Geotechnical Engineering Parameters Research Articles

Hybrid XGB model for predicting unconfined compressive strength of solid waste-cement-stabilized cohesive soil

The utilization of cement has been found to have negative environmental impacts. In order to reduce the quantity of cement used and improve the mechanical properties of solid waste-cement-stabilized cohesive soil, the incorporation of solid waste as additives has been investigated. Unconfined compressive strength is a crucial parameter in geotechnical engineering. However, existing empirical formulas have limited accuracy and applicability when it comes to the unconfined compressive strength of solid waste-cement-stabilized cohesive soil. The machine learning model can be used to provide accurate and comprehensive predictions by considering the nonlinear relationships between independent and dependent variables. This study aims to propose a machine learning model tuned by optimization algorithms with high generalization performance in accurately predicting the unconfined compressive strength. Firstly, a database containing 474 specimens was developed. Secondly, eight machine learning models were established, composed five single models and three hybrid models, to train and test the database. Six performance indicators were employed to evaluate the generalization ability of these models. Finally, the optimal model was selected for analysis of the importance of the feature variables using shapley additive explanations, which were compared with those of the existing empirical model. The research findings indicated that, the extreme gradient boosting model tuned with tree-structured parzen estimators exhibited the highest predictive accuracy and generalization ability. The curing age, cement content, plastic limit, and water content were identified as the most critical factors influencing the unconfined compressive strength. Among the chemical components in solid waste, the aluminum oxide content and silicon dioxide content were found to significantly influence the unconfined compressive strength, while the impact of calcium oxide content was relatively minor. Furthermore, the optimal solid waste content was found to be around 10 %. This study made a significant contribution to the effective utilization of waste resources in the context of sustainable construction practices.

Modelowanie cyfrowe w celu przewidywania zagrożenia osuwania się gruntu na wybranym obszarze; Przypadek testowy Ponzano (Włochy)

Creating a digital model is one of the aims of the geotechnical engineers, to predict the land sliding hazard which occur in different regions in the world. In February 2017, an extensive landslide occurred in the hamlet of Ponzano as a sloped area in the Abruzzo region in Italy. In this regard, predicting the land sliding hazard is one of the important issues to prevent hazard to the civilizations. In this project we created a model in the 3D dimension through the Plaxis 3D numerical solution software from the data based of the region Abruzzo in 2007 to evaluate the land sliding hazard before happened and then compare the results with the data from the drone data surveyed recently in the 2022. In this regard, the data from the Abruzzo resources from 2007 imported into the QGIS as the open-source cross-platform software to analysis the geospatial data and then imported into the Recap software to work on the point cloud data and then imported into the Civil 3D software to create a solid surface from the TIN surface and finally since the solid surface contains a large number of irrelative details, they were imported into the Rhinoceros software to create a NURBS surface to be smoothed for better performance in the analysis. The NURBS surface imported into the Plaxis and all of the geometry and geotechnical engineering parameters by considering the investigated geotechnical survey that was conducted in parallel in the area, defined for the model. The “Hardening Soil” model considered for the 1st layer as the “clay and lime” and the “Hoek-Brown” model defined for the 2nd layer as the “marl-flysch”. A fine mesh elements distribution assessed also for the model. The phases defined as the “gravity” to define the unit weight of the soil layers, the “plastic” phase to calculate the instant deformations and the “consolidation” to analyze the plastic deformations in the sloped area of the model. In parallel, the drone data achieved in 2022, were imported into the CloudCompare as the 3D point cloud processing software and different methods such as the “segment”, “statistical outlier Filter”, “CSF filter”, “noise filter”, “cross section” etc. were performed to clean the data and then imported into the Recap software to work on data and then imported into the Civil 3D software to create solid surface of the current data after the land sliding. In this regard, to evaluate the displacement occurred from the year 2007 toward the 2022, a TIN volume surface as the colored map created through the Civil 3D software to show the displacements in the z direction and all of the results were compared with the Plaxis 3D numerical solution software. The results showed that the colored map with the displacement in the positive and negative direction of the z is the same of the analyzed model and the values match each other’s and we created a digital model of the selected area to predict the land sliding hazard in the region in the following.

