Predicting the performance of embankments on PVD-improved subsoils

Chai, Jinchun; Shen, Shui‐Long; Liu, Martin D.; Yuan, Da-Jun

doi:10.1016/j.compgeo.2017.05.018

Cited by 57 publications

(14 citation statements)

References 20 publications

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“…Subsequently, several case studies have reported the successful application of PVDs such as Ballina Bypass, Australia [13], Yaoqiang Airport runway, China [14], and Ramp ES, Ohio [15]. Numerous studies have explained the advantages of PVDs, such as an increase in soil strength [7] and reduce in the total settlement [16]. Furthermore, this technique is also able to improve the highway construction rate and give a good impact on the environment [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are two parameters of smear effect, i.e., the zone diameter of the smear (ds) and the smear effect of permeability ratio (kr). Traditionally, the researchers determine the value of these parameters based on the literature as there is no comprehensive method to measure it so far [5,7]. Previous studies have reported a range of the smear effect of permeability ratio selected values are from 1.3 to 10.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Settlement and Pore Water Pressure of Road Embankment on Yan soft soil Treated with PVDs

2019

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The application of prefabricated vertical drains (PVDs) in the road embankment construction has been successfully performed in many projects throughout the country. The simulation of finite element method (FEM) can assist engineers in modelling very complex structures and foundations. This paper presents a plane–strain numerical analysis that was performed to verify the effectiveness of the model embankment stabilised with PVD using Plaxis 2D version 8. This study employed the smear effect of permeability ratio (kr) of 3 in the PVD modelling. The data of settlement and pore water pressure in the left and right sides of road embankment were monitored for 177 days, then the data were collected and compared by a numerical simulation. The coefficient of determination (R2) was used to assess the performance of the comparative analysis. The results of numerical simulation on settlement and pore water pressure obtained a coefficient of determination of greater than 0.9 which has reached a good agreement with those of the field measurement. On other the hand, there was no significant difference in the performance between both sides of the embankment. The smear effect parameter (kr = 3) is recommended for PVD designs and can provide accurate FEM prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Settlement and Pore Water Pressure of Road Embankment on Yan soft soil Treated with PVDs

2019

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“…Reliable predictions depend heavily on plausible constitutive models with reasonable parameters in geotechnical engineering . To achieve this purpose, many constitutive models have been proposed for soils .…”

Section: Introductionmentioning

confidence: 99%

“…Reliable predictions depend heavily on plausible constitutive models with reasonable parameters in geotechnical engineering. [1][2][3][4][5][6][7] To achieve this purpose, many constitutive models have been proposed for soils. [8][9][10][11] However, different constitutive models render dissimilar results in the process of numerical simulations, prompting variations in engineering decisions that affect the levels of safety, economy, and risk in construction.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model selection for sand with generalization ability evaluation

Jin

Yin

Zhou

et al. 2019

Num Anal Meth Geomechanics

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Summary Current studies have focused on selecting constitutive models using optimization methods or selecting simple formulas or models using Bayesian methods. In contrast, this paper deals with the challenge to propose an effective Bayesian‐based selection method for advanced soil models accounting for the soil uncertainty. Four representative critical state‐based advanced sand models are chosen as database of constitutive model. Triaxial tests on Hostun sand are selected as training and testing data. The Bayesian method is enhanced based on transitional Markov chain Monte Carlo method, whereby the generalization ability for each model is simultaneously evaluated, for the model selection. The most plausible/suitable model in terms of predictive ability, generalization ability, and model complexity is selected using training data. The performance of the method is then validated by testing data. Finally, a series of drained triaxial tests on Karlsruhe sand is used for further evaluating the performance.

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“…Figure 9 also depicts the location of Xinmo, which is near the fracture zone northwest of the Sichuan Basin. The fracture zone includes Longmen Shan Thrust Fault, Minjiang Thrust Fault, Tazang Fault, Huya Thrust Fault, and Qingchuan Fault [43][44][45][46][47]. Thus, the landslide occurred at a geological disaster-prone area.…”

Section: Rainfall and Topographymentioning

confidence: 99%

Landslide Event on 24 June in Sichuan Province, China: Preliminary Investigation and Analysis

Meng

Cheng

et al. 2018

Geosciences

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This paper reports on a massive landslide event, in which 8 million cubic meters of earth and rocks slid down from the top of a mountain in the village of Xinmo, located in the county of Maoxian, in the province of Sichuan, China, on 24 June 2017. This landslide resulted in 10 fatalities and 73 people were reported as missing. This paper details the preliminary investigation, the joint-force rescue activity, and the analysis of the nearby topography, rainfall, and seismic fracture zone. The combined effects of large amounts of rainwater, steep topography, deep-seated sliding interface, and significant altitude difference between the highest point of the mountain and the Xinmo villagers' houses are considered as the main influencing factor that triggered this landslide event. To develop geological disaster-prone areas in the future, four main recommendations to reduce casualties and environmental impacts are provided in this paper.

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Predicting the performance of embankments on PVD-improved subsoils

Cited by 57 publications

References 20 publications

Comparative Analysis of Settlement and Pore Water Pressure of Road Embankment on Yan soft soil Treated with PVDs

Comparative Analysis of Settlement and Pore Water Pressure of Road Embankment on Yan soft soil Treated with PVDs

Bayesian model selection for sand with generalization ability evaluation

Landslide Event on 24 June in Sichuan Province, China: Preliminary Investigation and Analysis

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