Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting ...(XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between the USS and various basic soil parameters. Based on the soil data sets from TC304 database, a general approach is developed to predict the USS of soft clays using the two machine learning methods above, where five feature variables including the preconsolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL) and natural water content (W) are adopted. To reduce the dependence on the rule of thumb and inefficient brute-force search, the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF. The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation (CV). It is shown that XGBoost-based and RF-based methods outperform these approaches. Besides, the XGBoost-based model provides feature importance ranks, which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.
Display omitted
•XGBoost and RF-based ELMs for predicting the undrained shear strength of soft clays.•Bayesian hyper-parameter optimization for determination of the hyper-parameters.•ELMs performance and transformation model comparison.•Feature relative importance ranked by the proposed XGBoost-based model.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior ...predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.
Display omitted
•Systematic state-of-the-art review of soft computing applications in underground excavations.•Performance comparisons of chosen SCMs including MARS, ANN, XGBoost, and SVM.•Some discussions and further research recommendations provided.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Reliability analysis approach provides a rational means to quantitatively evaluate the safety of geotechnical structures from a probabilistic perspective. However, it suffers from a known criticism ...of extensive computational requirements and poor efficiency, which hinders its application in the reliability analysis of earth dam slope stability. Until now, the effects of spatially variable soil properties on the earth dam slope reliability remain unclear. This calls for a novel method to perform reliability analysis of earth dam slope stability accounting for the spatial variability of soil properties. This paper develops an efficient extreme gradient boosting (XGBoost)-based reliability analysis approach for evaluating the earth dam slope failure probability. With the aid of the proposed approach, the failure probability of earth dam slope can be evaluated rationally and efficiently. The proposed approach is illustrated using a practical case adapted from Ashigong earth dam. Results show that the XGBoost-based reliability analysis approach is able to predict the earth dam slope failure probability with reasonable accuracy and efficiency. The coefficient of variations and scale of fluctuations of soil properties affect the earth dam slope failure probability significantly. Moreover, the earth dam slope failure probability is highly dependent on the selection of auto-correlation function (ACF), and the widely used single exponential ACF tends to provide an unconservative estimate in this study.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper adopts the NGI-ADP soil model to carry out finite element analysis, based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were ...evaluated. More than one thousand finite element cases were numerically analyzed, followed by extensive parametric studies. Surrogate models were developed via ensemble learning methods (ELMs), including the eXtreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR) to predict the maximum lateral wall deformation (δhmax). Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression (DTR), Multilayer Perceptron Regression (MLPR), and Multivariate Adaptive Regression Splines (MARS). This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast, alternative way.
Display omitted
•FE analysis considering soil anisotropy via NGI-ADP model carried out.•Effects of anisotropy on diaphragm wall deflections evaluated.•ELMs as well as soft computing models for prediction of lateral wall deformation.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A small-scale shaking table model test was used to investigate the characteristics of the granular landslide deposits under influences of seismic wave. The model was subjected to horizontal and ...vertical seismic waves. The maximum displacement, width, thickness and area are measured. The results show that vibration frequency significantly influences the deposit shape. The maximum displacement, width, thickness and area of the deposit increase with the enhanced frequency when the vibration orientation is horizontal. The maximum displacement of granule is relatively smaller when the vibration orientation is vertical, while the deposit width and thickness turn out to be greater. Other characteristics of landslide deposits under different seismic waves and some preliminary guidelines for design of engineering protective structures are also provided in this study.
•Down-scaled shaking table model tests designed.•Influences of vibration frequency and orientation from seismic waves examined.•Characteristics including the deposit shape, thickness and area, as well as max. displacement.
Full text
Available for:
GEOZS, IJS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UPCLJ, UPUK, ZAGLJ
A finite-element analysis considering the anisotropy for the undrained shear strength was performed to examine the effects of the total stress-based anisotropic model NGI-ADP (developed by Norwegian ...Geotechnical Institute based on the Active-Direct simple shear-Passive concept) parameters on the base stability of deep braced excavations in clays. These parameters included the ratio of the plane strain passive shear strength to the plane strain active shear strength suP/suA, the ratio of the unloading/reloading shear modulus to the plane strain active shear strength Gur/suA, the plane strain active shear strength suA, the unit weight γ, the excavation width B, the wall thickness b, and the wall penetration depth D. According to the numerical results for 1778 hypothetical cases, extreme gradient boosting (XGBoost) and random forest regression (RFR) were adopted to predict the factor of safety (FS) against basal heave for deep braced excavations. The results indicated that the anisotropic characteristics of soil parameters need to be considered when determining the FS against basal heave for braced excavation. XGBoost and RFR can yield a reasonable prediction of the FS. This paper presents a cutting-edge application of ensemble learning methods in geotechnical engineering.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•FE analyses considering soil anisotropy validated though case history.•Effects of soil properties and twin-tunnel arrangements on tunneling responses.•Machine learning models built to predict tunnel ...maximum bending moment.