A Weighted Average of Multiple Inversions of Rayleigh Wave Dispersion Curve Using Particle Swarm Optimization for Geotechnical Site Characterization

<p class="Keywords">Shear wave velocity is an important parameter in geotechnical engineering for studying liquefaction, finding bedrock for the basement of a building, and figuring out the presence of subsurface cavities. This study aims to develop and evaluate the accuracy of the multiple inversions by the Particle Swarm Optimization (MI-PSO) algorithm with a weighted average solution. This algorithm is applied to Rayleigh wave dispersion data for geotechnical site characterization. Two synthetic models, the HVL model and the complex model (i.e., a combination of models with LVL and HVL characteristics), are used to conduct algorithm tests. These synthetic models replicate subsurface characteristics that are frequently encountered in geotechnical cases. Synthetic data tests show that the MI-PSO algorithm with a weighted average solution works excellently. The MI-PSO technique with a weighted average solution resolves the model better than the conventional average solution. When applied to two field data sets, the MI-PSO algorithm with a weighted average solution can delineate target models that are consistent with the qualitative interpretation based on the observed dispersion curve characteristics.</p>

Prediction of uniaxial compressive strength of rock based on lithology using stacking models

Uniaxial compressive strength (UCS) of rock is an essential parameter in geotechnical engineering. Point load strength (PLS), P-wave velocity, and Schmidt hammer rebound number (SH) are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS. This study collected 1080 datasets consisting of SH, P-wave velocity, PLS, and UCS. All datasets were integrated into three categories (sedimentary, igneous, and metamorphic rocks) according to lithology. Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types. Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types. UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks. Nonetheless, the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks. The developed predictive models can be applied to quickly predict UCS at engineering sites, which benefits the rapid and intelligent classification of rock masses. Moreover, the importance of SH, P-wave velocity, and PLS were analyzed for the estimation of UCS. SH was a reliable indicator for UCS evaluation across various rock types. P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks, but it was not reliable for assessing the UCS of metamorphic rocks.

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson’s ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects.

Back analysis of geomechanical parameters for rock mass under complex geological conditions using a novel algorithm

Geomechanical properties of rock mass are of vital importance for design optimization and stability evaluation in geotechnical engineering. Traditionally, they are estimated by laboratory experiments, in situ tests, and empirical classification, which ignore the scale effect, and may be not representative, leading to uncertainties of parameters. Back analysis method based on the monitoring displacement data provides the state-of-the-art tool for the estimation of geomechanical parameters. This paper proposed a novel back analysis method, which combined the optimized particle swarm optimization (OPSO) algorithm with the support vector machine (SVM) algorithm. And then, we cooperate the method with finite element method (FEM) forming the comprehensive method named OPSO-SVM-ABAQUS. This method has developed the standard PSO algorithm from two aspects for more powerful optimization capability and higher optimization precision. The OPSO algorithm is then used for the estimation of the SVM hyperparameters and the optimal values of geomechanical parameters. Furthermore, the performance of the proposed algorithm is compared with other six algorithms. Finally, the geomechanical parameters of the interlayered soft and hard rocks are calculated accurately, with the average error between the back-analyzed and assumed actual parameters to be 0.91 %. And the parametric investigations are implemented with the trained model. It dominates that the proposed algorithm is feasible for reasonable parameters in geotechnical engineering under complex geological conditions.

Investigation on the suitability of aluminium-based water treatment sludge as a sustainable soil replacement for road construction

Water treatment sludge is a by-product of the drinking water treatment process, and it has been considered as a waste for water authorities around the world. This study aims to investigate the reuse of aluminium-based water treatment sludge (AS) as a soil replacement in road construction which can improve sustainable waste management practices for positive environmental outcomes. To evaluate the potential reuse of AS, a 50/50 mixture ratio of AS and clay (CL) was selected and tested for geotechnical engineering parameters through Odometer Tests, Unconfined Compression Strength Tests, Static triaxial, Cyclic triaxial and California Bearing Ratio. Detailed physical and chemical were also conducted with the water treatment sludge. Results suggest that combining AS with CL increases the optimal moisture content and decreases the dry density of the soil. Oedometer tests indicated that the CLAS mixture distributed stress better than CL and AS independently. In the UCS tests, CLAS enhanced the strength of the mixture from 74 kPa with only CL to 126 kPa with the introduction of AS. The shear strength of CLAS samples increased by 50% compared to CL. In the cyclic triaxial test, CLAS showed less cumulative strain than CL, and improved the CBR results of the mixture from 0.95% to 1.92%. Results indicate that AS can be beneficially reused as a low-cost soil replacement to contribute the Circular Economy and net-zero emission targets. Future research should examine the variability of AS, optimal ratios of AS to CL, and the levelized cost of AS in different environmental conditions.

