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Piles in offshore constructions have often been reinforced with anchored piles, batter piles, and micro piles to strengthen their uplift load. The opening bladed anchor pile is a recent alternative approach that is presented in this study. This modified pile aims to provide a bearing area underneath the soil during uplift. This method is not incompatible with driving or penetrating piles into the ground. The pullout response of the modified anchor was examined in an experimental investigation. The impact of relative density of the sand, the embedment ratio, and the angle of the blade on the uplift capacity of the anchor pile with blades are studied. The ultimate uplift response of the bladed anchor implanted in sand improves with the increase in embedment ratio and blade angle. The percentage of amelioration in the uplift load at embedment ratio of 32 and blade angle of 90° was (231.1%, 208.6%, and 191.4%) at sand relative densities of (30%, 50%, and 80%), respectively, compared to unbladed anchor. The presence of blades produced a perfect anchoring system. For offshore applications, this piling modification is practical, appropriate for marine constructions, and simple to install.

In the process of designing pile foundations, it is essential to take the axial bearing capacity (Bc) of the pile into consideration., where determination of this target requires extreme fields and experimental efforts along with its cost. The primary objective of this study was to investigate the possibility of using tree-based approaches in order to estimate the axial Bc of piles. The goal of building Random Forests (RF) models is to produce a strong and adaptable machine learning method that is capable of reliably and accurately completing tasks related to classification as well as regression. Enhanced precision in predicting feature significance, scalability, adaptability, and managing missing data are the main objectives of employing RF. The accuracy of this model is very dependent on its hyperparameters, which are linked to the Coati optimizer (CO) and Giant trevally optimizer (GTO) procedures (also called RF-C and RF-G) in order to find the optimal combinations. In a database, there were 472 driven pile static load test results collected from previous papers. Specifically, the construction, validation, and testing phases of the proposed framework were carried out using the learning set (70%), validation set (15%), and evaluating set (15%) of the dataset. Moreover, the feature importance analysis is designed to assess the impact of each input variable on the axial Bc of piles. RF-C and RF-G offer promising Bc forecasting capabilities, where the RF-C approach outperformed the RF-G method in R2 values, with values of 0.9876, 0.9781, and 0.9873.

Detailed particle breakage adjacent to a pile has great influence on the settlement and bearing capacity of a pile foundation. Before the pile test, coral sand was divided into different grain-size groups and dyed in different colors, then mixed as the ground soil. After pile penetration, the sand around the pile was divided into many zones and sampled. Grains in different colors in each size range of each sample were discerned quantitatively. Results show that the settlement curve dropped fast and the skin friction of pile was small due to the obvious particle breakage. In each zone, the actual particle breakage in each size range was different from the change in relative mass percentage, and the lost of angular edges is the dominant type of particle breakage under the bottom pressure of pile. The index Bag, excluding the interference effect of size overlap between fragments and unbroken grains in each size range, was slightly larger than Bg for most zones around the pile. The breakage-zone was limited to 1.5 times of the pile diameter at the radial direction and 2.5 times at the depth direction, which is much deeper than that in silica sand. Particle breakage at some distance from pile bottom is larger than that at the very bottom of the pile due to the shearing effect in the sand. Detailed particle breakage around the pile is useful in studying the interaction between the pile and crushable granular soil.

This paper aims to investigate the effect of combined end-and-side grouting on the bearing properties of large-diameter rock-socketed bored piles in highly weathered rock layers. Eight full-scale pile load tests were conducted in the highly weathered rock layer to analyze the enhanced mechanism of the combined grouted bored piles. The test data from pile mechanical testing were compared with the recommended values in the current specification and geological survey report. The results demonstrate significant improvement in the side and end resistances of the combined grouted bored piles, resulting in a substantial increase in the bearing capacity and effective settlement control. It was observed that the construction of impact holes for bored piles can cause severe damage to highly weathered rock structures and weaken the mobilization of side and end resistances. Moreover, it was found that the calculation of the enhancement coefficient in the current specification underestimates the practical bearing capacity. The measured enhancement coefficients for the side and end resistance of piles in fully or highly weathered rock layers range from 2.49 to 3.05 and 2.24 to 2.43, respectively, which are more reasonable and feasible for the calculation. The research findings deepen the understanding of the bearing characteristics of large-diameter rock-socketed bored piles with combined grouting and provide valuable case references for the optimal design of large-diameter combined grouted piles for building foundations in Shenzhen, China.HighlightsPost-grouting had the potential to improve super high-building foundation reliability while reducing pile length and cost.The improvement effect and improvement mechanism of combined grouted bored piles embedded in highly rock strata were revealed.The influence of the size effect for large-diameter piles in highly weathered rock was revealed.The construction of impact holes for bored piles can cause severe damage to highly weathered rock structures and weaken the mobilization of side and end resistances.The enhancement coefficients for the side and end resistance of piles in fully or highly weathered rock layers were proposed.