Excessive structural forces generated inside tunnel linings could affect the safety and serviceability of tunnels, emphasizing the need to accurately predict the forces acting on tunnel linings during the preliminary design phase. In this study, an anisotropic soil model developed by Norwegian Geotechnical Institute (NGI) based on the Active-Direct shear-Passive concept (NGI-ADP model) was adopted to conduct finite element (FE) analyses. A total of 682 cases were modeled to analyze the effects of five key parameters on twin-tunnel structural forces; these parameters included twin-tunnel arrangements and subsurface soil properties: burial depth H, tunnel center-to-center distance D, soil strength suA, stiffness ratio Gu/suA, and degree of anisotropy suP/suA. The significant factors contributing to the bending moment and thrust force of the linings were the tunnel distance and overlying soil depth, respectively. The degree of anisotropy of the surrounding soil was found to be extremely important in simulating the twin-tunnel construction, and severe design errors could be made if the soil anisotropy is ignored. A cutting-edge application of machine learning in the construction of twin tunnels is presented; multivariate adaptive regression splines and decision tree regressor methods are developed to predict the maximum bending moment within the first tunnel’s linings based on the constructed FE cases. The developed prediction model can enable engineers to estimate the structural response of twin tunnels more accurately in order to meet the specific target reliability indices of projects.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Soil liquefaction is one of the most complicated phenomena to assess in geotechnical earthquake engineering. The conventional procedures developed to determine the liquefaction potential of sandy ...soil deposits can be categorized into three main groups: Stress-based, strain-based, and energy-based procedures. The main advantage of the energy-based approach over the remaining two methods is the fact that it considers the effects of strain and stress concurrently unlike the stress or strain-based methods. Several liquefaction evaluation procedures and approaches have been developed relating the capacity energy to the initial soil parameters, such as the relative density, initial effective confining pressure, fine contents, and soil textural properties. In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. The results clearly prove the capability of the proposed models and the capacity energy concept to assess liquefaction resistance of soils. It is also proposed that these approaches should be used as cross-validation against each other. The result shows that the capacity energy is most sensitive to the relative density.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Probabilistic risk assessment of slope failure evaluates the slope safety in a quantitative manner, which considers the failure probability and failure consequence simultaneously. However, risk ...assessment of unsaturated slope accounting for spatially variable soil-water characteristic curve (SWCC) model parameter and saturated hydraulic conductivity has been rarely reported. A probabilistic risk assessment approach is proposed in current study for rationally quantifying the unsaturated slope failure risk with the aid of Monte Carlo (MC) simulation. The SEEP/W and SLOPE/W modules contained in Geostudio software are applied to carry out deterministic analysis, where factor of safety (
FS
) of the unsaturated slope is calculated by Morgenstern–Price method. The spatially variable hydraulic parameters are characterized by their respective random fields that are transferred from the random void ratio field in this study, rather than generating them separately. The proposed approach is subsequently employed to an unsaturated slope example for exploring the influences of spatially variable void ratio. Results show that the unsaturated slope failure risk is considerably affected by the spatially variable void ratio, and the single exponential autocorrelation function (ACF) popularized in geotechnical engineering tends to underestimate the failure risk in the unsaturated slope risk assessment.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Cytidine 5′-monophosphate (CMP) was widely applied in the food and pharmaceutical industries. Currently, CMP is mainly produced by enzyme catalysis. However, the starting materials for enzyme ...catalysis were relatively expensive. Therefore, seeking a low-cost production process for CMP was attractive. In this study, Escherichia coli (E. coli) was systematically modified to produce CMP. First, a the cytidine-producing strain was constructed by deleting cdd, rihA, rihB, and rihC. Second, the genes involved in the pyrimidine precursor competing pathway and negative regulation were deleted to increase cyti dine biosynthesis. Third, the deletion of the genes that caused the loss of CMP phosphatase activity led to the accumulation of CMP, and the overexpression of the rate-limiting step genes and feedback inhibition resistance genes greatly increased the yield of CMP. The yield of CMP was further increased to 1013.6 mg/L by blocking CMP phosphorylation. Ultimately, the yield of CMP reached 15.3 g/L in a 50 L bioreactor. Overall, the engineered E. coli with a high yield of CMP was successfully constructed and showed the potential for industrial production.
Full text
Available for:
IJS, KILJ, NUK, PNG, UL, UM, UPUK
PDF