Improving the estimation of soil spatial variability by considering transformation uncertainty based on LDRFE analysis

Transformation uncertainty is an imperative consideration in the process of transforming measurement data to desired design parameters in geotechnical engineering. In the reliability-based design framework, an improved understanding of transformation uncertainty is expected to produce more appropriate design of geotechnical structures. This paper quantifies the site-specific and aleatory transformation uncertainty and investigates how to consider it in estimation of random field parameters through numerically simulated cone penetration tests in spatially variable clay by means of large deformation random finite element analysis. The transformation uncertainty of a transformation model is modelled as a random field. Random field parameters of transformation uncertainty, including the standard deviation, scale of fluctuation and sample path smoothness, are estimated using maximum likelihood estimation along with Whittle-Matérn autocorrelation function model. Random field parameters of predicted shear strength with and without considering transformation uncertainty are thereafter compared with those random field parameters of actual shear strength. Results show that the consideration of transformation uncertainty in the transformation model can considerably improve the prediction accuracy of random field parameters. It is important to consider the spatial correlation structure of transformation uncertainty. This study offers a simple-to-use framework to estimate random field parameters considering the site-specific and aleatory transformation uncertainty.

Numerical Study of Stress Distribution in Soft Clay Treated with Stone Column

This study used a finite element analysis approach employing Plaxis 3D to analyze the stress concentration ratio, a critical parameter in geotechnical engineering, to examine stresses operating on stone columns and soft soils. This study also looked at the effect of the stiffness ratio between the stone column and the neighboring soil. With the same length and three different diameters, 0.8 m, 1.0 m, and 1.2 m, or three area replacement ratios ranging from 7% to 16%, respectively, floating and end-bearing stone columns were used. The influence of soft soil undrained cohesion, cu ranging from 6 kPa to 40 kPa, was also considered in the current study. The stiffness ratio for columns to adjacent soil, end bearing or floating stone column, and area ratio all have a significant impression on the performance of the stone column in treating soft soil and stress transmission mechanisms in the enhanced soil body, according to parametric studies. The average stress concentration ratio in soil improved with an end-bearing stone column of φ= 35° and raised to 2.63 and 4.71 at φ = 50°, ranging from 1.41 to 2.35 for area replacement ratios of 7% and 16%.

Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

Abstract This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furthermore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.

Parameter Identification of 3D Elastic-Plastic Model for Tunnel Engineering Based on Improved Genetic Algorithm

In order to overcome the deficiencies of the existing intelligent displacement back analysis methods, the authors propose a parameter identification method for the 3D elastic-plastic model of tunnel engineering based on an improved genetic algorithm. An improved SVR algorithm is introduced, which solves this problem by transforming the multidimensional output variable regression into a multilayer standard one-dimensional output variable regression; combined with the decimal-coded genetic algorithm, an improved GA-SVR algorithm is formed, and genetic algorithm is used to search for optimal SVR model parameters, in order to establish the optimal nonlinear mapping relationship between the parameters to be identified and the displacement. The genetic algorithm is used to carry out the optimal identification of the parameters to be identified. In order to compare the effect of this improved GA-SVR algorithm, the genetic algorithm is combined with the BP neural network to form the GA-BP algorithm and compile the corresponding calculation program. The two algorithms are applied to the intelligent identification of the parameters of the 3D elastic-plastic model of the same tunnel engineering. The experimental results show that the maximum relative error of the 6 parameters to be identified is 18.51%, only 2 are over 15% and 14 are within 10%, and the average relative error of the 6 parameters is only 10.32%, the minimum is 2.63%; for the GA-BP algorithm, the maximum relative error of identification is 22.73%, 4 are more than 15% and 9 are within 10%, and the average relative error of 6 parameters’ identification is up to 13.91%, and the minimum is 4.64%. The improved GA-SVR algorithm can achieve higher identification accuracy and better calculation efficiency than the GA-BP algorithm and can be used in the identification of similar geotechnical engineering parameters.

Multivariate distribution models of soil electrical resistivity

Soil electrical resistivity is an important parameter in geotechnical engineering. In this paper, a multivariate distribution model is used to predict soil electrical resistivity. The Box-Cox method was used to transform the soil parameters, which were then analysed by a correlation matrix. Multivariate distribution models with different input parameters were established, verified and analysed, and compared with conventional empirical models. The results show that the predictive accuracy of the multivariate distribution models was improved significantly by increasing the number of parameters. The correlation coefficient (R2) of the calibration data increased from 0.05 to 0.88, and that of the validation data increased from 0.06 to 0.83. The model with the best predictive accuracy was the RE−{G, F, Sr} model (R2 = 0.87, E(ε) = 1.22, COV(ε) = 0.98). The predictive accuracy of the multivariate distribution model was obviously higher than that of the conventional empirical model. The multivariate distribution model is an effective and simple way to predict soil electrical resistivity. The proposed model exhibits good performance using limited electrical resistivity data from frozen soils.