To study the applicability of the new geopolymer grouting material for super-long and large-diameter post-grouting bored piles in silty fine sand geology, this paper compares the bearing capacity of two grouting materials, geopolymer and normal Portland cement, and different grouting volume pile side-distributed grouting piles in silty fine sand based on field model tests are analyzed through the diffusion forms of the two materials in silty fine sand through the morphology of the grouted body after excavation. The results show that the ultimate bearing capacities of P0 (ungrouted pile), P1 (8 kg cement grouted pile), P2 (6 kg geopolymer-grouted pile), P3 (8 kg geopolymer-grouted pile) and P4 (10 kg geopolymer-grouted pile) are 5400 N, 8820 N, 9450 N, 11,700 N and 12,600 N, respectively, and that the ultimate bearing capacity of the grouted pile is improved compared with that of the ungrouted pile since, under the same grouting amount, the maximum bearing capacity of the pile using geopolymer grouting is increased by 133% compared with that of the pile with cement grouting. This further verifies the applicability of the geopolymer grouting material for the post-grouting of the pile foundation in silty fine sand. Under the action of the ultimate load, the pile side friction resistance of P1, P2, P3 and P4 is increased by 200%, 218%, 284% and 319% compared with that of P0. In addition, the excavation results show that the geopolymer post-grouting pile forms the ellipsoidal consolidation body at the pile side grouting location, which mainly comprises extrusion diffusion with a small amount of infiltration diffusion, and the cement grouting pile forms a sheet-like consolidation body at the lower grouting location, which primarily comprises split diffusion. This study can provide a reference basis for the theoretical and engineering application of post-grouting piles using geopolymers.

Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this purpose, a Genetic Algorithm (GA) was developed to select the most significant features in the raw dataset. After that, a GA-DLNN hybrid model was developed to select optimal parameters for the DLNN model, including: network algorithm, activation function for hidden neurons, number of hidden layers, and the number of neurons in each hidden layer. A database containing 472 driven pile static load test reports was used. The dataset was divided into three parts, namely the training set (60%), validation (20%) and testing set (20%) for the construction, validation and testing phases of the proposed model, respectively. Various quality assessment criteria, namely the coefficient of determination (R 2 ), Index of Agreement (IA), mean absolute error (MAE) and root mean squared error (RMSE), were used to evaluate the performance of the machine learning (ML) algorithms. The GA-DLNN hybrid model was shown to exhibit the ability to find the most optimal set of parameters for the prediction process.The results showed that the performance of the hybrid model using only the most critical features gave the highest accuracy, compared with those obtained by the hybrid model using all input variables.

The construction industry is a major contributor to the global climate crisis, prompting increasing interest in minimising the embodied carbon of structures, whether through material production regulations or the optimisation of structural elements. While a wide body of literature addresses the reduction of embodied carbon in superstructures, limited attention has been devoted to the optimisation of foundations, particularly piles. This research introduces a hybrid genetic algorithm optimisation tool designed to minimise the embodied carbon of tension piles in different soil conditions. Six different pile types are analysed: solid and hollow concrete piles, steel pipes, universal column (UC) sections, and timber piles in both square and circular forms. The optimal design parameters for each pile type on undrained clay and loose sand are presented and compared. The results demonstrate the potential for reducing the embodied carbon of tension piles when utilising optimised designs. Finally, a case study involving an 8-metre-high cross-road signpost is presented, illustrating the practical application of the proposed optimisation algorithm for reducing embodied carbon in future designs.

Pile foundation is a commonly recognized form of foundation, and earthquakes are a common seismic damage phenomenon. Accidents resulting from reduction in pile bearing capacity due to earthquakes pose a great threat to people’s lives and safety. This article investigates the interaction between soil and piles under earthquake action. Utilizing the MIDAS GTS NX finite element software, the vertical bearing characteristics of piles under earthquake action are studied. Obtained acceleration of piles, pile settlement, pile axial force, pile top horizontal displacement, soil pore water pressure, and pore pressure ratio under different earthquake magnitudes. The research results indicate that as the depth increases, the acceleration at the pile top is significantly greater than that at the pile bottom, with an average increase of 20% in acceleration at three different earthquake magnitudes; Both the beginning of the pore pressure ratio growth and the ultimate reaching of its stable pore pressure ratio coincide with a rise in earthquake magnitude. Additionally, the axial force of the pile body also increases with the magnitude of the earthquake, and the maximum axial force of the pile body can increase by 40% at the same time. Simultaneously, the magnitude of the earthquake influences both the displacement of the pile body and the settling of the pile top. This article can provide reference for pile foundation design and engineering construction in liquefaction sites.

Due to the advantages of high bearing capacity, small settlement of pile body, and high material utilization rate, rotary drilling thread special-shaped pile (RDTSSP) has been applied in pile foundation engineering at home and abroad. Through the field static load test, the bearing characteristics of the single pile of the rotary drilling screw pile are tested and analyzed. Based on the field-measured data, the stress characteristics of the rotary drilling screw pile are analyzed by FLAC3D6.0 finite difference software, and the pile characteristics affecting the vertical bearing capacity of the rotary drilling screw-shaped pile are studied. The impact of various pile factors, including length, diameter, and the ratio of pile body to screw modulus, as well as the presence of an enlarged bottom, the elastic modulus of the pile, and the ratio of the pile body to soil elastic modulus, on the load-bearing capacity of rotary drilling thread special-shaped pile (RDTSSP) is examined. The results show that with the increase in pile length, the bearing capacity of the screw-shaped pile increases gradually, but when it increases to a certain value, the increased bearing capacity per unit volume decreases gradually. The increase in pile diameter will lead to a decrease in bearing capacity per unit volume, so the smaller pile diameter should be selected in the design to make full use of the material properties. The bottom expansion has little effect on the bearing capacity, but with the increase in the inner diameter of the bottom expansion, the bearing capacity increases gradually, while the bearing capacity per unit volume decreases and the material utilization rate decreases. Enhancing the modulus of a pile modestly boosts its load-bearing capacity, whereas augmenting the elastic modulus ratio between the pile and the surrounding soil substantially amplifies this capacity. The innovation of this study is to propose a new type of rotary drilling thread-shaped pile, which has significant economic and social benefits in engineering applications.

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile foundations. This research compares the Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) algorithms utilizing a data set of 257 dynamic pile load tests for the first time. Also, this research illustrates the multicollinearity effect on DNN, CNN, RNN, LSTM, and BiLSTM models' performance and accuracy for the first time. A comprehensive comparative analysis is conducted, employing various statistical performance parameters, rank analysis, and error matrix to evaluate the performance of these models. The performance is further validated using external validation, and visual interpretation is provided using the regression error characteristics (REC) curve and Taylor diagram. Results from the comparative analysis reveal that the DNN (Coefficient of determination ( R 2) training (TR) = 0.97, root mean squared error ( RMSE) TR = 0.0413; R 2 testing (TS) = 0.9, RMSE TS = 0.08) followed by BiLSTM ( R 2 TR = 0.91, RMSE TR = 0.782; R 2 TS = 0.89, RMSE TS = 0.0862) model demonstrates the highest performance accuracy. It is noted that the BiLSTM model is better than LSTM because the BiLSTM model, which increases the amount of information for the network, is a sequence processing model made up of two LSTMs, one of which takes the input in a forward manner, and the other in a backward direction. The prediction of pile-bearing capacity is strongly influenced by ram weight (having a considerable multicollinearity level), and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network approach. In this study, the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.