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Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches.

By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson’s ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness.

Commonly encountered problems, such as insufficient bearing capacity of the foundation and significant soil deformation, typically necessitate improvements to sandy soil. The excessive use of traditional soil improvement materials, such as cement and lime, causes irreversible damage to the ecological environment. As a sustainable soil reinforcement material, xanthan gum has broad application prospects with respect to its effects on the bearing capacity and deformation of sandy soil foundations. In this study, scanning electron microscope tests and cone penetration model tests based on particle image velocimetry technology were conducted to investigate the microstructure, mechanical behavior, and deformation characteristics around cones in sand treated with different xanthan gum rates. The test results show that the xanthan gum exerts cementation and filling effects between sand particles, enhanced the bearing capacity of sand. The displacement field around the cones in xanthan gum–treated sand during the penetration exhibits good symmetry. With increasing xanthan gum rate, the maximum displacement value and vertical influence range around the cone of xanthan gum-treated sand decrease, while the horizontal influence range increases. On the basis of the cone penetration test result, a predictive model for the vertical bearing capacity incorporating the xanthan gum rate is proposed using the Laboratoire Central des Ponts et Chaussées (LCPC) model. The research results can provide a scientific basis for using xanthan gum when designing and constructing sandy soil foundations.

This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The WOA, which is configured to search for an optimal set of XGBoost parameters, helps increase the model’s accuracy and robustness. The hybrid method is constructed by a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the hybrid model consistently outperforms the default XGBoost model and deep neural network (DNN) regression. In an experiment of 20 runs, the proposed model has gained roughly 12, 11.7, 9, and 12% reductions in root mean square error compared to the DNN with 2, 3, 4, and 5 hidden layers, respectively. The Wilcoxon signed-rank tests confirm that the proposed model is highly suitable for concrete pile capacity prediction.

Extensive degradation of near-surface permafrost is projected during the twenty-first century1, which will have detrimental effects on northern communities, ecosystems and engineering systems. This degradation is predicted to have consequences for many processes, which previous modelling studies have suggested would occur gradually. Here we project that soil moisture will decrease abruptly (within a few months) in response to permafrost degradation over large areas of the present-day permafrost region, based on analysis of transient climate change simulations performed using a state-of-the-art regional climate model. This regime shift is reflected in abrupt increases in summer near-surface temperature and convective precipitation, and decreases in relative humidity and surface runoff. Of particular relevance to northern systems are changes to the bearing capacity of the soil due to increased drainage, increases in the potential for intense rainfall events and increases in lightning frequency. Combined with increases in forest fuel combustibility, these are projected to abruptly and substantially increase the severity of wildfires, which constitute one of the greatest risks to northern ecosystems, communities and infrastructures. The fact that these changes are projected to occur abruptly further increases the challenges associated with climate change adaptation and potential retrofitting measures.

This paper presents the results of field tests performed to investigate the compressive bearing capacity of pre-bored grouted planted (PGP) pile with enlarged grout base focusing on its base bearing capacity. The bi-directional O-cell load test was conducted to evaluate the behavior of full scale PGP piles. The test results show that the pile head displacements needed to fully mobilize the shaft resistance were 5.9% and 6.4% D (D is pile diameter), respectively, of two test piles, owing to the large elastic shortening of pile shaft. Furthermore, the results demonstrated that the PHC nodular pile base and grout body at the enlarged base could act as a unit in the loading process, and the enlarged grout base could effectively promote the base bearing capacity of PGP pile through increasing the base area. The normalized base resistances (unit base resistance/average cone base resistance) of two test piles were 0.17 and 0.19, respectively, when the base displacement reached 5% Db (Db is pile base diameter). The permeation of grout into the silty sand layer under pile base increased the elastic modulus of silty sand, which could help to decrease pile head displacement under working load.

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

This paper presents a machine learning (ML)-based method for predicting the end-bearing capacity of rock-socketed shafts. For ML model training and testing, a database of 151 test shafts covering a wide range of rock types, shaft dimensions, and ground profiles has been developed from various sources. To properly take into account different factors, the rock property constant mi, unconfined compressive strength of intact rock σc (MPa), geological strength index GSI, length of the shaft within the soil layer Hs (m), length of the shaft within the rock layer Hr (m), and shaft diameter B (m) were taken as the inputs and the ultimate bearing capacity factor Nσ, which is the ratio of ultimate end-bearing capacity to σc, was taken as the target output. Four commonly used ML algorithms, support vector machine (SVM), decision trees (DT), random forest (RF), and Gaussian process regression (GPR), were first utilized to train models, respectively. Then, the trained models with the four ML algorithms were fused together with an ensemble learning (EL) approach to further enhance the prediction accuracy. Comparisons with existing empirical equations show a much better performance of the ML-based method for predicting the end-bearing capacity of rock-socketed shafts. Parametric studies were also performed with the EL model to investigate the importance of the six input parameters and the results show that the most important parameter is σc, followed by B, GSI, Hr, Hs and mi in the order of importance. For the convenient application of the ML-based method, a graphical user interface (GUI) app has been developed. Finally, two examples were analyzed to demonstrate the application of the GUI app with the implemented EL models. The results show that the GUI app can be used for quick and accurate prediction of the end-bearing capacity of rock-socketed shafts by considering the various parameters.HighlightsFour machine learning algorithms are fused together with an ensemble learning approach to predict the ultimate bearing capacity of rock socketed shafts.The proposed ensemble learning model outperforms other existing empirical methods.For the convenient application of the ensemble learning-based method, a graphical user interface app has been developed.

The influence of thermal loads on the ultimate bearing capacity of energy piles is examined. Five laboratory model tests were carried out to investigate piles equipped with U-shaped and W-shaped heat exchangers in dry and saturated sand. The pile load–displacement relationships were investigated for one, three, and five heating–cooling cycles and under three different pile temperatures. The results show that the ultimate bearing capacity, in dry sand at high soil relative density, increased as pile temperature increased. After one heating–cooling cycle, the ultimate bearing capacity reduced slightly. Compared with dry sand, the thermo-mechanical response in saturated sand was less obvious and the reduction of pile ultimate capacity after one heating–cooling cycle was smaller. A reduction in the ultimate bearing capacity of 13.4% was observed after three heating–cooling cycles in dry sand, while a reduction in ultimate bearing capacity of 9.2% was observed after five heating–cooling cycles in saturated sand. The more noticeable reduction of ultimate bearing capacity in dry sand was related to the larger temperature variation which would induce more degradation at the pile–soil interface. In addition, the pore water viscosity in saturated sand may contribute to less degradation at pile–soil interface during heating and cooling.

Stone-column’s are recently garnered popularity being an effective ground-enhancement technique. This study investigates stone-column’s reinforcement impacts of black cotton soil (BCS) upon consolidation & strength characteristics. The laboratory experiments were performed upon BCS specimens reinforced with three various diameters (50 mm, 75 mm, & 100 mm) of stone-column’s and four slenderness ratios (l/d = 3, 4, 5, and 6). Consolidation characteristics and load-settlement responses of reinforced & unreinforced samples were compared. Results demonstrate how bearing capacity of reinforced soil rises along both column diameter & l/d ratio under end-bearing conditions. Furthermore, key geotechnical parameters which are compressibility coefficient (a v ), void ratio (e), coefficient of consolidation (C v ), volume change index (m v ), & permeability (k) are significantly affected by stone-column geometry. The findings confirm the efficacy of end-bearing stone columns in improving loading-carrying capacity and expediting consolidation in BCS, underscoring their suitability for ground stabilization in expansive soils.