Theoretical and Numerical Analyses on Hydro–Thermal–Salt–Mechanical Interaction of Unsaturated Salinized Soil Subjected to Typical Unidirectional Freezing Process

Zhang, Xudong; Zhai, Encheng; Wu, Yajun; Sun, Deqing; Lu, Yitian

doi:10.1061/(asce)gm.1943-5622.0002036

Cited by 74 publications

(13 citation statements)

References 43 publications

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“…After analysis, when the delay time is 1, the autocorrelation coefficients of the three cases are the largest, which are 0.938, 0.779, and 0.786, respectively. Therefore, the delay time of settlement 4 Geofluids data is 1. Secondly, when the delay time is 1, FNN is used to determine the embedding dimension of settlement data, as shown in Figure 1.…”

Section: Example Applicationmentioning

confidence: 99%

“…Effectively improving the prediction accuracy of foundation pit deformation is of great significance for judging the stability of foundation pit, predicting the damage degree of surrounding buildings, and scientifically grasping the future deformation trend of foundation pit. At present, the methods used in deformation prediction include grey prediction method [1,2], regression method [3,4], extreme learning machine method [5,6], support vector machine method [7,8], artificial neural network method [9][10][11], and combination prediction method [12,13], all of which have achieved certain prediction effect in the application of foundation pit deformation prediction, but there are also some shortcomings: the grey prediction method has a high degree of dependence on the original data, and the short-term prediction effect is poor. The regression method is sensitive to outliers and easy to over fit.…”

Section: Introductionmentioning

confidence: 99%

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Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm

Jing

Wang

2021

Geofluids

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In order to improve the prediction accuracy of foundation pit deformation, an improved optimization algorithm of supply and demand-exponential power product foundation pit deformation prediction model (ISDO-EPP model) is proposed. Through six standard test functions and three application examples, the optimization ability of the ISDO algorithm is verified, and the optimization results are compared with those of basic supply demand optimization algorithm (SDO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), moth swarm algorithm (MSA), and particle swarm optimization algorithm (PSO). Taking the settlement prediction of three foundation pits as an example, the delay time and embedding dimension of each case are determined by autocorrelation function method and false nearest neighbor method, and input and output vectors are constructed to train and predict each model. The results show that the search ability of the ISDO algorithm is better than that of SDO and other five algorithms, and the ISDO algorithm has better search accuracy, global search ability, and robustness. The absolute values of average relative errors of the ISDO-EPP model for three cases are 0.73%, 3.36%, and 1.33%, respectively, which are better than ISDO-SVM and ISDO-BP models. It shows that the ISDO algorithm can effectively optimize the parameters of the EPP model, and the ISDO-EPP model is feasible and effective for deformation prediction.

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Section: Example Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm

Jing

Wang

2021

Geofluids

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“…The essence of deep dewatering is to destroy the occulent structure of EPS, change the particle size, oc structure and water storage state (Sheng et al, 2010). To realize the objective of deep dewatering and reducing the volume of LS, numerous methods have been applied to facilitate the dewatering e ciency of sewage sludge in recent years, such as chemical (Lin et al, 2014), freeze-thaw (Wu et al, 2020;Zhang et al, 2021b), thermal (Ruan et al, 2021), electrolysis (Yuan et al, 2011) and ultrasonication (Feng et al, 2009). Among them, chemical conditioning possesses the following advantages: low cost, convenient operation, and remarkable dewatering effect (Xu Y. et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Insights into vacuum preloading consolidation of landfill sludge based on Fe2+-activated sodium persulfate

Zhang

et al. 2022

Environ Sci Pollut Res

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To further reduce the in-situ sludge from land ll, Fe 2+ -activated sodium persulfate combined with vacuum preloading was rst proposed. Firstly, the effects of optimal Na 2 S 2 O 8 dosage on the land ll sludge (LS) were investigated by the vacuum ltration experiments. Then, vacuum preloading experiments were conducted on the sludge with different Na 2 S 2 O 8 dosage to study the water content, water discharge and settlement. Besides, sludge particle size diversi cation was carried out by particle size distribution and scanning electron microscopy (SEM) tests. The results were summarized as follows: the speci c resistance of ltration (SRF) of LS could be reduced by 98.5% mostly when the Na 2 S 2 O 8 dosage was 30%; the particle size became signi cantly smaller, and large particles were converted to small particles; the water content dropped from 86.9% to 58.3%; the SEM test manifested the oxidation of sodium persulfate caused the destruction of the glial structure of the sludge and the recombination of partial particles.

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“…The use of vacuum as preload and vertical drainage pipes to reduce postconstruction settlement and increase shear strength in soft soil was first mentioned by Kjellman [4]. Since then, many scholars have systematically studied the vacuum preloading method from laboratory tests [5][6][7], field tests [8,9], numerical simulations [10,11] and theoretical studies [12][13][14]. The results show that the vacuum preloading method can be effectively applied to the improvement of ultra-soft soil foundation.…”

Section: Introductionmentioning

confidence: 99%

Ultra-soft Ground Improvement Using Air-Booster Vacuum Preloading Method: Laboratory Model Test Study

Lei

Fang

Liu

et al. 2021

Int. J. of Geosynth. and Ground Eng.

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The air-booster vacuum preloading (AVP) method has been adopted for the treatment of ultra-soft soil foundations, especially in land reclamation projects, but the air injection mode has not yet been studied. In this paper, the authors investigated the difference in improvement effect in laboratory tests of two different air injection modes, including variable pressure and constant pressure. The results show that compared with the constant pressure AVP (CAVP) method with constant air injection pressure of 75 kPa, the variable pressure AVP (VAVP) method with variable air injection pressure from 25 to 150 kPa has a better effect on ultra-soft soil improvement. Besides, if the final air injection pressure increases properly, and the air injection pressure increases with the times of air injections slowly, the soil improvement effect is significantly improved. Compared to CAVP method, VAVP method can fully utilize the air injection effect of pressurization by forming closed spaces and enhancing permeability by crack growth.

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Theoretical and Numerical Analyses on Hydro–Thermal–Salt–Mechanical Interaction of Unsaturated Salinized Soil Subjected to Typical Unidirectional Freezing Process

Cited by 74 publications

References 43 publications

Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm

Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm

Insights into vacuum preloading consolidation of landfill sludge based on Fe2+-activated sodium persulfate

Ultra-soft Ground Improvement Using Air-Booster Vacuum Preloading Method: Laboratory Model Test Study

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