CPTU Interpretation for Silty Sand in Binh Dai Offshore Wind Farm

Geotechnical engineering parameters are of significant effect on foundation design. In-situ tests such as piezocone penetration test (CPTU) are widely used for estimate of effective internal friction angle of sand. How to choose appropriate interpretation method for CPTU is a big problem for geotechnical design. As a case study, the laboratory test results and CPTU results of two Phases (Phase 2 and Phase 3) in Binh Dai offshore wind farm of Vietnam were analysed to establish the correlation between effective internal friction angle φ' and corrected tip resistance qt. The effective internal friction angles of the four sand layers in Binh Dai are found to be linear dependent on the soil depth. Meanwhile, linear expressions between effective internal friction angle φ' and corrected tip resistance qt are proposed, which have high quality in prediction of effective internal friction angle φ' for silty sand in Binh Dai. It worth noting that direct use of CPTU method established by other site’s data without any site specific experience would lead to misestimate at offshore wind farm in Vietnam. New correlations should be established by using site specific data cautiously.

Finite Element Modelling of Prestressed Concrete Piles in Soft Soils, Case Study: Northern Jakarta, Indonesia

Jakarta is faced with limited land resources due to its position as the capital city of Indonesia. Therefore, numerous high-rise buildings are being constructed to solve this problem and provide accommodations for a large number of Jakarta residents. Studies have shown that prestressed concrete piles (spun piles) are commonly used as the foundations of high-rise buildings in metropolitan cities across Indonesia, especially in the Northern Jakarta Coastal area, which is predominant with deep soft soils deposit. To further assess and verify the ultimate capacity of the pile, a static loading test was conducted. However, not all results from the field test produced ideal, accurate, precise, and reliable load-settlement curve (until failure) results. Therefore, this study aims to determine the soil properties for the analysis of prestressed concrete spun piles with a diameter of 600 mm in the Northern Jakarta coastal area based on the standard penetration test values (SPT-N). It is a case study of a well-documented static pile load test using the kentledge system. Back analyses were performed by the finite element method to obtain the extrapolated load-settlement curve. Furthermore, the effect of interface strength between pile and soil on the load-settlement curve was also investigated. The results showed that a reduction of interface strength leads to a smaller load–settlement curve. In addition, several geotechnical engineering parameters of soil, such as the undrained shear strength and effective young's modulus, were established using data from an in-situ soil site investigation and empirical correlations with SPT-N.

The design and implementation of the auxiliary calculation system for geotechnical engineering index and parameters

A program based on Visual Basic for the auxiliary calculation and statistical analysis of geotechnical engineering index and parameters is introduced in this paper. The application methods and skills of the program are illustrated with examples. This program contains 10 main calculation modules of geotechnical, a total of 23 different index calculation methods were integrated in it. Through this program, users only need to input the original data of geotechnical engineering tests of different projects and plots, and can quickly conduct the statistical analysis of geotechnical engineering parameters in a certain area, and can also compare the significance of differences among various factors. It is simple to operate, and the results are accurate and reliable, which can greatly improve the efficiency of parameter calculation and calibration in the practice of geotechnical engineering, and ensure the accuracy and precision of the calculation results.

Geotechnical Engineering Evaluation of Superficial Deposits Utilizing Seismic Methods at Al al-Bayt University, Jordan

Integration of seismic refraction tomography and multi-channel analyses shear wave methods were used at Al al-Bayt university campus in Mafraq, NE Jordan to delineate the shallow subsurface layering and determine their geotechnical properties, in addition, to estimate the geotechnical engineering parameters of the superficial deposits for detailed purposes. The P-wave refraction velocities were found to range from 180 to 2300 m/s for the three identified layers which indicating superficial soil deposits with an increasing degree of compaction with depth. The S-wave velocities were found to lie in a narrow velocity range (250 to 600 m/s), which badly resolved the subsurface layering. Moreover, four soil samples were taken from the sites of seismic survey points for density calculations. The average calculated density was found to be 1.85 g/cm3. Two soil samples from nearby seismic profile-1 and seismic profile-4 were tested for geotechnical properties. The results showed that the seismic method is an effective tool to estimate the different elastic moduli; Poisson's ratio, Young's modulus, Bulk modulus, Shear modulus, Lame's constants, and geotechnical engineering parameters; N-value, bearing capacity, material index, concentration index, and stress ratio of the superficial soil deposits. The study recommends conducting a detailed geophysical study before starting any type of geoengineering construction in the area.

The Undrained Shear Strength Evaluation of Vinh Chau Clay in Vietnam Coast

In the foundation design, the geotechnical engineering parameters are of great importance. As a case study, the laboratory test results of two locations (Banpu and Lac Hoa) in Vinh Chau coast of Vietnam were analysed to acquire the accurate strength for clayey soil. Due to the sample disturbance, the undrained shear strength s u from unconfined compression test are not so reliable, while su from direct quick shear tests (DSq) are more representative in Vinh Chau. A regression analysis between the DSq tests and soil depth had been carried out, good linear correlations are provided for all the three clayey layers in Banpu and Lac Hoa, the results are relatively similar for each clay layer, indicating a same sedimentary and stress history. Furthermore, the normalised undrained shear strengths s u/σ v0’ presented negative linear correlation of liquidity index, and the regression equations have been provided to rapid and cautious estimation of Vinh Chau clay in the future, using the liquidity index.

A microstructural investigation on hydraulic conductivity of soft clay

The hydraulic conductivity is a key parameter in geotechnical engineering. It is closely associated to soil microstructure. In practice, consolidation stress, dry density, and natural structure of soil can affect pore diameter and distribution of soil. Thus, to investigate soil hydraulic conductivity, their effects on microstructure and the corresponding hydraulic conductivity were analyzed. The undisturbed soil and the remolded soil with different dry densities were studied by the oedometer tests, hydraulic conductivity tests, and field emission scanning electron microscope (FESEM) tests. The results indicate that the natural structure inside the undisturbed soil is the intrinsic reason that affects hydraulic conductivity. The increase of consolidation stress mainly reduces the number and size of interaggregate pores in terms of provoking aggregation of soil particles. The increase in dry density will fill the interaggregate pores and weaken their connectivity. The area percentage of macropores can be used to reflect the change of soil microstructure. The consolidation stress is the main factor dominating the area percentage of macropores under a high-stress level. A stable intrinsic structure will be developed to inhibit the soil deformation when dry density is 1.4 g/cm3 or above, which helps to restrain the reduction of area percentage and slow down the decrease of hydraulic conductivity. Finally, a brief statistical regression analysis indicates that the area percentage of pores whose diameter larger than 0.4 μm is significantly related to the hydraulic conductivity, which are responsible for seepage in soft clay.

Experimental and theoretical investigation of transparent sand composing of fused quartz and calcium bromide solution

The thermal conductivity of soil is a fundamental physical property related to heat conduction and also one of the widely applied parameters in geotechnical engineering. Thermal needle tests were performed to measure the thermal conductivity of transparent sand. The thermal conductivity of three particle sizes of transparent sand was measured at different porosities and temperatures. The result shows that the thermal conductivity of transparent sand decreases with the increase of porosity and temperature. A modified model is proposed to calculate the thermal conductivity of the tested sand. Compared with the literature models, which are only suitable for room temperature, the modified model in this paper is applicable to a wider temperature range, ranging from 5∘C to 45∘C. It will also be beneficial to future studies on thermal reponses of granular materials, i.e., energy pile model test using transparent sand.

Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength

This paper focuses on the prediction of soil shear strength (SSS), which is one of the most fundamental parameters in geotechnical engineering. Consisting of 12 influential factors, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index as input variables, as well as the shear strength as the desired output, the dataset is provided through a field survey in Vietnam. Thereafter, as for used intelligent techniques, the main focus of the current study is on evaluating the efficiency of three novel optimization techniques for optimizing an artificial neural network (ANN) in predicting the SSS. To this end, the dragonfly algorithm (DA), whale optimization algorithm (WOA), and invasive weed optimization (IWO) are synthesized with ANN to prevail its computational drawbacks. The complexity of the models is optimized by sensitivity analysis. The results confirmed the effectiveness of all three applied algorithms, as the learning error was reduced by nearly 17%, 27%, and 32%, respectively by functioning the DA, WOA, and IWO. As for the testing phase, the IWO and DA achieved a close prediction accuracy. Overall, due to the superiority of the IWO-ANN ensemble, this model could be a promising alternative to traditional methods of shear strength determination.