  Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression , 2017, Appl. Comput. Intell. Soft Comput..

[32]  Weiya Xu,et al.  Investigation on jointed rock strength based on fractal theory , 2017 .

[33]  Simaan M. AbouRizk,et al.  Intelligent Approach to Estimation of Tunnel-Induced Ground Settlement Using Wavelet Packet and Support Vector Machines , 2017, J. Comput. Civ. Eng..

[34]  Wujiao Dai,et al.  Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter , 2016, ISPRS Int. J. Geo Inf..

[35]  S. Shen,et al.  Ground fissures in Xi’an and measures to prevent damage to the Metro tunnel system due to geohazards , 2016, Environmental Earth Sciences.

[36]  David Lehký,et al.  Neural network ensemble-based parameter sensitivity analysis in civil engineering systems , 2017, Neural Computing and Applications.

[37]  Hisham T. Eid,et al.  Estimating the Elastic Settlement of Piled Foundations on Rock , 2015 .

[38]  Sifeng Liu,et al.  A multi-variable grey model with a self-memory component and its application on engineering prediction , 2015, Eng. Appl. Artif. Intell..

[39]  Ning Xu,et al.  The Optimization of Grey Verhulst Model and Its Application , 2015 .

[40]  Hehua Zhu,et al.  A case history of shield tunnel crossing through group pile foundation of a road bridge with pile underpinning technologies in Shanghai , 2015 .

[41]  Mohamed A. Shahin,et al.  Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks , 2014 .

[42]  Payam Setoodeh,et al.  Modeling and analysis of the thermal conductivities of air saturated sandstone, quartz and limestone using computational intelligence , 2014 .

[43]  Zhu-Hong You,et al.  Predicting dynamic deformation of retaining structure by LSSVR-based time series method , 2014, Neurocomputing.

[44]  Xin Zhao,et al.  Settlement Monitoring of a Supertall Building Using the Kalman Filtering Technique and Forward Construction Stage Analysis , 2014 .

[45]  Ming Jiang,et al.  Grey System Theory based prediction for topic trend on Internet , 2014, Eng. Appl. Artif. Intell..

[46]  Yong Tan,et al.  Characteristics of a Large-Scale Deep Foundation Pit Excavated by the Central-Island Technique in Shanghai Soft Clay. II: Top-Down Construction of the Peripheral Rectangular Pit , 2013 .

[47]  Chun Mei Zhang.  Applications of Soil Nailed Wall in Foundation Pit Support , 2013 .

[48]  Yong Tan,et al.  Characteristics of a Large-Scale Deep Foundation Pit Excavated by the Central-Island Technique in Shanghai Soft Clay. I: Bottom-Up Construction of the Central Cylindrical Shaft , 2013 .

[49]  Yusuf Erzin,et al.  The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test , 2012, Neural Computing and Applications.

[50]  Weiya Xu,et al.  Creep failure mode and criterion of Xiangjiaba sandstone , 2012 .

[51]  S. Mohyeddin Bateni,et al.  Estimating soil thermal properties from sequences of land surface temperature using hybrid Genetic Algorithm-Finite Difference method , 2012, Eng. Appl. Artif. Intell..

[52]  Chang Xu,et al.  Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis , 2012, Eng. Appl. Artif. Intell..

[53]  Dong-Sheng Jeng,et al.  Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model) , 2011, Eng. Appl. Artif. Intell..

[54]  Okyay Kaynak,et al.  Grey system theory-based models in time series prediction , 2010, Expert Syst. Appl..

[55]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[56]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .